Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism
During 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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/684952 |
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| _version_ | 1849703507137921024 |
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| author | Ramsha Rizwan Farrukh Aslam Khan Haider Abbas Sajjad Hussain Chauhdary |
| author_facet | Ramsha Rizwan Farrukh Aslam Khan Haider Abbas Sajjad Hussain Chauhdary |
| author_sort | Ramsha Rizwan |
| collection | DOAJ |
| description | During 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. |
| format | Article |
| id | doaj-art-10e07a43ae024b988665bbb07270614e |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-10e07a43ae024b988665bbb07270614e2025-08-20T03:17:14ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/684952684952Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired MechanismRamsha Rizwan0Farrukh Aslam Khan1Haider Abbas2Sajjad Hussain Chauhdary3 National University of Computer and Emerging Sciences, A. K. Brohi Road, H-11/4, Islamabad 44000, Pakistan King Saud University, Riyadh 11653, Saudi Arabia National University of Sciences & Technology, Islamabad 44000, Pakistan Advance Technology R&D Center, LSIS Co., Ltd., Anyang 431-080, Republic of KoreaDuring 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.https://doi.org/10.1155/2015/684952 |
| spellingShingle | Ramsha Rizwan Farrukh Aslam Khan Haider Abbas Sajjad Hussain Chauhdary Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism International Journal of Distributed Sensor Networks |
| title | Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism |
| title_full | Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism |
| title_fullStr | Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism |
| title_full_unstemmed | Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism |
| title_short | Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism |
| title_sort | anomaly detection in wireless sensor networks using immune based bioinspired mechanism |
| url | https://doi.org/10.1155/2015/684952 |
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