Paper Info
Title
Anomaly detection in Wireless Sensor Networks using immune-based bioinspired mechanism
Abstract
AbstractDuring the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. For this purpose, we implement the enhanced NSA and make a detector set that holds anomalous packets only. Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. In this way, the number of wormholes, packets delayed, and packets dropped are calculated and identified. Simulations are performed on a large dataset and the results show high accuracy of the proposed algorithm in detecting anomalies. The proposed NSA is also compared with Clonal Selection Algorithm (CSA) for the same dataset. The results show significant improvement of the proposed NSA over CSA in most of the cases.
Year
DOI
Venue
2015
10.1155/2015/684952
Periodicals
Field
DocType
Volume
Particle swarm optimization,Ant colony optimization algorithms,Anomaly detection,Data mining,Artificial immune system,Computer science,Network packet,Clonal selection algorithm,Wireless sensor network,Genetic algorithm
Journal
2015
Issue
ISSN
Citations 
1
1550-1329
3
PageRank 
References 
Authors
0.41
23
4
Name
Order
Citations
PageRank
ramsha rizwan130.41
Farrukh Aslam Khan238834.17
Haider Abbas3252.92
Sajjad Hussain Chauhdary410613.93