arXiv Machine Learning
Jul 13
Regression-aware Continual Learning for Android Malware Detection
arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.
Continual Backdoor Training in IoT/CPS
arXiv:2606. 14987v1 Announce Type: cross Abstract: Internet of Things (IoT) and Cyber-physical systems (CPS) increasingly rely on continual learning (CL) to adapt to evolving environments, device heterogeneity, and concept drift, thereby improving overall utility.
Related stories
arXiv AI
Jul 1
An AI-Based Solution for Secure Service Provisioning in IoT
arXiv:2606. 30701v1 Announce Type: cross Abstract: As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions.
arXiv AI
1d ago
Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis
arXiv:2511. 11439v3 Announce Type: replace-cross Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics.
arXiv AI
Jul 14
Closing the Loop: An Access-Control Architecture for Automated, Anomaly-Driven Network Revocation in IoT Deployments
arXiv:2607. 11649v1 Announce Type: cross Abstract: Network-based anomaly detection for IoT devices has matured to the point of reporting strong detection accuracy, yet most published systems stop at raising an alert and leave the question of automated enforcement to future work or to a programmable data plane that few real networks operate.
Hugging Face Trending Papers
Jun 17
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses.
arXiv AI
Jun 9
Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips
arXiv:2606. 09548v1 Announce Type: cross Abstract: Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data.
arXiv Machine Learning
Jun 18
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution
arXiv:2606. 19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces.
arXiv AI
Jul 23
OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks
arXiv:2607. 19351v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions through inter-agent communication to propagate harmful behaviors.
arXiv Machine Learning
Jul 27
Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense
arXiv:2512. 23849v2 Announce Type: replace-cross Abstract: Sophisticated attackers can evade detection-based security by using encryption, stealth tactics, and low-rate attack patterns.
arXiv AI
Jun 11
LSTM based IoT Device Identification
arXiv:2304. 13905v2 Announce Type: replace-cross Abstract: While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market.
arXiv Machine Learning
Jul 9
What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
arXiv:2510. 13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability.
arXiv Machine Learning
1d ago
Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling
arXiv:2608. 15465v1 Announce Type: cross Abstract: Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks.