arXiv AI

Detecting and Mitigating DDoS Attacks with AI: A Survey

arXiv:2503. 17867v3 Announce Type: replace-cross Abstract: Distributed Denial of Service attacks represent an active cybersecurity research problem.

arXiv AI

Jul 13

Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation

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).

By Iakovos-Christos Zarkadis, Christos Douligeris

reinforcement-learning multimodal safety

arXiv Machine Learning

Jun 5

Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018

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.

By Md. Iqbal Hossan, Md. Serajul Kabir Chowdhury Rubel, Md. Arifur Rahman, B. M. Taslimul Haque

arXiv AI

1d ago

Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks

arXiv:2608. 17352v1 Announce Type: new Abstract: Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses traffic, infers intent, and supports an adaptive response under severe class imbalance and non-stationary conditions.

By Mohammad Arif Hossain, Yeahia Sarker, Md Jafrin Hossain, Most. Humayra Khanom Rime, Nirwan Ansari

benchmarks safety

arXiv AI

Jun 30

PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

arXiv:2606. 28439v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy.

By Jinhao You, Zan Zhou, Shujie Yang, Yi Sun, Lei Zhang, Changqiao Xu

computer-vision safety

arXiv Machine Learning

Jul 31

Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights

arXiv:2409. 08521v2 Announce Type: replace-cross Abstract: In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previously observed behaviors.

By Tian-Yi Zhou, Matthew Lau, Jizhou Chen, Wenke Lee, Xiaoming Huo

benchmarks

arXiv AI

Jul 3

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

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.

By Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar, Qixu Gong, Wenbin Jiang, Huiping Cao, Satyajayant Misra, Jayashree Harikumar

llms diffusion benchmarks safety data-engineering

arXiv Machine Learning

Aug 4

Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT

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.

By Mirza Akhi

efficiency safety

arXiv Machine Learning

1d ago

Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Models

arXiv:2608. 17796v1 Announce Type: cross Abstract: Deep learning-based DDoS detectors for 5G-enabled cyber-physical systems face scarce labeled attack data and unrealistic synthetic substitutes, which limit robustness against adaptive adversaries.

By Bilal Hussain, Xiao Tang, Qinghe Du, Tan Li, Muhammad Azhar, Danista Khan

diffusion safety

arXiv Machine Learning

Jun 29

CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

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

safety

arXiv AI

Jun 6

GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks

arXiv:2606. 05844v1 Announce Type: cross Abstract: Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats.

By Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker

llms agents benchmarks

arXiv Machine Learning

Aug 13

Dueling Deep Q-Learning for Intrusion Detection

arXiv:2608. 11291v1 Announce Type: cross Abstract: Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods.

By Logan Luna (Georgia Institute of Technology), Matthew P. Berkowitz (Embry-Riddle Aeronautical University), Laxima Niure Kandel (Embry-Riddle Aeronautical University), Sirio Jansen-S'anchez (Embry-Riddle Aeronautical University)

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 21

A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic

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

safety

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