Employing On-Line Training in SDN Intrusion Detection

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/119985

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题名:	Employing On-Line Training in SDN Intrusion Detection
作者:	Chuang, Po-Jen;Wu, Kuan-Lin
关键词:	Software Defined Networks (SDNs);Intrusion Detection System (IDS);Machine learning;Anomaly detection;On-line training;Network security
日期:	2021-03
上传时间:	2021-03-03 12:11:03 (UTC+8)
摘要:	In SDN anomaly detection systems, when a training mechanism adopts semi-supervised learning (consisting of self-training and self-learning) to attain the classifiers of on-line training, it may cause the accumulation of identification errors – to degrade the performance. This paper presents a new training and learning mechanism which involves the operations of self-training and active learning to solve the problem. The proposed mechanism first adds samples with “high confidence weights” and classified as “malicious” to the training set by random selection. It then practices active learning to label those samples with “low confidence weights”and add them to the training set for training, to further lift up identification accuracy. A faster clustering method has been brought in to reduce the operation time of active learning. In classifier retraining, parallel training is applied to keep the classifier in constant service without interruption. Simulation results show that, in contrast to existing active learning IDS (ALIDS), our new mechanism performs better in identifying unknown attacks, without occupying the operation time of detection as it processes both training and detection in parallel.
關聯:	Journal of Information Science and Engineering 37(2), p.483-496
显示于类别:	[電機工程學系暨研究所] 期刊論文

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