scholarly journals Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ramsha Rizwan ◽  
Farrukh Aslam Khan ◽  
Haider Abbas ◽  
Sajjad Hussain Chauhdary
Keyword(s):  
Clonal Selection ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Biologically Inspired ◽  
Data Packets ◽  
Negative Selection Algorithm ◽  
Bee Colony ◽  
Anomalous Data ◽  
Network Intrusion

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.

scholarly journals Replica Node Detection Using Enhanced Single Hop Detection with Clonal Selection Algorithm in Mobile Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
L. S. Sindhuja ◽  
G. Padmavathi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clonal Selection ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Mobile Wireless ◽  
Mobile Wireless Sensor Networks ◽  
Detection Ratio ◽  
Mobile Wireless Sensor

Security of Mobile Wireless Sensor Networks is a vital challenge as the sensor nodes are deployed in unattended environment and they are prone to various attacks. One among them is the node replication attack. In this, the physically insecure nodes are acquired by the adversary to clone them by having the same identity of the captured node, and the adversary deploys an unpredictable number of replicas throughout the network. Hence replica node detection is an important challenge in Mobile Wireless Sensor Networks. Various replica node detection techniques have been proposed to detect these replica nodes. These methods incur control overheads and the detection accuracy is low when the replica is selected as a witness node. This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD) method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. The advantages of the proposed method include (i) increase in the detection ratio, (ii) decrease in the control overhead, and (iii) increase in throughput. The performance of the proposed work is measured using detection ratio, false detection ratio, packet delivery ratio, average delay, control overheads, and throughput. The implementation is done using ns-2 to exhibit the actuality of the proposed work.

scholarly journals A Clone Selection Based Real-Valued Negative Selection Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Negative Selection ◽  
Immune Cell ◽  
Clonal Selection ◽  
Space Distribution ◽  
Small Radius ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Immune Cell Population ◽  
Negative Selection Algorithm ◽  
Selection Algorithms

Excessive detectors, high time complexity, and loopholes are main problems which current negative selection algorithms have face and greatly limit the practical applications of negative selection algorithms. This paper proposes a real-valued negative selection algorithm based on clonal selection. Firstly, the algorithm analyzes the space distribution of the self set and gets the set of outlier selves and several classification clusters. Then, the algorithm considers centers of clusters as antigens, randomly generates initial immune cell population in the qualified range, and executes the clonal selection algorithm. Afterwards, the algorithm changes the limited range to continue the iteration until the non-self space coverage rate meets expectations. After the algorithm terminates, mature detector set and boundary self set are obtained. The main contributions lie in (1) introducing the clonal selection algorithm and randomly generating candidate detectors within the stratified limited ranges based on clustering centers of self set; generating big-radius candidate detectors first and making them cover space far from selves, which reduces the number of detectors; then generating small-radius candidate detectors and making them gradually cover boundary space between selves and non-selves, which reduces the number of holes; (2) distinguishing selves and dividing them into outlier selves, boundary selves, and internal selves, which can adapt to the interference of noise data from selves; (3) for anomaly detection, using mature detector set and boundary self set to test at the same time, which can effectively improve the detection rate and reduce the false alarm rate. Theoretical analysis and experimental results show that the algorithm has better time efficiency and detector generation quality according to classic negative selection algorithms.

scholarly journals A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tianliang Lu ◽  
Lu Zhang ◽  
Yixian Fu
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Clonal Selection ◽  
Detection System ◽  
Detection Algorithm ◽  
Machine Language ◽  
Detection Accuracy ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Negative Selection Algorithm

Shellcodes are machine language codes injected into target programs in the form of network packets or malformed files. Shellcodes can trigger buffer overflow vulnerability and execute malicious instructions. Signature matching technology used by antivirus software or intrusion detection system has low detection rate for unknown or polymorphic shellcodes; to solve such problem, an immune-inspired shellcode detection algorithm was proposed, named ISDA. Static analysis and dynamic analysis were both applied. The shellcodes were disassembled to assembly instructions during static analysis and, for dynamic analysis, the API function sequences of shellcodes were obtained by simulation execution to get the behavioral features of polymorphic shellcodes. The extracted features of shellcodes were encoded to antigens based on n-gram model. Immature detectors become mature after immune tolerance based on negative selection algorithm. To improve nonself space coverage rate, the immune detectors were encoded to hyperellipsoids. To generate better antibody offspring, the detectors were optimized through clonal selection algorithm with genetic mutation. Finally, shellcode samples were collected and tested, and result shows that the proposed method has higher detection accuracy for both nonencoded and polymorphic shellcodes.

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