arXiv Machine Learning

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.

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 Machine Learning

Jun 25

A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

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.

By Sajib Debnath, Remon Das

efficiency benchmarks

arXiv Machine Learning

Jun 5

An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

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.

By Mohammad Tariq Ikhlas, Pohanyar Khowaja Khil, Malik Muhammad Mueed Aslam, Muhammad Khuram Shahzad

arXiv AI

Aug 11

Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems

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.

By Ayoub Si-ahmed, Mohammed Ali Al-Garadi, Narhimene Boustia

safety

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 AI

Jul 7

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN

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.

By Long Zhao, Shixun Ji, Bin Cheng, Bin He

benchmarks

arXiv Machine Learning

22h 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 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 AI

Jul 1

An AI-Based Solution for Secure Service Provisioning in IoT

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

agents reinforcement-learning

arXiv AI

6d ago

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

arXiv:2608. 12190v1 Announce Type: cross Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response.

By Md Yassir Mottalib, Md Yousuf, Eklachur Rahman Bhuiyan, S M Ahsan Habib, Sonjoy Kumar Dey, Md. Salahuddin Gazi, Molay Kumar Roy, Asaduzzaman Anik

agents reinforcement-learning

arXiv Machine Learning

Jul 7

F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks

arXiv:2607. 04698v1 Announce Type: new Abstract: The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS).

By Mohammad Ansarimehr, Somayeh Changiz, Ehsan Baghishani, Ali Mousavi

benchmarks safety

arXiv AI

Jun 2

XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT

arXiv:2606. 00134v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDS) in Internet of Things (IoT) environments face significant challenges due to data heterogeneity, lack of labeled data, and limited model interpretability.

By Ambreen Aslam, Maaz Hassan, Bibi Zahra, Muhammad Khuram Shahzad

safety

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