arXiv:2608. 00118v1 Announce Type: cross Abstract: Cybersecurity is a fundamental requirement for protecting wearable devices used in healthcare Internet of Things (H-IoT) systems.
arXiv:2609.39584v1 Announce Type: cross Abstract: Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Intern...
arXiv:2606. 05714v1 Announce Type: cross Abstract: Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing.
arXiv:2606. 25200v1 Announce Type: new Abstract: The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels.
arXiv:2606. 05776v1 Announce Type: cross Abstract: With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments.
arXiv:2403. 15509v3 Announce Type: replace-cross Abstract: Knowledge distillation (KD) has been widely used to improve lightweight AI models by transferring soft-label knowledge from a large teacher model to a student model.
arXiv:2403. 09752v4 Announce Type: replace-cross Abstract: The Internet of Medical Things transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention.
arXiv:2609.36167v1 Announce Type: cross Abstract: Distributed Denial of Service attacks are a growing threat to network infrastructure, and new techniques, including the use of generative AI, make th...
arXiv:2603. 17717v4 Announce Type: replace-cross Abstract: Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS).
arXiv:2607. 02981v1 Announce Type: cross Abstract: Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns.
Diff‑DDoS is a three‑phase framework that uses tabular diffusion models to generate realistic cyber‑physical attacks and strengthen DDoS detectors for 5G‑enabled systems. First, a CNN cell‑level detector is trained on call detail record (CDR) grids; second, a tabular denoising diffusion probabilistic model (TabDDPM) learns normal CDR aggregates to synthesize realistic attacks; third, adversarial diffusion training (ADT) iteratively produces hard, distribution‑preserving samples that harden the detector. On the Milano CDR dataset, ResNet50 with ADT achieves near‑perfect F1‑scores across multiple attack scenarios, outperforming existing synthetic‑data methods such as CTGAN.
The paper introduces a GAN‑based framework for detecting DDoS attacks that are designed to evade traditional security systems. It combines Random Forests, Deep Neural Ensembles, and Transformer models trained on the CICDDoS2019 dataset with synthetic adversarial traffic generated by a WGAN‑GP. Experiments show that this hybrid training significantly improves detection accuracy and resilience against unseen adversarial traffic, and real‑world tests confirm its practical effectiveness.
arXiv:2607. 01305v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments.