Cybersecurity at the intersection of AI covers both how machine learning strengthens defense and how it opens new attack surfaces. We report on adversarial robustness, model and data security, and the practical risks that come with deploying AI in production, grounded in current research.
Edge computing exists because sending everything to the cloud is slow, expensive, and increasingly a privacy liability. Cameras, routers, industrial sensors, and vehicles now do a meaningful share of their own processing, which is great for latency and bandwidth. It…
What 131 Studies Reveal About Federated Learning for Edge Cyberattack Detection Read More »
Adversarial machine learning is not a new worry. Since researchers first showed that tiny, carefully chosen changes to an input could flip a neural network’s classification, the idea has spread into every domain where models make consequential decisions, and network…
Adversarial Machine Learning Meets Intrusion Detection Read More »
For two decades, phishing training told people to watch for the tells. Clumsy grammar. A generic greeting. A sender address that looked almost right but not quite. That advice assumed a human was typing the message, usually working from a…
AI Phishing Attacks Nobody Can Spot Anymore Read More »
Most of what the research community knows about backdoor attacks on graphs comes from homogeneous graphs, where every node and every edge is the same type. In that setting, an attacker can attach almost any subgraph to almost any node,…
Backdoor Attacks Now Target Heterogeneous Graph Neural Networks Read More »
Federated recommenders drift apart when clients simply average parameters. AlignFedRec shares item similarity matrices instead, aligning structure rather than embeddings.
Why Federated Recommenders Break and How Structural Alignment Fixes Them Read More »
PFLlib benchmarks traditional and personalized federated learning algorithms across three heterogeneity scenarios, giving the field a common yardstick.
PFLlib Brings Order to Personalized Federated Learning Research Read More »
A team from Carnegie Mellon University flipped conventional wisdom on its head — proving that agents with different behavior policies don’t just tolerate each other’s differences, they actively benefit from them. And a novel importance-averaging scheme eliminates the last remaining…
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond Read More »
A team from Seoul National University and Ewha Womans University pinpointed a root cause of robust overfitting — the model memorizes tricky outliers during adversarial training rather than learning from them. Their remedy, difficulty proportional label smoothing, costs almost nothing…
Why Hard Training Examples Hurt Neural Networks — And How DPLS Fixes It Read More »
A multi-institution team from the University of Chicago, NJIT, Ant Financial, Zhejiang University, and USC cast client selection as a non-stationary online learning problem — and built an adaptive sampler that provably beats uniform sampling by reducing the gradient variance…
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Read More »
University of Waterloo researchers built SUP-Net, a 3D convolutional U-Net that fixes Doppler ultrasound’s fundamental speed limit by upsampling the raw slow-time signal — before any Doppler processing occurs — allowing color Doppler and pulse wave Doppler to work reliably…
SUP-Net: Deep Learning Fixes Doppler Ultrasound Aliasing by Upsampling the Raw Signal Read More »