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Resonance

resonance

Charles Dray is founder and CEO of Resonance Security, a full-spectrum cybersecurity suite of software and services that aims to integrate essential traditional and emerging offensive and defensive security practices into a single platform in order to protect companies and individuals against any and all threats that emerge, either from the existing internet or its future iterations. In this interview with Jillian Godsil, he shares his insights and experience running Resonance Security. What is your company in 2–5 words? Full spectrum effortless cybersecurity. Why is now the time for your company to exist? Cybersecurity threats continue to evolve and target larger audiences. Victims lose an estimated 714 Billion to cybercrime annually and it will increase with emerging technology threats. What do you love about your team, and why are you the ones to solve this problem? We focus on delivering an impactful experience we want organizations and individuals to be proud of using. Every single person on the team is highly passionate about our purpose, and we have the experience to back it. Our security team has a proven track record with defensive and offensive cybersecurity across institutions, small businesses, web3, and consumers. We’ve seen what’s out there and know we found the solution. If you weren’t building your startup, what would you be doing? I’d still be involved in cybersecurity and emerging tech, and I’d be thinking about another way to effectively make people safer from attacks which could ruin their lives and mental health. At the moment, how do you measure success? What are your metrics? I measure success through analyzing customer feedback, and reviewing how many customers end up being sales people. There’s a saying that says “your customer is your best sales person” and I believe it is spot on. In a few sentences, what do you offer to whom? Effortless, effective, and evolving cybersecurity software solutions for institutions, small businesses, web3, VIPs, and consumers. A solution for any circumstance, budget, and any technical level. What’s most exciting about your traction to date? We’ve enrolled customers in every vertical and received positive feedback from 100% of our customers since March of 2023! It’s an incredibly rewarding experience to see that the business model is working better then we could have ever imagined – I feel that giving each customer a human experience is a big part of it. Knowing they have a team of security experts on their side setting them up for success drives betterment on so many levels. Where do you think your growth will be next year? I see significant growth in the consumer, small business, and web3 segment in the next year. These segments are well served in the identity theft (for consumers) and testing market (for small businesses and web3), but are very exposed from an advanced full spectrum offensive and defensive security standpoint. It’s hard to build a team and software that can deliver those solutions truly effectively, so no one has really approached that market. For years, we have developed a playbook that effectively covers any audience. Our team of red team engineers and software builders are exclusive to Resonance and are driven by the passion to protect. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was back in March of 2023. It was a COSMWASM Rust code audit for a money transfer project. Our experience with emerging tech originates from our background in traditional cybersecurity, so projects are delighted to work with engineers that know how to tie together legacy technologies with emerging technologies and detect attack vectors of both technologies together. We reached nearly seven figures in Revenue our first year when we were in software beta mode. With the inflow of organizations and individuals starting to take security seriously we are on track to be at eight figures in the next year, and that’s mostly software revenue. What’s your biggest threat? That organizations and individuals don’t take cybersecurity seriously enough, and come to us later in a much less favorable situation after a hack rather than starting to take the proper actions today. It’s always sad to see how unwell people become after a major cybersecurity attack. Cybersecurity attacks make people loose trust in other people, and that’s never a good thing for mental health. Charles Dray is founder and CEO of Resonance Security, a full-spectrum cybersecurity suite of software and services that aims to integrate essential traditional and emerging offensive and defensive security practices into a single platform in order to protect companies and individuals against any and all threats that emerge, either from the existing internet or its future iterations. In this interview with Jillian Godsil, he shares his insights and experience running Resonance Security. What is your company in 2–5 words? What is your company in 2–5 words? Full spectrum effortless cybersecurity. Why is now the time for your company to exist? Why is now the time for your company to exist? Cybersecurity threats continue to evolve and target larger audiences. Victims lose an estimated 714 Billion to cybercrime annually and it will increase with emerging technology threats. What do you love about your team, and why are you the ones to solve this problem? What do you love about your team, and why are you the ones to solve this problem? We focus on delivering an impactful experience we want organizations and individuals to be proud of using. Every single person on the team is highly passionate about our purpose, and we have the experience to back it. Our security team has a proven track record with defensive and offensive cybersecurity across institutions, small businesses, web3, and consumers. We’ve seen what’s out there and know we found the solution. If you weren’t building your startup, what would you be doing? If you weren’t building your startup, what would you be doing? I’d still be involved in cybersecurity and emerging tech, and I’d be thinking about another way to effectively make people safer from attacks which could ruin their lives and mental health. At the moment, how do you measure success? What are your metrics? At the moment, how do you measure success? What are your metrics? I measure success through analyzing customer feedback, and reviewing how many customers end up being sales people. There’s a saying that says “your customer is your best sales person” and I believe it is spot on. In a few sentences, what do you offer to whom? In a few sentences, what do you offer to whom? Effortless, effective, and evolving cybersecurity software solutions for institutions, small businesses, web3, VIPs, and consumers. A solution for any circumstance, budget, and any technical level. What’s most exciting about your traction to date? What’s most exciting about your traction to date? We’ve enrolled customers in every vertical and received positive feedback from 100% of our customers since March of 2023! It’s an incredibly rewarding experience to see that the business model is working better then we could have ever imagined – I feel that giving each customer a human experience is a big part of it. Knowing they have a team of security experts on their side setting them up for success drives betterment on so many levels. Where do you think your growth will be next year? Where do you think your growth will be next year? I see significant growth in the consumer, small business, and web3 segment in the next year. These segments are well served in the identity theft (for consumers) and testing market (for small businesses and web3), but are very exposed from an advanced full spectrum offensive and defensive security standpoint. It’s hard to build a team and software that can deliver those solutions truly effectively, so no one has really approached that market. For years, we have developed a playbook that effectively covers any audience. Our team of red team engineers and software builders are exclusive to Resonance and are driven by the passion to protect. Tell us about your first paying customer and revenue expectations over the next year. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was back in March of 2023. It was a COSMWASM Rust code audit for a money transfer project. Our experience with emerging tech originates from our background in traditional cybersecurity, so projects are delighted to work with engineers that know how to tie together legacy technologies with emerging technologies and detect attack vectors of both technologies together. We reached nearly seven figures in Revenue our first year when we were in software beta mode. With the inflow of organizations and individuals starting to take security seriously we are on track to be at eight figures in the next year, and that’s mostly software revenue. What’s your biggest threat? What’s your biggest threat? That organizations and individuals don’t take cybersecurity seriously enough, and come to us later in a much less favorable situation after a hack rather than starting to take the proper actions today. It’s always sad to see how unwell people become after a major cybersecurity attack. Cybersecurity attacks make people loose trust in other people, and that’s never a good thing for mental health.

10 Questions for Cybersecurity Firm Resonance Founder Charles Dray

I spent the last year working on the new PHP framework to push this programming language further into the modern era. I firmly believe that the future of PHP lies in , Coroutines, and libraries like . Fibers Swoole The Bright Future of PHP Most people know PHP just as a website framework, but it's much more - it can serve as a communication hub in your infrastructure that interconnects all your services through REST API, GraphQL, WebSockets, gRPC, and more. It's effortless to prototype and write new features in PHP. PHP is also straightforward to extend; if necessary, replacing parts of your application with code written in a different language is simple. It's the perfect starting point when creating new applications. About Resonance Resonance is centered around this idea - to quickly start from the ground up in the asynchronous environment and be able to set up all the necessary services and APIs and then swap, replace, or expand all the application's components. Attributes First Everything is as modular as possible, with only minimal central configuration files - everything is configured through Attributes. For example: <?php

namespace App\HttpResponder;

use App\DatabaseEntity\BlogPostForDisplay;
use App\HttpRouteSymbol;
use Distantmagic\Resonance\Attribute\RespondsToHttp;
use Distantmagic\Resonance\Attribute\RouteParameter;
use Distantmagic\Resonance\Attribute\Singleton;
use Distantmagic\Resonance\HttpInterceptableInterface;
use Distantmagic\Resonance\HttpResponder;
use Distantmagic\Resonance\HttpResponder\HttpController;
use Distantmagic\Resonance\RequestMethod;
use Distantmagic\Resonance\SingletonCollection;
use Distantmagic\Resonance\TwigTemplate;

#[RespondsToHttp(
    method: RequestMethod::GET,
    pattern: '/blog/{blog_post_slug}',
    routeSymbol: HttpRouteSymbol::BlogPostShow,
)]
#[Singleton(collection: SingletonCollection::HttpResponder)]
final readonly class BlogPostShow extends HttpController
{
    public function handle(
        #[RouteParameter(from: 'blog_post_slug')]
        BlogPostForDisplay $blogPost,
    ): HttpInterceptableInterface {
        return new TwigTemplate('turbo/website/blog_post.twig', [
            'blog_post' => $blogPost,
        ]);
    }
} Asynchronous GraphQL in PHP You can build the entire GraphQL schema in PHP using attributes: <?php

use GraphQL\Type\Definition\ObjectType;
use GraphQL\Type\Definition\Type;
use Distantmagic\Resonance\Framework\Attribute\Singleton;
use Distantmagic\Resonance\Framework\SwooleFuture;

#[Singleton]
final class PingType extends ObjectType
{
    public function __construct()
    {
        parent::__construct([
            'name' => 'Ping',
            'description' => 'Test field',
            'fields' => [
                'message' => [
                    'type' => Type::string(),
                    'description' => 'Always responds with pong',
                    'resolve' => $this->resolveMessage(...),
                ],
            ],
        ]);
    }

    private function resolveMessage(): SwooleFuture
    {
        return new SwooleFuture(function () {
            sleep(1);

            return 'pong';
        });
    }
} This query is going to take 1 second (instead of 3), since the field is resolved asynchronously: query MultiPing() {
    ping1: ping { message }
    ping2: ping { message }
    ping3: ping { message }
} Promise-like Objects in PHP Swoole's coroutines are wrapped inside a SwooleFuture object, which provides easy access to asynchronous features: <?php

use Distantmagic\Resonance\SwooleFuture;

$future1 = new SwooleFuture(function (int $value) {
    assert($value === 1);

    return $value + 2;
});

$future2 = $future1->then(new SwooleFuture(function (int $value) {
    assert($value === 3);

    return $value + 4;
}));

assert($future2->resolve(1)->result === 7); Much more The project is free and open source. You can check the documentation here: https://resonance.distantmagic.com/docs/features/ GitHub repo: https://github.com/distantmagic/resonance Feel free to join the community: https://resonance.distantmagic.com/community/ Resonance is new, and it's my first open-source project of this magnitude. Currently, I am working on it full-time. If you try it out and Resonance will help you with your projects, I'd be the happiest man alive. If you find the project interesting please leave me a star on GitHub! Also published . here

