Intrusion Detection based on Federated Learning: a systematic review

JL Hernandez-Ramos, G Karopoulos… - arxiv preprint arxiv …, 2023 - arxiv.org
The evolution of cybersecurity is undoubtedly associated and intertwined with the
development and improvement of artificial intelligence (AI). As a key tool for realizing more …

A data-driven network intrusion detection system using feature selection and deep learning

L Zhang, K Liu, X **e, W Bai, B Wu, P Dong - Journal of Information Security …, 2023 - Elsevier
Network intrusion detection system (NIDS) is an important line of defense for network
security as network attacks become more frequent. In this paper, we propose a data-driven …

A soft actor-critic reinforcement learning algorithm for network intrusion detection

Z Li, C Huang, S Deng, W Qiu, X Gao - Computers & Security, 2023 - Elsevier
Network intrusion detection plays a very important role in network security. Although current
deep learning-based intrusion detection algorithms have achieved good detection …

On the detection of lateral movement through supervised machine learning and an open-source tool to create turnkey datasets from sysmon logs

C Smiliotopoulos, G Kambourakis… - International Journal of …, 2023 - Springer
Lateral movement (LM) is a principal, increasingly common, tactic in the arsenal of
advanced persistent threat (APT) groups and other less or more powerful threat actors. It …

Best of both worlds: Detecting application layer attacks through 802.11 and non-802.11 features

E Chatzoglou, G Kambourakis, C Smiliotopoulos… - Sensors, 2022 - mdpi.com
Intrusion detection in wireless and, more specifically, Wi-Fi networks is lately increasingly
under the spotlight of the research community. However, the literature currently lacks a …

Wireless local area networks threat detection using 1D-CNN

M Natkaniec, M Bednarz - Sensors, 2023 - mdpi.com
Wireless Local Area Networks (WLANs) have revolutionized modern communication by
providing a user-friendly and cost-efficient solution for Internet access and network …

Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices.

M Fatima, O Rehman, IMH Rahman, A Ajmal… - Future …, 2024 - search.ebscohost.com
The emergence of smart technologies and the wide adoption of the Internet of Things (IoT)
have revolutionized various sectors, yet they have also introduced significant security …

Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

ZK Maseer, QK Kadhim, B Al‐Bander, R Yusof… - IET …, 2024 - Wiley Online Library
Intrusion detection systems built on artificial intelligence (AI) are presented as latent
mechanisms for actively detecting fresh attacks over a complex network. The authors used a …

Rule-based system with machine learning support for detecting anomalies in 5g wlans

K Uszko, M Kasprzyk, M Natkaniec, P Chołda - Electronics, 2023 - mdpi.com
The purpose of this paper is to design and implement a complete system for monitoring and
detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the …

Adversarial attack detection framework based on optimized weighted conditional stepwise adversarial network

K Barik, S Misra, L Fernandez-Sanz - International Journal of Information …, 2024 - Springer
Abstract Artificial Intelligence (AI)-based IDS systems are susceptible to adversarial attacks
and face challenges such as complex evaluation methods, elevated false positive rates …