Guest Editors
Dr. Mobashar Rehman, Universiti Tunku Abdul Rahman, Malaysia.
Dr. Aamir Amin, Universiti Tunku Abdul Rahman, Malaysia.
Dr. Rehan Akbar, Universiti Tunku Abdul Rahman, Malaysia.
Dr. Manzoor Ahmed Hashmani, Universiti Teknologi PETRONAS, Malaysia.
Dr. Sohail Safdar, Ahlia University, Bahrain.
Summary
Advancement in technology is ensuring that people use smart devices excessively. This excessive use of such devices results in the generation and storage of a huge amount of data at the personal as well as organizational level. However, there are continuous threats to this data being stolen and misused owing to security breaches especially among Small and Medium Enterprises (SMEs) due to lack of awareness, lack of training, lack of expertise, and less financial resources. Unfortunately, majority of the security breaches among SMEs are caused by human mistakes or human behaviour thus making people as the most vulnerable component for cyber security hackers.
Therefore, the objective of this special issue is to investigate human and associated aspects that results in cyber security breaches.
Keywords
·Cybersecurity behavior in SMEs
·Personal factors
·Social factors
·Socio-cognitive factors
·Environmental factors
·Organizational factors
·Cyber hygiene
·Cybersecurity awareness
Published Papers
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Open Access
ARTICLE
Insider Threat Detection Based on NLP Word Embedding and Machine Learning
Mohd Anul Haq, Mohd Abdul Rahim Khan, Mohammed Alshehri
Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 619-635, 2022, DOI:10.32604/iasc.2022.021430
（This article belongs to this Special Issue:
Humans and Cyber Security Behaviour)
Abstract The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires state-of-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which are major concerns needing further…
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Open Access
ARTICLE
Machine Learning Approach for Improvement in Kitsune NID
Abdullah Alabdulatif, Syed Sajjad Hussain Rizvi
Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 827-840, 2022, DOI:10.32604/iasc.2022.021879
（This article belongs to this Special Issue:
Humans and Cyber Security Behaviour)
Abstract Network intrusion detection is the pressing need of every communication network. Many network intrusion detection systems (NIDS) have been proposed in the literature to cater to this need. In recent literature, plug-and-play NIDS, Kitsune, was proposed in 2018 and greatly appreciated in the literature. The Kitsune datasets were divided into 70% training set and 30% testing set for machine learning algorithms. Our previous study referred that the variants of the Tree algorithms such as Simple Tree, Medium Tree, Coarse Tree, RUS Boosted, and Bagged Tree have reported similar effectiveness but with slight variation inefficiency. To further extend this investigation, we…
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Open Access
ARTICLE
AI/ML in Security Orchestration, Automation and Response: Future Research Directions
Johnson Kinyua, Lawrence Awuah
Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 527-545, 2021, DOI:10.32604/iasc.2021.016240
（This article belongs to this Special Issue:
Humans and Cyber Security Behaviour)
Abstract Today’s cyber defense capabilities in many organizations consist of a diversity of tools, products, and solutions, which are very challenging for Security Operations Centre (SOC) teams to manage in current advanced and dynamic cyber threat environments. Security researchers and industry practitioners have proposed security orchestration, automation, and response (SOAR) solutions designed to integrate and automate the disparate security tasks, processes, and applications in response to security incidents to empower SOC teams. The next big step for cyber threat detection, mitigation, and prevention efforts is to leverage AI/ML in SOAR solutions. AI/ML will act as a force multiplier empowering SOC analysts…
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Open Access
ARTICLE
Assessing User’s Susceptibility and Awareness of Cybersecurity Threats
Maha M. Althobaiti
Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 167-177, 2021, DOI:10.32604/iasc.2021.016660
（This article belongs to this Special Issue:
Humans and Cyber Security Behaviour)
Abstract Cybersecurity threats, including those involving machine learning, malware, phishing, and cryptocurrency, have become more sophisticated. They target sensitive information and put institutions, governments, and individuals in a continual state of risk. In 2019, phishing attacks became one of the most common and dangerous cyber threats. Such attacks attempt to steal sensitive data, such as login and payment card details, from financial, social, and educational websites. Many universities have suffered data breaches, serving as a prime example of victims of attacks on educational websites. Owing to advances in phishing tactics, strategies, and technologies, the end-user is the main victim of an…
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