arXiv Machine Learning

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.

arXiv AI

Jun 11

LSTM based IoT Device Identification

arXiv:2304. 13905v2 Announce Type: replace-cross Abstract: While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market.

By Kahraman Kostas

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

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 AI

Jul 1

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks

arXiv:2606. 31594v1 Announce Type: cross Abstract: The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more.

By Rana Alharbi, Chuadhry Mujeeb Ahmed

safety

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

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 AI

Jul 2

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

arXiv:2607. 00553v1 Announce Type: cross Abstract: Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment.

By MD Azizul Hakim, Md Shihab Uddin, Talha Ibne Anis

efficiency safety

arXiv AI

Jul 2

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.

By Jinju Park, Seokho Kang

llms rag benchmarks

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 AI

Jun 17

Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection

arXiv:2606. 17109v1 Announce Type: cross Abstract: Given their effectiveness in modeling the relational structure among network traffic flows, graph neural networks (GNNs) have been widely adopted in network intrusion detection systems (NIDSs).

By Jianli Dai, Guangwei Wu, Jiacheng Li, Weiping Wang, An He, Xinjun Xiao

benchmarks

arXiv Machine Learning

6d ago

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

Hugging Face Trending Papers

Aug 12

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

With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based dynamic cyber defense framework.

agents reinforcement-learning

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