Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian linear functions as hidden layers display autonomous learning capabilities and are a highly accurate anomaly detection method that can be implemented in cyberattack detection and intrusion prevention with low incidence of false positives.
Nunez, Juan E.; Tchegui Donfack, Roger W.; Rohit, Rohit; and Horn, Hayley
"Self-Learning Algorithms for Intrusion Detection and Prevention Systems (IDPS),"
SMU Data Science Review: Vol. 6:
2, Article 20.
Available at: https://scholar.smu.edu/datasciencereview/vol6/iss2/20
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