Intrusion Detection
Intrusion detection focuses on automatically identifying malicious activities within computer networks and systems, aiming to enhance cybersecurity. Current research emphasizes the application of machine learning, particularly deep learning models like transformers, recurrent neural networks (RNNs), and graph neural networks (GNNs), often combined with ensemble methods and autoencoders for feature extraction and improved accuracy. This field is crucial for protecting diverse systems, from IoT devices and 5G networks to autonomous vehicles and industrial control systems, and advancements in intrusion detection directly impact the security and reliability of critical infrastructure and everyday technologies.
Papers
Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
Mona Esmaeili, Morteza Rahimi, Matin Khajavi, Dorsa Farahmand, Hadi Jabbari Saray
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach
Tuan-Cuong Vuong, Cong Chi Nguyen, Van-Cuong Pham, Thi-Thanh-Huyen Le, Xuan-Nam Tran, Thien Van Luong
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks
Osama Mustafa, Khizer Ali, Talha Naqash
AI-Driven Intrusion Detection Systems (IDS) on the ROAD dataset: A Comparative Analysis for automotive Controller Area Network (CAN)
Lorenzo Guerra, Linhan Xu, Pavlo Mozharovskyi, Paolo Bellavista, Thomas Chapuis, Guillaume Duc, Van-Tam Nguyen
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks
Amar Amouri, Mohamad Mahmoud Al Rahhal, Yakoub Bazi, Ismail Butun, Imad Mahgoub
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic
Maximilian Wolf, Dieter Landes, Andreas Hotho, Daniel Schlör