SEED: Semi-Supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget

Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss HCL with active learning to improve robustness against drift by exploiting semantic structure...

Evaluating Tabular Representation Learning for Network Intrusion Detection

Classic Network Intrusion Detection Systems NIDS often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: tha...

RadEar: A Self-Supervised RF Backscatter System for Voice Eavesdropping and Separation

Eavesdropping on voice conversations presents a growing threat to personal privacy and information security. In this paper, we present RadEar, a novel RF backscatter-based system designed to enable covert voice eavesdropping through walls. RadEar consists of two key components: i a batteryless RF...

ThreatFormer-IDS: Robust Transformer Intrusion Detection with Zero-Day Generalization and Explainable Attribution

Intrusion detection in IoT and industrial networks requires models that can detect rare attacks at low false-positive rates while remaining reliable under evolving traffic and limited labels. Existing IDS solutions often report strong in-distribution accuracy, but they may degrade when evaluated ...

PrivFly: A Privacy-Preserving Self-Supervised Framework for Rare Attack Detection in IoFT

The Internet of Flying Things IoFT plays a vital role in modern applications such as aerial surveillance and smart mobility. However, it remains highly vulnerable to cyberattacks that threaten the confidentiality, integrity, and availability of sensitive data. Developing effective intrusion...

Predicting Tail-Risk Escalation in IDS Alert Time Series

Network defenders face a steady stream of attacks, observed as raw Intrusion Detection System IDS alerts. The sheer volume of alerts demands prioritization, typically based on high-level risk classifications. This work expands the scope of risk measurement by examining alerts not only through the...

Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification

Malicious WebShells pose a significant and evolving threat by compromising critical digital infrastructures and endangering public services in sectors such as healthcare and finance. While the research community has made significant progress in WebShell detection i.e., distinguishing malicious...

Identification of Malicious Posts on the Dark Web Using Supervised Machine Learning

Given the constant growth and increasing sophistication of cyberattacks, cybersecurity can no longer rely solely on traditional defense techniques and tools. Proactive detection of cyber threats has become essential to help security teams identify potential risks and implement effective mitigatio...

SAND: A Self-Supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection

The globalized semiconductor supply chain has made Hardware Trojans HT a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ...

A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things

The rapid expansion of the Internet of Things IoT and Wireless Sensor Networks WSNs has significantly increased the attack surface of such systems, making them vulnerable to a wide range of cyber threats. Traditional Intrusion Detection Systems IDS often fail to meet the stringent requirements of...

Multilingual Source Tracing of Speech Deepfakes: a First Benchmark

Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applications, they also raise serious concerns by making it possible to generate convincing fake speech in many languages...

Two Views, One Truth: Spectral and Self-Supervised Features Fusion for Robust Speech Deepfake Detection

Recent advances in synthetic speech have made audio deepfakes increasingly realistic, posing significant security risks. Existing detection methods that rely on a single modality, either raw waveform embeddings or spectral based features, are vulnerable to non spoof disturbances and often overfit...

LENS-DF: Deepfake Detection and Temporal Localization for Long-Form Noisy Speech

This study introduces LENS-DF, a novel and comprehensive recipe for training and evaluating audio deepfake detection and temporal localization under complicated and realistic audio conditions. The generation part of the recipe outputs audios from the input dataset with several critical...

Technical Evaluation of a Disruptive Approach in Homomorphic AI

We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI Hash-based Homomorphic Artificial Intelligence. HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rel...

When Better Features Mean Greater Risks: the Performance-Privacy Trade-Off in Contrastive Learning

With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. However, their widespread use has raised significant concerns about the risk o...

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery

The growing complexity of encrypted network traffic presents dual challenges for modern network management: accurate multiclass classification of known applications and reliable detection of unknown traffic patterns. Although deep learning models show promise in controlled environments, their...

A Contrastive Federated Semi-Supervised Learning Intrusion Detection Framework for Internet of Robotic Things

In intelligent industry, autonomous driving and other environments, the Internet of Things IoT highly integrated with robotic to form the Internet of Robotic Things IoRT. However, network intrusion to IoRT can lead to data leakage, service interruption in IoRT and even physical damage by...

Apple Vision Pro Vulnerability Exposed Virtual Keyboard Inputs to Attackers

Details have emerged about a now-patched security flaw impacting Apple's Vision Pro mixed reality headset that, if successfully exploited, could allow malicious attackers to infer data entered on the device's virtual keyboard. The attack, dubbed GAZEploit, has been assigned the CVE identifier...

Security and Artificial Intelligence: Hype vs. Reality

While artificial intelligence and machine learning are far from new, many in security suddenly believe these technologies will transform their business and enable them to detect every cyber threat that comes their way. But instead, the hype may create more problems than it solves. Recently,...

Monitor More, Worry Less. Outpace Threats With Machine Learning.

In the past two years, enterprises have created more data than has been created in the entire history of humankind. At scale, securing this amount of data requires a re-think of how we grant and revoke access to sensitive files and, more importantly, how we identify and track the inevitable acces...