Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a pr...

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Autores principales:	Abdullah Alharbi, Adil Hussain Seh, Wael Alosaimi, Hashem Alyami, Alka Agrawal, Rajeev Kumar, Raees Ahmad Khan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning cybersecurity hesitant fuzzy logic AHP-TOPSIS idealness assessment IDS Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/681dc52c9b5d4ce69fb5f2b667788603
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spelling	oai:doaj.org-article:681dc52c9b5d4ce69fb5f2b6677886032021-11-25T19:00:16ZAnalyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems10.3390/su1322123372071-1050https://doaj.org/article/681dc52c9b5d4ce69fb5f2b6677886032021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12337https://doaj.org/toc/2071-1050Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a proactive security mechanism in the cybersecurity domain. Cybersecurity ensures the real time protection of information, information systems, and networks from intruders. Several security and privacy reports have cited that there has been a rapid increase in both the frequency and the number of cybersecurity breaches in the last decade. Information security has been compromised by intruders at an alarming rate. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber-security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address the current cybersecurity issues and challenges. However, in this research endeavor, our objective is to make an idealness assessment of machine learning-based intrusion detection systems (IDS) under the hesitant fuzzy (HF) conditions, using a multi-criteria decision making (MCDM)-based analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal-solutions (TOPSIS). Hesitant fuzzy sets are useful for addressing decision-making situations in which experts must overcome the reluctance to make a conclusion. The proposed research project would assist the machine learning practitioners and cybersecurity specialists in identifying, selecting, and prioritizing cybersecurity-related attributes for intrusion detection systems, and build more ideal and effective intrusion detection systems.Abdullah AlharbiAdil Hussain SehWael AlosaimiHashem AlyamiAlka AgrawalRajeev KumarRaees Ahmad KhanMDPI AGarticlemachine learningcybersecurityhesitant fuzzy logicAHP-TOPSISidealness assessmentIDSEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12337, p 12337 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning cybersecurity hesitant fuzzy logic AHP-TOPSIS idealness assessment IDS Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	machine learning cybersecurity hesitant fuzzy logic AHP-TOPSIS idealness assessment IDS Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Abdullah Alharbi Adil Hussain Seh Wael Alosaimi Hashem Alyami Alka Agrawal Rajeev Kumar Raees Ahmad Khan Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems
description	Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a proactive security mechanism in the cybersecurity domain. Cybersecurity ensures the real time protection of information, information systems, and networks from intruders. Several security and privacy reports have cited that there has been a rapid increase in both the frequency and the number of cybersecurity breaches in the last decade. Information security has been compromised by intruders at an alarming rate. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber-security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address the current cybersecurity issues and challenges. However, in this research endeavor, our objective is to make an idealness assessment of machine learning-based intrusion detection systems (IDS) under the hesitant fuzzy (HF) conditions, using a multi-criteria decision making (MCDM)-based analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal-solutions (TOPSIS). Hesitant fuzzy sets are useful for addressing decision-making situations in which experts must overcome the reluctance to make a conclusion. The proposed research project would assist the machine learning practitioners and cybersecurity specialists in identifying, selecting, and prioritizing cybersecurity-related attributes for intrusion detection systems, and build more ideal and effective intrusion detection systems.
format	article
author	Abdullah Alharbi Adil Hussain Seh Wael Alosaimi Hashem Alyami Alka Agrawal Rajeev Kumar Raees Ahmad Khan
author_facet	Abdullah Alharbi Adil Hussain Seh Wael Alosaimi Hashem Alyami Alka Agrawal Rajeev Kumar Raees Ahmad Khan
author_sort	Abdullah Alharbi
title	Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems
title_short	Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems
title_full	Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems
title_fullStr	Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems
title_full_unstemmed	Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems
title_sort	analyzing the impact of cyber security related attributes for intrusion detection systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/681dc52c9b5d4ce69fb5f2b667788603
work_keys_str_mv	AT abdullahalharbi analyzingtheimpactofcybersecurityrelatedattributesforintrusiondetectionsystems AT adilhussainseh analyzingtheimpactofcybersecurityrelatedattributesforintrusiondetectionsystems AT waelalosaimi analyzingtheimpactofcybersecurityrelatedattributesforintrusiondetectionsystems AT hashemalyami analyzingtheimpactofcybersecurityrelatedattributesforintrusiondetectionsystems AT alkaagrawal analyzingtheimpactofcybersecurityrelatedattributesforintrusiondetectionsystems AT rajeevkumar analyzingtheimpactofcybersecurityrelatedattributesforintrusiondetectionsystems AT raeesahmadkhan analyzingtheimpactofcybersecurityrelatedattributesforintrusiondetectionsystems
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Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

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