arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.
By Daniele Ghiani, Daniele Angioni, Giorgio Piras, Angelo Sotgiu, Luca Minnei, Srishti Gupta, Maura Pintor, Fabio Roli, Battista Biggio
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.
By Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo, Antonino Nocera, Vinod P
The paper explores how to combine Federated Learning with TinyML model compression techniques—such as knowledge distillation, structured pruning, and quantization—to create lightweight, privacy‑preserving intrusion detection systems for IoT devices. It evaluates these strategies within a federated training pipeline and finds that training stability is crucial; a server‑coordinated cosine learning‑rate schedule boosts Attack Recall from 46.7% to 93.85% while still allowing significant model compression and efficient edge deployment.
By Younsoo Park, Seokhyoen Bae, Shasi Kumar Ramachandran Prabhu, Suman Saha, Peilong Li
arXiv:2609.06346v1 Announce Type: new
Abstract: Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However,...
By Keyu Lin, Fei Ye, Qihe Liu, Shijie Zhou, Jiguo Yu
The paper addresses the challenge of adapting malware detection systems to new threats without retraining from scratch, focusing on the Few-Shot Class-Incremental Learning (FSCIL) setting. It proposes a hybrid framework that uses a self-supervised learning backbone pre-trained on malware packets, incorporates Low-Rank Adaptation (LoRA) to adapt the model while preserving core representations, and employs a prototype-based classification head for incremental sessions. Experiments on multiple datasets show that this approach consistently outperforms existing FSCIL baselines and achieves state-of-the-art performance.
By Kyle Stein, Guillermo Francia, III Eman El-Sheikh, Andrew Arash Mahyari
arXiv:2511. 11439v3 Announce Type: replace-cross Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics.
By Yiling He, Junchi Lei, Hongyu She, Shuo Shao, Xinran Zheng, Yiping Liu, Zhan Qin, Lorenzo Cavallaro