Papers
1–6 of 6A Novel Contrastive Loss for Zero-Day Network Intrusion Detection
Machine learning has achieved state-of-the-art results in network intrusion detection; however, its performance significantly degrades when confronted by a new attack class -- a zero-day attack. In si...
SemFuzz: A Semantics-Aware Fuzzing Framework for Network Protocol Implementations
Network protocols are the foundation of modern communication, yet their implementations often contain semantic vulnerabilities stemming from inadequate understanding of specification semantics. Existi...
Role Classification of Hosts within Enterprise Networks Based on Connection Patterns
Role classification involves grouping hosts into related roles. It exposes the logical structure of a network, simplifies network management tasks such as policy checking and network segmentation, and...
Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks
Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems (NIDS), they can jeopardize...
Machine Learning for Network Attacks Classification and Statistical Evaluation of Machine Learning for Network Attacks Classification and Adversarial Learning Methodologies for Synthetic Data Generation
Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal time for artificial intelligence (AI), with even more soph...
Unsupervised Cross-Protocol Anomaly Analysis in Mobile Core Networks via Multi-Embedding Models Consensus
Mobile core networks rely on several signalling protocols in parallel, such as SS7, Diameter, and GTP, so many security-relevant problems become visible only when their interactions are analyzed joint...