Introducing Resonance, A New PHP Framework Built on Top of Swoole

“Why do these things keep happening?” I asked myself, staring blankly at the news headline. It was a hot Sunny afternoon, and Cointelegraph had just reported the of , which resulted in the loss of over $31 million. hacking Alphapo’s hot wallets Two days earlier, Curve’s liquidity pool platform, Conic Finance, was reportedly hacked for $3.2 million in eth. The recurring attacks on crypto exchanges and service providers are a major challenge for the crypto industry. However, cyber security experts understand why they occur and how they can be addressed. CEO , gave valuable insights into blockchain cybersecurity in this interview with Olayimika Oyebanji. Resonance Security Charles Dray It’s great to sit down with you, Charles. Can you tell us about yourself? First of all, thank you for taking the time to speak with me. I am a born and bred New Yorker who spent more than a decade working in traditional banking covering FX, eFX, and transactional banking for both individuals and institutional clients. After noticing continuous inefficiencies in transactional processing and KYC, I immersed myself in better understanding the blockchain space to figure out a way to solve issues my clients frequently experienced. In that research, I realized that there are a number of projects that would help with the inefficiencies I encountered in traditional banking, but the downside is that many of these projects were vulnerable to hacks, making them less appealing to potential clients. I realized that focusing on cybersecurity would help these projects build trust with the public, and open the door for a wider variety of end users to take advantage of blockchain efficiencies. What's your route to blockchain technology? I have always been a bit of a tech geek wanting to stay up to date with the newest gadgets and software. I dabbled a bit in cryptocurrencies out of curiosity and even created an NFT gallery in Decentraland. The more time I spent on these hobbies, the more opportunities I started to see what blockchain could accomplish. Not only could blockchain solve some of the ongoing issues with transactional processing and KYC I saw daily in the corporate banking world, but there was a slew of other use cases for blockchain that spanned industries far beyond banking. When I tried to explain these benefits to many of my colleagues, their responses were that it wasn’t secure enough to be a viable alternative, which got me looking more deeply into the cybersecurity side of the blockchain world. It was clear to me that in order to unlock blockchain opportunities, there was a lot of work to be done to secure these projects and to influence public opinion, which led me to opportunities in the Web3 cybersecurity space. Charles, we are dealing with a 4 billion dollar cybersecurity  problem. How did we get to this point? There are so many contributing factors leading to these high dollar vulnerabilities. Some of it comes down to the fact that blockchain is still evolving. As the space has built various chains and projects, we’re obviously thinking about functionality and safety, and as we’ve stress tested those systems some of their vulnerabilities become more apparent. Any time the space builds a new chain or functionality, we open the door to new hacking opportunities, so the proliferation of blockchains widens the vectors for attack, and that requires more attention paid to testing and auditing before going live. We’ll continue to see this as we incorporate other emerging technologies as well. On the other hand, if you look at the Web 2 space, it’s not as though that space is “hack free” either, although more time has been spent on identifying potential risks and solutions to mitigate them. Many of the vulnerabilities we see on the Web2 side also carry over to Web3. We spend a lot of time on education because people are often the biggest opportunity for vulnerability. Obviously, we hear a lot about bad actors in blockchain, but there are plenty of people in the space who are inadvertently jeopardizing projects by doing simple things like clicking a link in an unverified email or leaving their laptops unattended. At the end of the day, we can’t be naive about all the ways our security can potentially fail, and we need to hold projects accountable for being more proactive about their security strategies. What inspired the founding of Resonance Security? As I spent time talking to a huge variety of project founders in the space, two things became clear to me. The first is that security is not one size fits all and different projects had different needs when it came to cybersecurity. The second was that we were on the precipice of a number of new technologies, such as AI, machine learning, and quantum computing, being leveraged in the space that would increase the sophistication of cybersecurity attacks. For the industry to be prepared to meet these threats, we needed to be immersed in these technologies and we needed to have the flexibility to provide the right security mix to each client. This realization led me to form a team of seven all-star engineers and executives to start Resonance Security, with a focus on delivering a true cybersecurity concierge experience that covers best-in-class engagements with a cybersecurity dashboard made up of dozens of curated products and services. Is there a market gap you are trying to fill? The landscape in the industry is constantly changing, and it’s hard, even for the most informed people in the space, to stay up to date on all the options available to secure a project. We realized what was missing in the cybersecurity space was a digital Shepard or someone who has insider knowledge and can ensure projects are picking the best services and products for their specific needs, and even better, provide it all in one place. Our key objective is to make it effortless for projects to implement the appropriate cybersecurity measures for their organization. We take a personalized, holistic look at a project’s needs, identify their cybersecurity priorities, and provide them with a security roadmap. We have partnered with dozens of cybersecurity products spanning AI, forensics, social engineering, fuzzing tools, hardware, bug bounty programs, and more to ensure a strong security posture. If we don’t offer the cybersecurity product you are looking for, we will act as a concierge and interview products to find the right match for you. We want to be a top-tier service provider but also the add-on to any audit no matter where you’re getting your audit or what stage you’re at. Why are the routine cybersecurity practices that you recommend for users’ safety? There are many best practices, and it depends on the scope, audience, chain, and various other factors, but some tried and true things to consider as an individual is to Do your own research first. Understand the history of projects before you invest, always use 2fa, be aware of social engineering attacks, and generally just seek to know when it comes to cybersecurity and emerging tech. For developers, ongoing awareness of the different types of security vulnerabilities in web3 apps and how they tie into web2 components including webapps cloud and ci/cd are very important. The space is constantly changing, so this is a constant activity for our engineers, and if you’re developing in the space, you should make it your job to stay up to date. Following the best secure coding practices for the specific language you are working with, you should have extensive knowledge of the blockchains they are deploying the system on. There are so many more!! I can go on forever. Keep in mind that while these are some best practices, we believe that cybersecurity needs to be customized based on a variety of factors. We always suggest conducting multiple audits with different security companies as well as implementing a suite of tools for ongoing security. Organizations must educate their audiences regarding cybersecurity threats from all fronts; this not only includes auditing the technical stack, but also training staff members, and users. Security awareness and education should be a continuous narrative in every ecosystem, and even that is just a small piece of security that should resonate. What is the biggest challenge in blockchain cybersecurity? How can it be addressed? One of the biggest challenges I think we see in cybersecurity currently is not taking the topic of end-to-end cybersecurity seriously enough. There are a lot of check-box security reviews happening rather than truly taking an end-to-end look at how projects can be secure on all fronts. Smart contract audits are great, and I’m going to be the first one to tell you that you need more than one, but there are other aspects to security like front-end, back-end, and employee attack vectors that can destroy an organization. That’s why Resonance not only offers audits, pen tests, and education but our cybersecurity product dashboard as an add-on to any audit that can ensure you have the bespoke security you need. The other looming challenge is the impact of emerging tech and how we’ll need to respond to meet the new threats that arise from those technologies. My feeling is the best way to stay ahead of threats is to get immersed in the technologies. Understanding how they can be used and leveraging them to build our own tools is the best way for us to be prepared to meet this threat. Can you share your personal experience as the CEO of Resonance Security? One of the things I love the most about my job is the ability to talk to a wide variety of cybersecurity projects and products spanning every use case you can possibly imagine. It’s incredible to see the innovation happening in the space and seeing how invested people are in creating novel solutions to problems that have long existed. I’m always excited to get on a call with a new client and learn about what they’re doing. I am also incredibly lucky to have an amazing team at Resonance. I’m impressed daily by our engineers; not only are they auditors, but they are researchers, teachers, and innovators. Their insights help us to develop high-quality in-house tools, integrate emerging technologies into our audits and product offerings, and stay on top of trends in the space. Does addressing cyber security challenges require heavy funding? So much of what a project’s cybersecurity needs are will be unique to an individual project, so there is no one size fits all answer to this question. That being said, there are a variety of ways to help secure your project. Audits can be found at a variety of price points, and although all audits are not equal, having someone look at your code who was not part of its creation will help projects identify issues that may have been overlooked. We’re also at a place now where there are so many high-quality tools and products in the market that can help identify threats outside of an audit. Some of the products we work with currently cost less than your monthly coffee habit, and others are free for the basic functionality. Despite the fact that there are some lower-cost options in the space, I think it’s important for projects to start to understand what their specific security needs will be early on in their building process and make sure to budget accordingly for the security needed. This is not the place to cheap out because if you run into a problem down the road, it will cost ten times more to fix, and the reputational damage may not be fixable. Any parting words? We are at the stone age of the emerging technology era, and we've just discovered fire. It's important that we all be cognizant that everything will need to be tested, fine-tuned, and eventually converged to create a stronger whole. The best way for the community to learn and adapt quickly is for us to foster collaboration throughout the industry so we stay ahead of future threats and optimize the innovation from these new technologies.

