Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms

Chabathula, Krupa Joel; C.D, Jaidhar; Kumara, Ajay

doi:10.1109/icscn.2015.7219853

Cited by 19 publications

(9 citation statements)

References 8 publications

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“…In this system, the detection stage relies on the major principal component score and the minor principal component score. In addition, PCA has been used in intrusion detection techniques based on payload modeling, statistical modeling, data mining and machine learning [56][57][58].…”

Section: Idss: Detection Techniquesmentioning

confidence: 99%

Intrusion detection systems for IoT-based smart environments: a survey

Elrawy¹,

2018

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One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

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Section: Idss: Detection Techniquesmentioning

confidence: 99%

Intrusion detection systems for IoT-based smart environments: a survey

Elrawy¹,

2018

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“…Chabathula et. al in [37] have made an experiment on the effect of dimensionality reduction with PCA on different machine learning algorithms like…”

Section: Existing Approaches To Idsmentioning

confidence: 99%

Intrusion Detection Analytics: A Comprehensive Survey

Sameera¹,

Shashi²

2019

International Journal of Advanced Scientific Research and Management

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Cyber threat is growing on par with the advancements in the field of co mputer technology and information age which makes Intrusion detection Systems (IDSs) to get a lot of attention now a days. IDS is an evolv ing research area in the field of cyber security, which is aimed to detect cyber-intrusions. The authors have surveyed many research papers on IDS in the resent past and the essence of their survey is presented in this paper by keeping in thought of helping research scholars in the area of IDS. This paper aims at p resenting brief description of IDS and mach ine learning approach for its imp lementation. Though lot of literature survey on IDS exist, in this paper authors attempt to present a clear picture of IDS in all aspects through their extensive survey.

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“…PCA can also be used for feature reduction [31] and feature selection [32]. In addition, PCA can be combined with machine learning methods such as SVM [33], genetic algorithm [34] and naïve bayes [35]. Chen et al [36] using the Multi-Scale Principal Component Analysis (MSPCA) to identify the Denial of Service (DoS) attacks.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Ahsan¹,

Mashuri²,

Kuswanto³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling's T 2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T 2 based on PCA for IDS. KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T 2 based PCA will be compared with conventional T 2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling's T 2 based on PCA has excellent performance to detect anomaly in network. Compared to conventional T 2 control chart, the T 2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.

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Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms

Cited by 19 publications

References 8 publications

Intrusion detection systems for IoT-based smart environments: a survey

Intrusion detection systems for IoT-based smart environments: a survey

Intrusion Detection Analytics: A Comprehensive Survey

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

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