Detection of Cyber-attacks with Zone Dividing and PCA

Morita, Takahito; Yogo, Shuichi; Koike, Masato; Hamaguchi, Takashi; Jung, Sun Young; Koshijima, Ichiro; Hashimoto, Y.

doi:10.1016/j.procs.2013.09.154

Cited by 15 publications

(6 citation statements)

References 2 publications

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“…PCA is an effective technique in data compression and feature extraction. In Feature selection process information space is converted into a feature space, which has a lower dimension [11,15,16].…”

Section: Feature Extraction 241 Principal Component Analysis (Pca)mentioning

confidence: 99%

Machine Learning for Cyber Threat Detection

More¹

2020

IJATCSE

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In recent years, anomaly detection has more importance in networking domains. Machine learning is very effective in anomaly detection to improve accuracy in classification. To ensure automated and effective cyber threat detection, analysis of security logs from the dataset is required. Usage of the internet increases cyber-attacks and at present, the cyber security situation is pessimistic. Use of social media and networking has increased in daily life, nowadays all are learning and working by using the internet but on the other hand, it becomes serious security threats problem. Thus the development of the Intrusion Detection System (IDS) is essential to provide an extra level of security. Cyber threat is an important issue faced by all organizations. However, it has difficult to use machine learning algorithms for threat detection analysis, due to huge number of negative threats detection, especially in the case of large scale environments. In this paper, we surveyed clustering and classification of machine learning algorithms. Using machine learning algorithms cyber threats and false detection rates are reduced which increases the performance of the system.

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Section: Feature Extraction 241 Principal Component Analysis (Pca)mentioning

confidence: 99%

Machine Learning for Cyber Threat Detection

More¹

2020

IJATCSE

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“…Machine learning algorithms are used to design such a detection system to detect any concealed cyber‐attacks. Morita et al () have proposed an automatic detection system based on machine learning techniques. In order to carry out the experiment, a simple plant was considered and calibrated in which the hot water is circulated between two tanks that vary in heights.…”

Section: Applications Of Machine Learning In Cybersecuritymentioning

confidence: 99%

Machine learning in cybersecurity: A review

Handa

Sharma

Shukla

2019

WIREs Data Min & Knowl

105

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Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty. Examples include malware analysis, especially for zero‐day malware detection, threat analysis, anomaly based intrusion detection of prevalent attacks on critical infrastructures, and many others. Due to the ineffectiveness of signature‐based methods in detecting zero day attacks or even slight variants of known attacks, machine learning‐based detection is being used by researchers in many cybersecurity products. In this review, we discuss several areas of cybersecurity where machine learning is used as a tool. We also provide a few glimpses of adversarial attacks on machine learning algorithms to manipulate training and test data of classifiers, to render such tools ineffective. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > Classification Application Areas > Data Mining Software Tools

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“…It introduced a qualitative method on the anomaly analysis through the information in other normal zones. Then, this team proposed a quantitative method that was based on principle component analysis (PCA) to detect the anomalies on their testbed [10]. Mechtri et al [11] used the PCA algorithm to reduce data dimension in their anomaly detection method for network communications.…”

Section: A Security Protection For Field Zonesmentioning

confidence: 99%

A Software-Defined Security Approach for Securing Field Zones in Industrial Control Systems

2019

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Industrial control systems (ICSs) are facing increasingly severe security threats. Zone isolation, a commonly adopted idea for stopping attack propagation in general information systems, has been investigated for ICS security protection. It is usually implemented through perimeter security techniques. However, anomaly states of the physical processes in a compromised field zone may spread into other zones through the inter-zone information interaction. Due to the coupling of the physical processes between different zones, it is difficult to prevent the propagation of attack impact in ICSs. In this paper, a softwaredefined security (SDSec) approach is presented to address this problem. It consists of a hybrid anomaly detection module and a multi-level security response module, both of which work together to secure the ICS field zones. The hybrid anomaly detection module inspects anomaly behaviors from the perspectives of network communications and physical process states. The multi-level security response module helps prevent unapproved packets from communications, thus isolating any compromised zone. It also generates attack mitigation strategies to secure physical processes. Hardware-in-the-loop simulations are conducted to demonstrate the effectiveness of the presented approach.

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Detection of Cyber-attacks with Zone Dividing and PCA

Cited by 15 publications

References 2 publications

Machine Learning for Cyber Threat Detection

Machine Learning for Cyber Threat Detection

Machine learning in cybersecurity: A review

A Software-Defined Security Approach for Securing Field Zones in Industrial Control Systems

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