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:Enhanced Attack Blocking in IoT Environments: Engaging Honeypots and Machine Learning in SDN OpenFlow Switches
學年108
學期1
出版(發表)日期2019/11/23
作品名稱Enhanced Attack Blocking in IoT Environments: Engaging Honeypots and Machine Learning in SDN OpenFlow Switches
作品名稱(其他語言)
著者Po-Jen Chuang; Tzu-Chao Hung
單位
出版者
著錄名稱、卷期、頁數Journal of Applied Science and Engineering 23(1), p.163-173
摘要This paper introduces a new attack blocking mechanism to defend against malicious unknown
attacks in the Internet of Things (IoT) environments. The new mechanism starts by installing a
honeypot in each Software Defined Network OpenFlow switch to attract and collect suspicious traffic.
Upon detecting suspicious traffic, it will first store the traffic in the honeypot first, instead of
performing instant anomaly detection, to preserve the overall network speed and packets. The
mechanism then sends the collected attack traffic to the controller, to extract more appropriate features
by the machine learning practice and to ensure more accurate anomaly identification. After identifying
the attack type, it will add a proper defense rule in the flow table – a new entry – to block similar future
attacks. Experimental evaluation proves that the new mechanism is more advantageous than the
existing flow-based IDS mechanism. Major advantages include being able to detect and prevent
unknown attacks without blocking regular network traffic, achieve better capture rates than the
Intrusion Detection System (IDS) upon traffic-high or short packet attacks, and avoid potential packet
loss.
關鍵字Internet of Things (IoT);Software Defined Network (SDN);Intrusion Detection System (IDS);Flow Table;Honeypot;Machine Learning;Anomaly Detection;Distributed Denial of Services (DDoS)
語言英文
ISSN
期刊性質國內
收錄於ESCI;
產學合作
通訊作者
審稿制度
國別中華民國
公開徵稿
出版型式,電子版,紙本
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