教師資料查詢 | 類別: 期刊論文 | 教師: 莊博任 Chuang Po-jen (瀏覽個人網頁)

標題:Employing On-Line Training in SDN Intrusion Detection
學年
學期
出版(發表)日期2021/03/01
作品名稱Employing On-Line Training in SDN Intrusion Detection
作品名稱(其他語言)
著者Po-Jen Chuang; Kuan-Lin Wu
單位
出版者
著錄名稱、卷期、頁數Journal of Information Science and Engineering 37(2), p.483-496
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.
關鍵字Software Defined Networks (SDNs);Intrusion Detection System (IDS);Machine learning;Anomaly detection;On-line training;Network security
語言英文
ISSN1016-2364
期刊性質國內
收錄於SCI;EI;
產學合作
通訊作者Po-Jen Chuang
審稿制度
國別中華民國
公開徵稿
出版型式,電子版,紙本
相關連結
SDGs
  • 優質教育,產業創新與基礎設施
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