Blockchain Cybersecurity: Exclusive Interview With Resonance Security CEO Charles Dray

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2.2 Backbone, Diffusion Prior, & Submodules 2.2 Backbone, Diffusion Prior, & Submodules 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.5 Model Inference 2.5 Model Inference 3 Results and 3.1 fMRI-to-Image Reconstruction 3 Results and 3.1 fMRI-to-Image Reconstruction 3.2 Image Captioning 3.2 Image Captioning 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.5 Ablations 3.5 Ablations 4 Related Work 4 Related Work 5 Conclusion 5 Conclusion 6 Acknowledgements and References 6 Acknowledgements and References A Appendix A Appendix A.1 Author Contributions A.1 Author Contributions A.2 Additional Dataset Information A.2 Additional Dataset Information A.3 MindEye2 (not pretrained) vs. MindEye1 A.3 MindEye2 (not pretrained) vs. MindEye1 A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.5 Single-Subject Evaluations A.5 Single-Subject Evaluations A.6 UnCLIP Evaluation A.6 UnCLIP Evaluation A.7 OpenCLIP BigG to CLIP L Conversion A.7 OpenCLIP BigG to CLIP L Conversion A.8 COCO Retrieval A.8 COCO Retrieval A.9 Reconstruction Evaluations: Additional Information A.9 Reconstruction Evaluations: Additional Information A.10 Pretraining with Less Subjects A.10 Pretraining with Less Subjects A.11 UMAP Dimensionality Reduction A.11 UMAP Dimensionality Reduction A.12 ROI-Optimized Stimuli A.12 ROI-Optimized Stimuli A.13 Human Preference Experiments A.13 Human Preference Experiments Abstract Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are trained independently per subject where each subject requires dozens of hours of expensive fMRI training data to attain high-quality results. The present work showcases high-quality reconstructions using only 1 hour of fMRI training data. We pretrain our model across 7 subjects and then fine-tune on minimal data from a new subject. Our novel functional alignment procedure linearly maps all brain data to a shared-subject latent space, followed by a shared non-linear mapping to CLIP image space. We then map from CLIP space to pixel space by fine-tuning Stable Diffusion XL to accept CLIP latents as inputs instead of text. This approach improves out-of-subject generalization with limited training data and also attains state-of-the-art image retrieval and reconstruction metrics compared to single-subject approaches. MindEye2 demonstrates how accurate reconstructions of perception are possible from a single visit to the MRI facility. All code is available on GitHub. 1 Introduction Spurred by the open releases of deep learning models such as CLIP (Radford et al., 2021) and Stable Diffusion (Rombach et al., 2022), along with large-scale functional magnetic resonance imaging (fMRI) datasets such as the Natural Scenes Dataset (Allen et al., 2022) where human participants were scanned viewing tens of thousands of images, there has been an influx of research papers demonstrating the ability to reconstruct visual perception from brain activity with high fidelity (Takagi and Nishimoto, 2022; 2023; Ozcelik et al., 2022; Ozcelik and VanRullen, 2023; Gaziv et al., 2022; Gu et al., 2023; Scotti et al., 2023; Kneeland et al., 2023a;b;c; Ferrante et al., 2023a; Thual et al., 2023; Chen et al., 2023a;b; Sun et al., 2023; Mai and Zhang, 2023; Xia et al., 2023). FMRI indirectly measures neural activity by detecting changes in blood oxygenation. These patterns of fMRI brain activity are translated into embeddings of pretrained deep learning models and used to visualize internal mental representations (Beliy et al., 2019; Shen et al., 2019a;b; Seeliger et al., 2018; Lin et al., 2019). Visualization of internal mental representations, and more generally the ability to map patterns of brain activity to the latent space of rich pretrained deep learning models, has potential to enable novel clinical assessment approaches and brain-computer interface applications. However, despite all the recent research demonstrating high-fidelity reconstructions of perception, the practical adoption of such approaches to these settings has been limited if not entirely absent. A major reason for this is that the high-quality results shown in these papers use single-subject models that are not generalizable across people, and which have only been shown to work well if each subject contributes dozens of hours of expensive fMRI training data. MindEye2 introduces a novel functional alignment procedure that addresses these barriers by pretraining a shared-subject model that can be fine-tuned using limited data from a held-out subject and generalized to held-out data from that subject. This approach yields similar reconstruction quality to a single-subject model trained using 40× the training data. See Figure 1 for selected samples of reconstructions obtained from just 1 hour of data from subject 1 compared to their full 40 hours of training data in the Natural Scenes Dataset. In addition to a novel approach to shared-subject alignment, MindEye2 builds upon the previous SOTA approach introduced by MindEye1 (Scotti et al., 2023). In terms of similarities, both approaches map flattened spatial patterns of fMRI activity across voxels (3-dimensional cubes of cortical tissue) to the image embedding latent space of a pretrained CLIP (Radford et al., 2021) model with the help of a residual MLP backbone, diffusion prior, and retrieval submodule. The diffusion prior (Ramesh et al., 2022) is used for reconstruction and is trained from scratch to take in the outputs from the MLP backbone and produce aligned embeddings suitable as inputs to any pretrained image generation model that accepts CLIP image embeddings (hereafter referred to as unCLIP models). The retrieval submodule is contrastively trained and produces CLIP-fMRI embeddings that can be used to find the original (or nearest neighbor) image in a pool of images, but is not used to reconstruct a novel image. Both MindEye2 and MindEye1 also map brain activity to the latent space of Stable Diffusion’s (Rombach et al., 2022) variational autoencoder (VAE) to obtain blurry reconstructions that lack high-level semantic content but perform well on low-level image metrics (e.g., color, texture, spatial position), which get combined with the semantically rich outputs from the diffusion prior to return reconstructions that perform well across perceptual and semantic features. MindEye2 innovates upon MindEye1 in the following ways: (1) Rather than the whole pipeline being independently trained per subject, MindEye2 is pretrained on data from other subjects and then fine-tuned on the held-out target subject. (2) We map from fMRI activity to a richer CLIP space provided by OpenCLIP ViT-bigG/14 (Schuhmann et al., 2022; Ilharco et al., 2021), and reconstruct images via a fine-tuned Stable Diffusion XL unCLIP model that supports inputs from this latent space. (3) We merge the previously independent high- and low-level pipelines into a single pipeline through the use of submodules. (4) We additionally predict the text captions of images to be used as conditional guidance during a final image reconstruction refinement step. The above changes support the following main contributions of this work: (1) Using the full fMRI training data from Natural Scenes Dataset we achieve state-of-the-art performance across image retrieval and reconstruction metrics. (2) Our novel multi-subject alignment procedure enables competitive decoding performance even with only 2.5% of a subject’s full dataset (i.e., 1 hour of scanning). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC);
(2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution;
(3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution;
(4) Reese Kneeland, University of Minnesota and a Core contribution;
(5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC);
(6) Ashutosh Narang, Medical AI Research Center (MedARC);
(7) Charan Santhirasegaran, Medical AI Research Center (MedARC);
(8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC);
(9) Thomas Naselaris, University of Minnesota;
(10) Kenneth A. Norman, Princeton Neuroscience Institute;
(11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC). Authors: Authors: (1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC); (2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution; (3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution; (4) Reese Kneeland, University of Minnesota and a Core contribution; (5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC); (6) Ashutosh Narang, Medical AI Research Center (MedARC); (7) Charan Santhirasegaran, Medical AI Research Center (MedARC); (8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC); (9) Thomas Naselaris, University of Minnesota; (10) Kenneth A. Norman, Princeton Neuroscience Institute; (11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC).

