Deep learning for insider threat detection: Review, challenges and opportunities
Insider threats, as one type of the most challenging threats in cyberspace, usually cause
significant loss to organizations. While the problem of insider threat detection has been …
significant loss to organizations. While the problem of insider threat detection has been …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - ar** study
Ł Korzeniowski, K Goczyła - IEEE Access, 2022 - ieeexplore.ieee.org
Logging is a common practice in software engineering to provide insights into working
systems. The main uses of log files have always been failure identification and root cause …
systems. The main uses of log files have always been failure identification and root cause …
Insider threat detection using deep autoencoder and variational autoencoder neural networks
Internal attacks are one of the biggest cybersecurity issues to companies and businesses.
Despite the implemented perimeter security systems, the risk of adversely affecting the …
Despite the implemented perimeter security systems, the risk of adversely affecting the …
Machine learning approaches to detect, prevent and mitigate malicious insider threats: State-of-the-art review
Insider threats are profoundly damaging and pose serious security challenges. These
threats, perpetrated by insiders, may arise from delinquency, retaliation, or motives such as …
threats, perpetrated by insiders, may arise from delinquency, retaliation, or motives such as …