The paper addresses the lack of statistical validity in language‑model‑based network intrusion detection. It introduces traffic‑aware conformal prediction, which calibrates on attacker‑expected traffic to restore coverage guarantees, and further mitigates adaptive attacks by removing attacker‑controllable features, achieving exact pathwise coverage. Experiments on three benchmarks show that this approach maintains coverage while incurring a modest accuracy cost.
By Zhenpeng Li
arXiv:2607. 17105v1 Announce Type: cross Abstract: It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks.
By Khushnaseeb Roshan
arXiv:2504. 01882v2 Announce Type: replace Abstract: The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious actors to bypass traditional monitoring and intrusion detection systems while evading detection by conventional traffic analysis techniques.
By Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo, Carlos Garcia-Rubio, Rebeca P. D\'iaz-Redondo, Celeste Campo, Ana Fern\'andez-Vilas, Manuel Fern\'andez-Veiga
arXiv:2401. 14283v4 Announce Type: replace-cross Abstract: In today's data-driven world, the proliferation of publicly available information raises security concerns due to the information leakage (IL) problem.
By Pritha Gupta, Marcel Wever, Eyke H\"ullermeier
The article discusses a notable imbalance in AI security research, where studies on attacking AI systems outnumber those on defending them. It highlights that this skew is evident across various subfields such as federated learning, speech recognition, membership inference, and large language models. The authors argue that attack papers often benefit from favorable evaluation conditions, whereas defense papers face stricter standards, resulting in a literature rich in vulnerabilities but lacking robust, deployable protections.
By Youqian Zhang
The paper presents a fast machine unlearning method that uses Hessian analysis to identify correlated training data and applies a closed‑form update rule. This approach achieves an 82× speedup over traditional influence‑function unlearning while maintaining or slightly improving model accuracy. Experiments on seven dataset‑architecture pairs, including CIFAR‑100 with ResNet‑50, show strong forgetting performance and low vulnerability to membership inference attacks.
By Ayushi Thakur, Ruchir Gupta, Amit Kumar Jaiswal, Prayag Tiwari