A Collaborative Intrusion Detection System Using Machine Learning
Krisha Khandhar, Neha Bagul, Shrutika Badgujar, Shreya Bachhav
CIDS, Intrusion, Machine Learning, Optimization
The design and implementation of a Collaborative Intrusion Detection System (CIDS) for precise and effective intrusion detection in a distributed system are presented in this study. The network, kernel, and application levels are where CIDS uses a variety of specialised detectors. In essence, CIDS combines the alarms from these detectors to produce a single intruder alarm. In comparison to separate detectors, this improves detection accuracy without noticeably degrading performance. The optimization algorithm is utilised to help those detectors find the attack faster, and graph-based detection is demonstrated to find the attack. The same is done using machine learning techniques, from feature selection and normalisation to categorization and attack detection.
Article Details
Unique Paper ID: 159756

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 700 - 704
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