Adversarial machine learning for network intrusion detection systems: A comprehensive survey
Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …
network attacks that compromise the security of the data, systems, and networks. In recent …
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 …
Trafficgpt: Breaking the token barrier for efficient long traffic analysis and generation
J Qu, X Ma, J Li - arXiv preprint arXiv:2403.05822, 2024 - arxiv.org
Over the years, network traffic analysis and generation have advanced significantly. From
traditional statistical methods, the field has progressed to sophisticated deep learning …
traditional statistical methods, the field has progressed to sophisticated deep learning …
Threat modeling for machine learning-based network intrusion detection systems
HA Alatwi, C Morisset - … Conference on Big Data (Big Data), 2022 - ieeexplore.ieee.org
Network Intrusion Detection Systems (NIDS) monitor networking environments for
suspicious events that could compromise the availability, integrity, or confidentiality of the …
suspicious events that could compromise the availability, integrity, or confidentiality of the …
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 …
Explainable and transferable adversarial attack for ml-based network intrusion detectors
H Zhang, D Han, Y Liu, Z Wang, J Sun… - arXiv preprint arXiv …, 2024 - arxiv.org
espite being widely used in network intrusion detection systems (NIDSs), machine learning
(ML) has proven to be highly vulnerable to adversarial attacks. White-box and black-box …
(ML) has proven to be highly vulnerable to adversarial attacks. White-box and black-box …
GPMT: Generating practical malicious traffic based on adversarial attacks with little prior knowledge
Abstract Machine learning (ML) is increasingly used for malicious traffic detection and
proven to be effective. However, ML-based detections are at risk of being deceived by …
proven to be effective. However, ML-based detections are at risk of being deceived by …
Advances in adversarial attacks and defenses in intrusion detection system: A survey
Abstract Machine learning is one of the predominant methods used in computer science and
has been widely and successfully applied in many areas such as computer vision, pattern …
has been widely and successfully applied in many areas such as computer vision, pattern …
DNS Exfiltration Guided by Generative Adversarial Networks
Today, DNS exfiltration attacks are detected by checking for anomalies present in the traffic,
such as unusu-ally high transmission rates to a single domain and/or DNS query patterns …
such as unusu-ally high transmission rates to a single domain and/or DNS query patterns …
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning
Embedding covert streams into a cover channel is a common approach to circumventing
Internet censorship, due to censors' inability to examine encrypted information in otherwise …
Internet censorship, due to censors' inability to examine encrypted information in otherwise …