Sscl-ids: Enhancing generalization of intrusion detection with self-supervised contrastive learning
With the increasing diversity and complexity of cyber attacks on computer networks, there is
a growing demand for Intrusion Detection Systems (IDS) that can accurately categorize new …
a growing demand for Intrusion Detection Systems (IDS) that can accurately categorize new …
The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users
Intrusion Detection Systems (IDS) tackle the challenging task of detecting network attacks as
fast as possible. As this is getting more complex in modern enterprise networks, Artificial …
fast as possible. As this is getting more complex in modern enterprise networks, Artificial …
HALIDS: A hardware-assisted machine learning IDS for in-network monitoring
Early decision-making at the network device level is crucial for network security. This entails
moving beyond traditional forwarding functions towards more intelligent network devices …
moving beyond traditional forwarding functions towards more intelligent network devices …
A data-driven solution for improving transferability of traffic flow feature selection
The expansion of Internet connectivity has increased cyber threats in computer networks.
Machine Learning (ML)-based Intrusion Detection Systems (IDS) have emerged as a …
Machine Learning (ML)-based Intrusion Detection Systems (IDS) have emerged as a …
Detecting Attacks at Switching Speed: AI/ML and Active Learning for in-Network Monitoring in Data Planes
Early decision-making at the network device is crucial for network security. This entails
moving beyond traditional forwarding functions towards more intelligent network devices …
moving beyond traditional forwarding functions towards more intelligent network devices …
Enhancing the Security of Software-Defined Networking through Forensic Memory Analysis
The increasing complexity and dynamic nature of software-defined networking (SDN)
environments pose significant challenges for network security. We propose a methodology …
environments pose significant challenges for network security. We propose a methodology …
Integrating Online Learning with Collaborative Machine Learning for Continuous Intrusion Detection in SDN
Software-Defined Networking (SDN) improves network management and flexibility by
separating control and data plane functions. However, the centralized architecture of SDN …
separating control and data plane functions. However, the centralized architecture of SDN …
Agree to Disagree: Exploring Consensus of XAI Methods for ML-based NIDS
The increasing complexity and frequency of cyber attacks require Network Intrusion
Detection Systems (NIDS) that can adapt to evolving threats. Artificial intelligence (AI) …
Detection Systems (NIDS) that can adapt to evolving threats. Artificial intelligence (AI) …
Verifying the Robustness of Machine Learning based Intrusion Detection Against Adversarial Perturbation
Neural networks (NNs) have been extensively adapted to various security tasks, such as
spam detection, phishing, and intrusion detection. Particularly in IDS, NNs face significant …
spam detection, phishing, and intrusion detection. Particularly in IDS, NNs face significant …
[PDF][PDF] The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users
J KÖGEL, S SCHWINGER, M SICHERMANN - 2024 - opus.bibliothek.uni-augsburg.de
ABSTRACT Intrusion Detection Systems (IDS) tackle the challenging task of detecting
network attacks as fast as possible. As this is getting more complex in modern enterprise …
network attacks as fast as possible. As this is getting more complex in modern enterprise …