Name
Title | ||
|---|---|---|
An intrusion detection method for internet of things based on suppressed fuzzy clustering |
Abstract | ||
|---|---|---|
In order to improve the effectiveness of intrusion detection, an intrusion detection method of the Internet of Things (IoT) is proposed by suppressed fuzzy clustering (SFC) algorithm and principal component analysis (PCA) algorithm. In this method, the data are classified into high-risk data and low-risk data at first, which are detected by high frequency and low frequency, respectively. At the same time, the self-adjustment of the detection frequency is carried out according to the suppressed fuzzy clustering algorithm and the principal component analysis algorithm. Finally, the key factors influencing the algorithm are analyzed deeply by simulation experiment. The results shows that, compared to traditional method, this method has better adaptability. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s13638-018-1128-z | EURASIP Journal on Wireless Communications and Networking |
Keywords | Field | DocType |
Internet of things,Intrusion detection,Suppressed fuzzy clustering algorithm,Principal component analysis algorithm | Adaptability,Fuzzy clustering,Data mining,Computer science,Internet of Things,Real-time computing,Intrusion detection system,Principal component analysis | Journal |
Volume | Issue | ISSN |
2018 | 1 | 1687-1499 |
Citations | PageRank | References |
3 | 0.36 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Liu | 1 | 14 | 3.54 |
| Bing Xu | 2 | 29 | 8.16 |
| Xiao-Ping Zhang | 3 | 6 | 2.79 |
| Xian-Jun Wu | 4 | 17 | 3.44 |