A Survey on Graph Neural Networks for Intrusion Detection Systems: Methods, Trends and Challenges
Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …
increasing sophistication of cyber attack methods, traditional detection approaches are …
Towards real-time network intrusion detection with image-based sequential packets representation
J Ghadermazi, A Shah… - IEEE Transactions on Big …, 2024 - ieeexplore.ieee.org
Machine learning (ML) and deep learning (DL) advancements have greatly enhanced
anomaly detection of network intrusion detection systems (NIDS) by empowering them to …
anomaly detection of network intrusion detection systems (NIDS) by empowering them to …
Empirical evaluation of autoencoder models for anomaly detection in packet-based nids
Anomaly detection is critical for network security. Unsupervised learning models trained on
benign network traffic data aim to detect anomalies without relying on attack data sets …
benign network traffic data aim to detect anomalies without relying on attack data sets …
Adversarial Example Attacks and Defenses in DNS Data Exfiltration
Abstract The Domain Network System (DNS) protocol is used on a daily basis to access the
internet. It acts as a phone book that allows users to access websites using words rather …
internet. It acts as a phone book that allows users to access websites using words rather …
Evasion Scheme for Intrusion Detection System Based on Demgan
D Xu, Y Lv, C Zhang, M Wang, J Zhao - Available at SSRN 4868511 - papers.ssrn.com
Network intrusion detection systems (IDS) are often an effective means of blocking attacks
on network traffic. Currently, IDS are mainly based on machine learning (ML) models, but …
on network traffic. Currently, IDS are mainly based on machine learning (ML) models, but …