Ensemble voting based intrusion detection technique using negative selection algorithm

Kuldeep Singh, L. Kaur, R. Maini
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Abstract

This paper proposes an Intrusion Detection Technique (IDT) using an Artificial Immune System (AIS) based on Negative Selection Algorithm (NSA) to distinguish the self and non-self (intrusion) in computer networks. The novelties of the work are 1) use of Stacked Autoencoders (SAEs) and random forest for dimensionality reduction of data, 2) use of AIS to exploit its feature like self-learning, distributed, self-adaption, self-regulation with self and non-self-distinguishing capability, 3) implementation of two algorithms i.e., NSA based on Cosine distance (NSA_CD) and NSA based on Pearson Distance (NSA_PD) to explore their intrusion detection capabilities, and iv) development of a new ensemble voting based Intrusion Detection Technique (IDT-NSAEV) to detect and test the anomalies in the system. The proposed IDT-NSAEV technique combines the power of NSA_CD, NSA_PD and NSA based on Euclidean distance (NSA_ED) algorithms to enhance the detection rate by reducing the false alarm rate. The performance of the proposed technique is tested on standard benchmark NSL-KDD dataset and the results are compared with the state-of-the-art techniques. The results are in the favour of the proposed technique.
基于集合投票的负选择算法入侵检测技术
提出了一种基于负选择算法(NSA)的人工免疫系统(AIS)入侵检测技术(IDT),以区分计算机网络中的自我和非自我(入侵)。本研究的新颖之处在于:1)利用叠置自编码器(sae)和随机森林对数据进行降维;2)利用AIS的自学习、分布式、自适应、自调节等特征,具有自区分和非自区分能力;3)实现基于余弦距离的NSA (NSA_CD)和基于Pearson距离的NSA (NSA_PD)两种算法,探索其入侵检测能力。iv)开发一种新的基于集成投票的入侵检测技术(IDT-NSAEV)来检测和测试系统中的异常。本文提出的IDT-NSAEV技术结合了基于欧几里得距离(NSA_ED)算法的NSA_CD、NSA_PD和NSA的功能,通过降低虚警率来提高检测率。在标准基准NSL-KDD数据集上测试了所提出技术的性能,并将结果与最新技术进行了比较。结果支持所提出的技术。
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