Reconstructing Vision With Minimal fMRI Data: Cross-Subject Pretraining With MindEye2

Turns Out, Star Trek Was Basically a Tech Roadmap

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 1.1 A Polyethylene-based metamaterial for acoustic control 1.1 A Polyethylene-based metamaterial for acoustic control 2 Relaxed micromorphic modelling of finite-size metamaterials 2 Relaxed micromorphic modelling of finite-size metamaterials 2.1 Tetragonal Symmetry / Shape of elastic tensors (in Voigt notation) 2.1 Tetragonal Symmetry / Shape of elastic tensors (in Voigt notation) 3 Dispersion curves 3 Dispersion curves 4 New considerations on the relaxed micromorphic parameters 4 New considerations on the relaxed micromorphic parameters 4.1 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s bulk material properties 4.1 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s bulk material properties 4.2 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s size 4.2 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s size 4.3 Relaxed micromorphic cut-offs 4.3 Relaxed micromorphic cut-offs 5 Fitting of the relaxed micromorphic parameters: the particular case of vanishing curvature (without Curl P and Curl P˙) 5 Fitting of the relaxed micromorphic parameters: the particular case of vanishing curvature (without Curl P and Curl P˙) 5.1 Asymptotes 5.1 Asymptotes 5.2 Fitting 5.2 Fitting 5.3 Discussion 5.3 Discussion 6 Fitting of the relaxed micromorphic parameters with curvature (with Curl P) 6 Fitting of the relaxed micromorphic parameters with curvature (with Curl P) 6.1 Asymptotes and 6.2 Fitting 6.1 Asymptotes and 6.2 Fitting 6.3 Discussion 6.3 Discussion 7 Fitting of the relaxed micromorphic parameters with enhanced kinetic energy (with Curl P˙) and 7.1 Asymptotes 7 Fitting of the relaxed micromorphic parameters with enhanced kinetic energy (with Curl P˙) and 7.1 Asymptotes 7.2 Fitting 7.2 Fitting 7.3 Discussion 7.3 Discussion 8 Summary of the obtained results 8 Summary of the obtained results 9 Conclusion and perspectives, Acknowledgements, and References 9 Conclusion and perspectives, Acknowledgements, and References A Most general 4th order tensor belonging to the tetragonal symmetry class A Most general 4th order tensor belonging to the tetragonal symmetry class B Coefficients for the dispersion curves without Curl P B Coefficients for the dispersion curves without Curl P C Coefficients for the dispersion curves with P C Coefficients for the dispersion curves with P D Coefficients for the dispersion curves with P◦ D Coefficients for the dispersion curves with P◦ 9 Conclusion and perspectives In addition to the fitting comparison presented in the present paper, one main result that we present here is the fitting procedure itself that has been automatized to a big extent by asking only the cut-offs and asymptotes to be imposed a priori. This has been done by imposing the exact value of the cut-offs and minimizing the asymptotes’ mean square error compared to the exact numerical values issued via Boch-Floquet analysis. The rest of the curves’ fitting follows directly. Based on the findings of this paper, we will briefly present some insight that will give directions to the follow-up research: • Further enhance the relaxed micromorphic model via the addition of extra microscopic degrees of freedom to increase its precision at very small wavelengths (approaching the unit cell’s size); • Design complex large-scale meta-structures that control elastic energy using the new labyrinthine metamaterial as a basic building block. This design would not be otherwise possible due to the huge number of degrees of freedom resulting from the meshing of all the tiny elements contained in the labyrinthine unit cells; • Study negative refraction phenomena in meta-structures including the new labyrinthine metamaterial as a basic building block; • Design complex structures for wave control simultaneously including the different metamaterials that were characterized via the relaxed micromorphic model until now. Acknowledgements Angela Madeo, Gianluca Rizzi and Jendrik Voss acknowledge support from the European Commission through the funding of the ERC Consolidator Grant META-LEGO, N◦ 101001759. Angela Madeo and Gianluca Rizzi acknowledge funding from the French Research Agency ANR, “METASMART” (ANR-17CE08-0006). Patrizio Neff acknowledges support in the framework of the DFG-Priority Programme 2256 “Variational Methods for Predicting Complex Phenomena in Engineering Structures and Materials”, Neff 902/10-1, Project-No. 440935806. References [1] A. Aivaliotis, A. Daouadji, G. Barbagallo, D. Tallarico, P. Neff, and A. Madeo. “Microstructure-related Stoneley waves and their effect on the scattering properties of a 2D Cauchy/relaxed-micromorphic interface”. en. Wave Motion 90 (Aug. 2019). Pp. 99–120. issn: 01652125. doi: 10.1016/j.wavemoti.2019.04.003. url: https://linkinghub.elsevier.com/ retrieve/pii/S0165212518304827 (visited on 04/23/2021). [2] A. Aivaliotis, D. Tallarico, M.-V. d’Agostino, A. Daouadji, P. Neff, and A. Madeo. “Frequency- and angle-dependent scattering of a finite-sized meta-structure via the relaxed micromorphic model”. en. Archive of Applied Mechanics 90.5 (May 2020). 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Madeo. “The relaxed linear micromorphic continuum: well-posedness of the static problem and relations to the gauge theory of dislocations”. en. The Quarterly Journal of Mechanics and Applied Mathematics 68.1 (Feb. 2015). Pp. 53–84. issn: 0033-5614, 1464-3855. doi: 10 . 1093 / qjmam / hbu027. url: https : / / academic.oup.com/qjmam/article-lookup/doi/10.1093/qjmam/hbu027 (visited on 04/25/2021). [33] P. Neff, B. Eidel, M. V. d’Agostino, and A. Madeo. “Identification of scale-independent material parameters in the relaxed micromorphic model through model-adapted first order homogenization”. en. Journal of Elasticity 139.2 (May 2020). Pp. 269–298. issn: 0374-3535, 1573-2681. doi: 10.1007/s10659-019-09752-w. url: http://link.springer.com/10.1007/ s10659-019-09752-w (visited on 04/23/2021). [34] P. Neff, I.-D. Ghiba, A. Madeo, L. Placidi, and G. Rosi. “A unifying perspective: the relaxed linear micromorphic continuum”. en. Continuum Mechanics and Thermodynamics 26.5 (Sept. 2014). 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Journal of the Mechanics and Physics of Solids 97 (Dec. 2016). Pp. 10–18. issn: 00225096. doi: 10.1016/j.jmps.2015.11.004. url: https://linkinghub.elsevier.com/retrieve/pii/ S0022509615302623 (visited on 04/23/2021). [48] J. Willis. “Bounds and self-consistent estimates for the overall properties of anisotropic composites”. Journal of the Mechanics and Physics of Solids 25.3 (1977). Pp. 185–202. [49] J. Willis. “Exact effective relations for dynamics of a laminated body”. Mechanics of Materials 41.4 (2009). Pp. 385–393. [50] J. Willis. “Effective constitutive relations for waves in composites and metamaterials”. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 467.2131 (2011). Pp. 1865–1879. [51] J. Willis. “The construction of effective relations for waves in a composite”. Comptes Rendus M´ecanique 340.4-5 (2012). Pp. 181–192. [52] R. Zhu, X. N. Liu, G. K. Hu, C. T. Sun, and G. L. Huang. “Negative refraction of elastic waves at the deep-subwavelength scale in a single-phase metamaterial”. en. Nature Communications 5.1 (Dec. 2014). P. 5510. issn: 2041-1723. doi: 10. 1038/ncomms6510. url: http://www.nature.com/articles/ncomms6510 (visited on 04/23/2021). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Jendrik Voss, Institute for Structural Mechanics and Dynamics, Technical University Dortmund and a Corresponding Author (jendrik.voss@tu-dortmund.de);
(2) Gianluca Rizzi, Institute for Structural Mechanics and Dynamics, Technical University Dortmund;
(3) Patrizio Neff, Chair for Nonlinear Analysis and Modeling, Faculty of Mathematics, University of Duisburg-Essen;
(4) Angela Madeo, Institute for Structural Mechanics and Dynamics, Technical University Dortmund. Authors: Authors: (1) Jendrik Voss, Institute for Structural Mechanics and Dynamics, Technical University Dortmund and a Corresponding Author (jendrik.voss@tu-dortmund.de); (2) Gianluca Rizzi, Institute for Structural Mechanics and Dynamics, Technical University Dortmund; (3) Patrizio Neff, Chair for Nonlinear Analysis and Modeling, Faculty of Mathematics, University of Duisburg-Essen; (4) Angela Madeo, Institute for Structural Mechanics and Dynamics, Technical University Dortmund.

