A Proposed Optimized and Efficient Intrusion Detection System for Wireless Sensor Network

In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion detection system (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw dataset for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle component analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. This optimal feature subset is used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

• Abdulaziz Alsadhan
• Naveed Khan

• Particle Swarm Optimization (PSO)
• Principle component analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Local Binary Pattern (LBP)
• Support Vector Machine (SVM)
• Multilayer Perceptron (MLP).

[1] Ahmad I, "Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques, "Ph.D. dissertation”, Dept., CIS, UTP., Tronoh, Perak, Malaysia, 2011.
[2] http://www.swarmintelligence.org/bibliography.php, (Accessed on 15th September, 2013).
[3] Leonardo N "Multilayer Perceptron Tutorial” School of Computing Staffordshire University Beaconside Staffordshire, 2005.
[4] http://iscx.ca/NSL-KDD/ (Accessed on 20th September, 2013).
[5] CAIDA: The Cooperative Association for Internet Data Analysis (online). Available: http://www.caida.org (1st April, 2011).
[6] Ahmad I, Abdullah A, and Alghamdi AS. "Application of Artificial Neural Network in Detection of Probing Attacks” IEEE Symposium on Industrial Electronics and Applications (ISIEA). Kuala Lumpur, Malaysia. pp. 557 – 562, 2009.
[7] Ahmad I, Abdullah AB, and Alghamdi AS "Artificial Neural Network Approaches Intrusion Detection: A Review” Telecommunications and Informatics conference. Istanbul, Turkey, pp. 200-205, 2009.
[8] Bace R, and Mell P. "Intrusion Detection Systems. National Institute of Standards and Technology (NIST) Special Publication. pp. 1-51, 2001.
[9] Tang P, Jiang R and Zhao M "Feature Selection and Design of Intrusion Detection System Based on k-Means and Triangle Area Support Vector Machine”. International Conference on Future Networks (ICFN), pp. 144-148, 2001.
[10] Zhang X "Intrusion Detection System Based on Feature Selection and Support Vector Machine”. IEEE conference on Communications and Networking China, pp. 1-5, 2006.
[11] Fox KL, Henning RR, Reed JH, Simonian RP "Information Systems Security” International conference on Computer Security, pp. 124-134, 1990.
[12] Khan L, Awad M, and Thuraisingham B. "A New Intrusion Detection System Using Support Vector Machines and Hierarchical Clustering” International Journal on Very Large Data Bases 16(4):507–521,2010.
[13] Mukherjee B, Heberlein LT, and Levitt KN "Network Intrusion Detection”, IEEE Network. pp. 26-41,1994.
[14] Pervez S, Ahmad I, Akram A, and Swati SU "A Comparative Analysis of Artificial Neural Network Technologies in Intrusion Detection Systems” WSEAS Transaction on Computers. pp. 175-180, 2007.
[15] Mike Meyers' "Managing and Troubleshooting Networks”, Second Edition, 2009.