DeBERTa-ConPara: Attack-Aware and Deployment-Realistic Detection of AI-Generated Text

Robust detection of AI-generated text under deployment conditions is challenging: distribution shifts across domains and generators, adversarial perturbations of the input surface, and the absence of target-domain labels for threshold calibration all degrade detectors that perform well in-domain...

AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection

Email spam and phishing attacks remain a critical security threat. Adversaries increasingly exploit large language models to craft contextually convincing malicious messages, and existing spam detection systems often struggle to keep pace. Generalization across diverse and evolving attack scenari...

Adaptive Intrusion Detection System Using Transformer-Based Neural Networks and Continual Learning Approach with Adversarial Investigation

Network intrusion detection systems IDS trained on fixed traffic snapshots decay silently after deployment as threat distributions shift. Fine-tuning models on new attacks triggers catastrophic forgetting, while retraining from scratch is computationally infeasible. Replay-based continual learnin...

Improving Router Security Using BERT

Previous work on home router security has shown that using system calls to train a transformer-based language model built on a BERT-style encoder using contrastive learning is effective in detecting several types of malware, but the performance remains limited at low false positive rates. In this...

Engineering Attack Vectors and Detecting Anomalies in Additive Manufacturing

Additive manufacturing AM is rapidly integrating into critical sectors such as aerospace, automotive, and healthcare. However, this cyber-physical convergence introduces new attack surfaces, especially at the interface between computer-aided design CAD and machine execution layers. In this work, ...

Self-Supervised Learning of Graph Representations for Network Intrusion Detection

Detecting intrusions in network traffic is a challenging task, particularly under limited supervision and constantly evolving attack patterns. While recent works have leveraged graph neural networks for network intrusion detection, they often decouple representation learning from anomaly detectio...

AttentionGuard: Transformer-Based Misbehavior Detection for Secure Vehicular Platoons

Vehicle platooning, with vehicles traveling in close formation coordinated through Vehicle-to-Everything V2X communications, offers significant benefits in fuel efficiency and road utilization. However, it is vulnerable to sophisticated falsification attacks by authenticated insiders that can...