The Role of Curl Terms in Micromorphic Models

Authors:
(1) Jendrik Voss, Institute for Structural Mechanics and Dynamics, Technical University Dortmund and a Corresponding Author (jendrik.voss@tu-dortmund.de);
(2) Gianluca Rizzi, Institute for Structural Mechanics and Dynamics, Technical University Dortmund;
(3) Patrizio Neff, Chair for Nonlinear Analysis and Modeling, Faculty of Mathematics, University of Duisburg-Essen;
(4) Angela Madeo, Institute for Structural Mechanics and Dynamics, Technical University Dortmund. Authors: Authors: (1) Jendrik Voss, Institute for Structural Mechanics and Dynamics, Technical University Dortmund and a Corresponding Author (jendrik.voss@tu-dortmund.de); (2) Gianluca Rizzi, Institute for Structural Mechanics and Dynamics, Technical University Dortmund; (3) Patrizio Neff, Chair for Nonlinear Analysis and Modeling, Faculty of Mathematics, University of Duisburg-Essen; (4) Angela Madeo, Institute for Structural Mechanics and Dynamics, Technical University Dortmund. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 1.1 A Polyethylene-based metamaterial for acoustic control 1.1 A Polyethylene-based metamaterial for acoustic control 2 Relaxed micromorphic modelling of finite-size metamaterials 2 Relaxed micromorphic modelling of finite-size metamaterials 2.1 Tetragonal Symmetry / Shape of elastic tensors (in Voigt notation) 2.1 Tetragonal Symmetry / Shape of elastic tensors (in Voigt notation) 3 Dispersion curves 3 Dispersion curves 4 New considerations on the relaxed micromorphic parameters 4 New considerations on the relaxed micromorphic parameters 4.1 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s bulk material properties 4.1 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s bulk material properties 4.2 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s size 4.2 Consistency of the relaxed micromorphic model with respect to a change in the unit cell’s size 4.3 Relaxed micromorphic cut-offs 4.3 Relaxed micromorphic cut-offs 5 Fitting of the relaxed micromorphic parameters: the particular case of vanishing curvature (without Curl P and Curl P˙) 5 Fitting of the relaxed micromorphic parameters: the particular case of vanishing curvature (without Curl P and Curl P˙) 5.1 Asymptotes 5.1 Asymptotes 5.2 Fitting 5.2 Fitting 5.3 Discussion 5.3 Discussion 6 Fitting of the relaxed micromorphic parameters with curvature (with Curl P) 6 Fitting of the relaxed micromorphic parameters with curvature (with Curl P) 6.1 Asymptotes and 6.2 Fitting 6.1 Asymptotes and 6.2 Fitting 6.3 Discussion 6.3 Discussion 7 Fitting of the relaxed micromorphic parameters with enhanced kinetic energy (with Curl P˙) and 7.1 Asymptotes 7 Fitting of the relaxed micromorphic parameters with enhanced kinetic energy (with Curl P˙) and 7.1 Asymptotes 7.2 Fitting 7.2 Fitting 7.3 Discussion 7.3 Discussion 8 Summary of the obtained results 8 Summary of the obtained results 9 Conclusion and perspectives, Acknowledgements, and References 9 Conclusion and perspectives, Acknowledgements, and References A Most general 4th order tensor belonging to the tetragonal symmetry class A Most general 4th order tensor belonging to the tetragonal symmetry class B Coefficients for the dispersion curves without Curl P B Coefficients for the dispersion curves without Curl P C Coefficients for the dispersion curves with P C Coefficients for the dispersion curves with P D Coefficients for the dispersion curves with P D Coefficients for the dispersion curves with P Abstract We present an inertia-augmented relaxed micromorphic model that enriches the relaxed micromorphic model previously introduced by the authors via a term Curl P˙ in the kinetic energy density. This enriched model allows us to obtain a good overall fitting of the dispersion curves while introducing the new possibility of describing modes with negative group velocity that are known to trigger negative refraction effects. The inertia-augmented model also allows for more freedom on the values of the asymptotes corresponding to the cut-offs. In the previous version of the relaxed micromorphic model, the asymptote of one curve (pressure or shear) is always bounded by the cut-off of the following curve of the same type. This constraint does not hold anymore in the enhanced version of the model. While the obtained curves’ fitting is of good quality overall, a perfect quantitative agreement must still be reached for very small wavelengths that are close to the size of the unit cell. 1 Introduction Metamaterials are materials whose mechanical properties go beyond those of classical materials thanks to their heterogeneous microstructure. They can show unusual static/dynamic responses such as negative Poisson’s ratio [27], twist or bend in response to being pushed or pulled [18, 36], band-gaps [28, 46, 8, 13], cloaking [11, 31], focusing [20, 16], channeling [24, 45], negative refraction [52, 25, 47], etc. The working frequency of each metamaterial strongly depends on the characteristic size and the geometry of the underlying unit cell, as well as on the choice of the base material. In this paper, we present a labyrinthine metamaterial that, thanks to the use of a polymeric based material and an optimized distribution of mass inside the unit cell (see Figure 1), gives rise to a wide acoustic band-gap with characteristic unit cell’s size of the order of centimeters. The direct finite element modeling of structures build up with this labyrinthine metamaterial is unfeasible due to the extremely tight meshing that would be needed to correctly cover the narrow strips of material inside each unit cell. It is thus apparent the need for a homogenized model to use this type of very promising metamaterials in actual engineering designs. Various homogenization techniques have been developed with the purpose of providing rigorous predictions of the macroscopic metamaterial’s mechanical response when the properties of the base materials and their spatial distribution are known. These homogenization approaches have been shown to be useful in describing the overall behavior of metamaterials in the static and quasi-static regimes [6, 40, 3, 30, 21, 48, 35, 9, 12, 44, 29, 19, 22] as well as, more recently, in the dynamic regime [5, 14, 10, 15, 4, 23, 49, 50, 51, 42, 41, 43]. However, these models are often unsuited to deal with finitesize metamaterials, because they are based on upscaling techniques valid for unbounded media. Because of that, finite-size metamaterials’ structures are mostly investigated via Finite Element simulations which are performed using directly the microstructured material, e.g. [26]. The downside of this approach is that the computational cost quickly becomes unsustainable (especially for unit cells as the one presented in this paper), although the propagation patterns obtained are very accurate. This heavily limits the possibility of exploring large-scale or very convoluted geometric meta-structures. To overcome this problem and open up the possibility of designing complex meta-structures using the metamaterial presented in this paper as a basic building block, we propose to use an inertia-augmented relaxed micromorphic model. This model is based on the relaxed micromorphic model that we previously established [34, 32, 17, 1, 2] and has been augmented with a new inertia term accounting for coupled space-time derivatives of the micro-distortion tensor. The relaxed micromorphic model has extensively proven its efficacy in describing the broadband behavior of many infinite and finite-size metamaterials [1, 2, 37, 38, 39] and is extended in this paper so as to be able to account for negative group velocity which was not the case before. We will show that the proposed model is able to describe well the labyrinthine metamaterial’s response for a large range of frequencies (going beyond the first band-gap) and wave numbers (approaching the size of the unit cell) and for all directions of propagation with a limited number of frequency- and scale-independent constitutive parameters. The new inertia-augmented term will be shown to trigger modes with negative group velocities that are known to be associated with negative refraction phenomena. The results presented in this paper will allow us to shortly present new designs of finite-size labyrinthine metamaterials’ structures that can control elastic energy in the acoustic regime for eventual subsequent re-use. 1.1 A Polyethylene-based metamaterial for acoustic control In this section, we present a new unit cell’s design that gives rise to a metamaterial for acoustic control. This unit cell is designed to achieve a band-gap at relatively low frequencies (600−2000 Hz) so that application for acoustic control can be targeted. The unit cell considered is made out of polyethylene, cf. Table 1. Compared to Aluminium or Titanium, that we used for the metamaterials studied in [37, 38, 39], Polyethylene gives rise to lower wave speeds, thus allowing band-gap phenomena to appear at lower frequencies. A further lowering of the band-gap is obtained through the adoption of a labyrinth-type geometry, cf. Figure 1. This structure presents a tetragonal symmetry and thus features a reduced number of parameters with respect to a fully anisotropic system. The circular center of the unit cell is connected by thin bars allowing the heavier center to move easily, thus giving rise to local resonance phenomena of relatively low frequencies while additionally providing a very soft macro-material behaviour. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

New Material Design Approach Could Change How Sound Travels Through Space

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新しい材料設計アプローチは、音の空間内での移動方法を変える可能性がある

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Ny metod för materialdesign kan förändra hur ljud rör sig genom rymden

แนวทางการออกแบบวัสดุใหม่สามารถเปลี่ยนวิธีการเคลื่อนที่ของเสียงผ่านอวกาศได้

Yeni Malzeme Tasarım Yaklaşımı Sesin Uzayda Hareket Etme Biçimini Değiştirebilir

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Today, organizations thrive in a complex and evolving threat landscape and cybersecurity has become pivotal for digital resilience. Various sophisticated attacks such as supply chain breaches, ransomware, phishing via malicious email campaigns, and vulnerabilities in the cloud and Internet of Things (IoT) environments need advanced, performance-sensitive solutions that can do detection in real time and also respond and mitigate these attacks. Artificial intelligence and Machine Learning (ML) play a frontal role in these transformations, providing necessary capabilities to secure digital systems effectively. Role of AI/ML in ransomware To effectively fight cyber threats, security systems need to process massive amounts of data quickly and accurately. That’s where AI and machine learning come in, providing the speed and intelligence needed to stay ahead of attacks. Take ransomware defense, for example—AI-powered systems can analyze file behavior, spot unusual encryption activity, and contain threats within milliseconds, preventing serious damage before it spreads. The report Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques highlights the growing interest in using AI to detect ransomware, particularly because machine learning and deep learning can identify zero-day threats. Another example comes from a paper discussing a novel approach that enhances ransomware detection through Autonomous Feature Resonance. This method improves accuracy by leveraging a decentralized architecture that processes data locally, reducing dependence on centralized systems and minimizing latency. Role of AI/ML in email security and social engineering High-performance, low-latency AI/ML systems have transformed email security by detecting phishing attempts and other malicious campaigns. They analyze metadata, behavioral patterns, and evolving attacker techniques to stay ahead of threats. For example, Gmail’s spam filters rely heavily on machine learning and user feedback to identify spam patterns. The real-time adaptability of these AI/ML systems significantly reduces the risk of successful social engineering attacks—providing a crucial layer of protection for both businesses and individuals. Many modern products come with built-in integrations to prevent malicious activity, and AI/ML systems are playing a crucial role in strengthening cybersecurity. Beyond just protection, they help build user trust and serve as a key competitive advantage. For example, both Google Workspace and Microsoft 365 leverage AI to detect threats, enhance security, and safeguard user data in real time. With the rise of Large Language Models (LLMs), attackers can now automate highly personalized social engineering attacks, making it much harder for traditional security systems to detect malicious intent. Gone are the days of the classic "Nigerian prince" scams—today’s attacks exploit personal connections and insider knowledge to manipulate victims. For example, a joint research paper from Columbia University, the University of Arizona, and others highlights that "The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks." Similarly, a study by Cornell University explored how LLMs can be weaponized for social engineering, further underscoring the evolving cybersecurity threat landscape. As attackers leverage machine learning to create more sophisticated threats, the need for equally advanced AI/ML systems to counter them has never been greater. Deep neural network-based models can process vast amounts of data in real time, identifying anomalies in emails and chat messages by detecting unusual word choices, phrasing, or suspicious links that deviate from normal communication patterns. A paper published in the 2024 2nd International Conference on Cyber Resilience (ICCR) highlights this, stating that "AI-driven behavioral analytics effectively detect subtle, manipulative cues indicative of phishing and other deceitful tactics, considerably reducing the incidence of successful attacks." AI/ML in Real-Time Defense Against Social Engineering Attacks It has become increasingly important these days private communications stay private and a lot of messaging apps are end-to-end encrypted. A lot of metadata analysis is leveraged by AI/ML systems to detect attacks in end-to-end encrypted systems. Example: To detect social engineering attacks from compromised accounts in a private communication world AI monitors leverage behavioral analytics to detect deviations from normal patterns such as login attempts from unusual locations or user behaviors inconsistent with their typical patterns, flagging a potential account compromise and deploying automated defenses such as killing sessions or showing additional security challenges. The Future of AI/ML Systems in Cybersecurity for the Digital Age A modern AI/ML-powered cybersecurity defense system relies on a high-performance, low-latency infrastructure to stay ahead of threats. These systems must operate at massive scales, analyzing millions of events per second to detect and respond to cyberattacks in real time. This is made possible through a combination of edge computing, real-time data processing, and optimized AI/ML algorithms, ensuring both effectiveness and cost efficiency. For example, research highlights how machine learning (ML) models play a crucial role in detecting cyberattacks within Smart Grids (SG) that operate on Software-Defined Networks (SDN), demonstrating the impact of AI-driven security in critical infrastructure. With the growing scale of cyberattacks in scale and sophistication, highly performant AI/ML systems are not a luxury but a necessity. These technologies enable organizations to stay ahead of emerging threats, protect their critical assets and ensure that their inter-connected systems operate securely in an ever-evolving digital world. In conclusion,b highly performant AI/ML systems are a cornerstone in keeping the digital ecosystems safe and allow users to interact with them. Today, organizations thrive in a complex and evolving threat landscape and cybersecurity has become pivotal for