Title :
A lightweight anomaly mining algorithm in the Internet of Things
Author :
Yanbing Liu ; Qin Wu
Author_Institution :
Chongqing Univ. of Posts & Telecommun., Chongqing, China
Abstract :
The security of Internet of Things (IoT) has already become a thorny problem because of opening deployment and limited resources. Thus, as the essential part of intrusion detection anomaly mining gets more and more attention. However, complexity of algorithm is the vital issue due to the specialty of IoT. Meanwhile, traditional methods with Euclidean distance may cause misjudgment at some extent. So this paper proposes a lightweight anomaly mining algorithm which employ Jaccard coefficient firstly as the judging criterion instead of Euclidean distance. The experiment verifies the availability of proposed algorithm.
Keywords :
Internet of Things; data mining; security of data; Euclidean distance; Internet of Things; IoT security; Jaccard coefficient; intrusion detection; judging criterion; lightweight anomaly mining algorithm; Complexity theory; Euclidean distance; Internet of Things; Sensors; Vectors; Wireless communication; Wireless sensor networks; Internet of things; anomaly mining; intrusion detection;
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-3278-8
DOI :
10.1109/ICSESS.2014.6933768