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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 …
[HTML][HTML] Evolving techniques in cyber threat hunting: A systematic review
In the rapidly changing cybersecurity landscape, threat hunting has become a critical
proactive defense against sophisticated cyber threats. While traditional security measures …
proactive defense against sophisticated cyber threats. While traditional security measures …
Graph neural networks for intrusion detection: A survey
Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
Flash: A comprehensive approach to intrusion detection via provenance graph representation learning
MU Rehman, H Ahmadi… - 2024 IEEE Symposium on …, 2024 - ieeexplore.ieee.org
Recently, provenance-based Intrusion Detection Systems (IDSes) have gained popularity for
their potential in detecting sophisticated Advanced Persistent Threat (APT) attacks. These …
their potential in detecting sophisticated Advanced Persistent Threat (APT) attacks. These …
{PROGRAPHER}: An anomaly detection system based on provenance graph embedding
In recent years, the Advanced Persistent Threat (APT), which involves complex and
malicious actions over a long period, has become one of the biggest threats against the …
malicious actions over a long period, has become one of the biggest threats against the …
Sok: Pragmatic assessment of machine learning for network intrusion detection
Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Temporal link prediction: A unified framework, taxonomy, and review
Dynamic graphs serve as a generic abstraction and description of the evolutionary
behaviors of various complex systems (eg, social networks and communication networks) …
behaviors of various complex systems (eg, social networks and communication networks) …
A survey on malware detection with graph representation learning
Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …
complexity of malware. Traditional detection methods based on signatures and heuristics …
LogFiT: Log anomaly detection using fine-tuned language models
System logs are a valuable source of information for monitoring and maintaining the security
and stability of computer systems. Techniques based on Deep Learning and Natural …
and stability of computer systems. Techniques based on Deep Learning and Natural …
" Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security Conferences
Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …
seemingly contradictory results and expands the boundaries of known discoveries …