digital resilience. Various sophisticated attacks such as supply chain breaches, ransomware, phishing via malicious email campaigns, and vulnerabilities in the cloud and Internet of Things (IoT) environments need advanced, performance-sensitive solutions that can do detection in real time and also respond and mitigate these attacks. Artificial intelligence and Machine Learning (ML) play a frontal role in these transformations, providing necessary capabilities to secure digital systems effectively. Role of AI/ML in ransomware To effectively fight cyber threats, security systems need to process massive amounts of data quickly and accurately. That’s where AI and machine learning come in, providing the speed and intelligence needed to stay ahead of attacks. Take ransomware defense, for example—AI-powered systems can analyze file behavior, spot unusual encryption activity, and contain threats within milliseconds, preventing serious damage before it spreads. The report Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques highlights the growing interest in using AI to detect ransomware, particularly because machine learning and deep learning can identify zero-day threats. report report Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques Another example comes from a paper discussing a novel approach that enhances ransomware detection through Autonomous Feature Resonance . This method improves accuracy by leveraging a decentralized architecture that processes data locally, reducing dependence on centralized systems and minimizing latency. paper paper Autonomous Feature Resonance Role of AI/ML in email security and social engineering High-performance, low-latency AI/ML systems have transformed email security by detecting phishing attempts and other malicious campaigns. They analyze metadata, behavioral patterns, and evolving attacker techniques to stay ahead of threats. For example, Gmail’s spam filters rely heavily on machine learning and user feedback to identify spam patterns. The real-time adaptability of these AI/ML systems significantly reduces the risk of successful social engineering attacks—providing a crucial layer of protection for both businesses and individuals. Gmail’s spam filters Many modern products come with built-in integrations to prevent malicious activity, and AI/ML systems are playing a crucial role in strengthening cybersecurity. Beyond just protection, they help build user trust and serve as a key competitive advantage. For example, both Google Workspace and Microsoft 365 leverage AI to detect threats, enhance security, and safeguard user data in real time. Google Workspace Microsoft 365 With the rise of Large Language Models (LLMs), attackers can now automate highly personalized social engineering attacks, making it much harder for traditional security systems to detect malicious intent. Gone are the days of the classic "Nigerian prince" scams—today’s attacks exploit personal connections and insider knowledge to manipulate victims. For example, a joint research paper from Columbia University , the University of Arizona, and others highlights that "The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks." Similarly, a study by Cornell University explored how LLMs can be weaponized for social engineering, further underscoring the evolving cybersecurity threat landscape. a joint research paper from Columbia University a joint research paper from Columbia University "The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks." a study by Cornell University a study by Cornell University As attackers leverage machine learning to create more sophisticated threats, the need for equally advanced AI/ML systems to counter them has never been greater. Deep neural network-based models can process vast amounts of data in real time, identifying anomalies in emails and chat messages by detecting unusual word choices, phrasing, or suspicious links that deviate from normal communication patterns. A paper published in the 2024 2nd International Conference on Cyber Resilience (ICCR) highlights this, stating that "AI-driven behavioral analytics effectively detect subtle, manipulative cues indicative of phishing and other deceitful tactics, considerably reducing the incidence of successful attacks." A paper A paper 2024 2nd International Conference on Cyber Resilience (ICCR) "AI-driven behavioral analytics effectively detect subtle, manipulative cues indicative of phishing and other deceitful tactics, considerably reducing the incidence of successful attacks." AI/ML in Real-Time Defense Against Social Engineering Attacks It has become increasingly important these days private communications stay private and a lot of messaging apps are end-to-end encrypted. A lot of metadata analysis is leveraged by AI/ML systems to detect attacks in end-to-end encrypted systems. Example: To detect social engineering attacks from compromised accounts in a private communication world AI monitors leverage behavioral analytics to detect deviations from normal patterns such as login attempts from unusual locations or user behaviors inconsistent with their typical patterns, flagging a potential account compromise and deploying automated defenses such as killing sessions or showing additional security challenges. Example: The Future of AI/ML Systems in Cybersecurity for the Digital Age A modern AI/ML-powered cybersecurity defense system relies on a high-performance, low-latency infrastructure to stay ahead of threats. These systems must operate at massive scales, analyzing millions of events per second to detect and respond to cyberattacks in real time. This is made possible through a combination of edge computing, real-time data processing, and optimized AI/ML algorithms, ensuring both effectiveness and cost efficiency. For example, research highlights how machine learning (ML) models play a crucial role in detecting cyberattacks within Smart Grids (SG) that operate on Software-Defined Networks (SDN), demonstrating the impact of AI-driven security in critical infrastructure. research research With the growing scale of cyberattacks in scale and sophistication, highly performant AI/ML systems are not a luxury but a necessity. These technologies enable organizations to stay ahead of emerging threats, protect their critical assets and ensure that their inter-connected systems operate securely in an ever-evolving digital world. In conclusion,b highly performant AI/ML systems are a cornerstone in keeping the digital ecosystems safe and allow users to interact with them.

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