An empirical model in intrusion detection systems using principal component analysis and deep learning models. (5th June 2020)

• Record Type: Journal Article
• Authors: Rajadurai, Hariharan; Gandhi, Usha Devi
• Other Names: Srivastava Gautam guestEditor.; Hsu Ching‐Hsien (Robert) guestEditor.; Kumar Priyan Malarvizhi guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 3(2021)
• Page Start: 1111
• Page End: 1124
• Publication Date: 2020-06-05
• Subjects: deep learning -- logistic regression -- principal component analysis -- random forest -- support vector machine; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12342
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Data are a main resource of a computer system, which can be transmitted over network from source to destination. While transmitting, it faces lot of security issues such as virus, malware, infection, error, and data loss. The security issues are the attacks that have to be detected and eliminated in efficient way to guarantee the secure transmission. The attack detection rates of existing Intrusion Detection Systems (IDS) are low, because the number of unknown attacks are high when compared to the known attacks in the network. Thus, recent researchers focus more on evaluation of known attacks attributes, that will help in identification of the attacks. But the difficulty here is the nature of the IDS datasets. The difficulty in any IDS dataset is to, too many attributes, irrelevant and unstructured in nature. So analyzing such attributes leads to a time consuming process and that produces an inefficient result. This article presents a combined approach Principle Component Analysis and Deep learning (PCA‐DL) model to address above issues. The proposed PCA‐DL method has achieved the accuracy 92.6% on detecting the attacks correctly.