Name
Title | ||
|---|---|---|
A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept. |
Abstract | ||
|---|---|---|
•Adaptive SVM classifier and RRF algorithm are effectively utilized to classify and to reduce the extra features within the sensor nodes.•High-Level Security Mechanism is employed to perform secured packet transmission.•Comparison is made with prior methods to show the effectiveness of the proposed work.•Achieves better detection rate 0.5% and improved prediction accuracy, when compared with prior works. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.suscom.2019.06.002 | Sustainable Computing: Informatics and Systems |
Keywords | Field | DocType |
Wireless sensor network (WSN),Intrusion detection system (IDS),Security,Chicken swarm optimization (CSO),Rotated random forest (RRF),Support vector machine (SVM),Clustering,High–level security | Data mining,Computer science,Support vector machine,Supervised learning,Acknowledgement,Cluster analysis,Wireless sensor network,Intrusion detection system,Python (programming language),Scalability | Journal |
Volume | ISSN | Citations |
23 | 2210-5379 | 2 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gautam M. Borkar | 1 | 2 | 0.36 |
| Leena H. Patil | 2 | 2 | 0.36 |
| Dilip Dalgade | 3 | 2 | 0.36 |
| Ankush Hutke | 4 | 2 | 0.36 |