Open AccessResearch on Data Security Detection Algorithm in IoT Based on K-means
Author(s) -
Jing Zhu,
Lina Huo,
Mohd Dilshad Ansari,
Mohammad Asif Ikbal
Publication year - 2021
Publication title -
scalable computing. practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.192
H-Index - 18
ISSN - 1895-1767
DOI - 10.12694/scpe.v22i2.1880
Subject(s) - computer science , intrusion detection system , cluster analysis , data mining , internet of things , network security , the internet , wireless sensor network , computer network , computer security , artificial intelligence , world wide web
The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method is aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.