A Hybrid Soft Computing Approach for Prediction of Cloud-Based Learning Management Systems Determinants

Mohammed, Yakubu Bala; Cavus, Nadire; Ya’u Gital, Abdulsalam; Bulama, Mohammed; Hassan, Abba

doi:10.1080/10447318.2023.2301264

Cited by 1 publication

(2 citation statements)

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“…Nowadays, organization's databases, online computers, smartphones, and other information systems which are created for the betterment of society are becoming more vulnerable to criminal activities. Crimes against computing systems typically involve targeting organizational data, personal computers, emails, servers, bank accounts, websites, and digital records of both public and private institutions [5,6]. These offenses are also referred to as "Online crimes, Computer crimes, Cybercrimes, Online crimes, Internet crimes, or Network crimes" [7].…”

Section: Digital Crimes: Issues and Challengesmentioning

confidence: 99%

“…The shortage of cybersecurity experts is a major obstacle faced by companies, as a sufficient number of these specialists are essential for designing a secure digital environment [2]. However, studies have shown that "AI-based methods offer significant enhancements in detecting phishing and ransomware attacks compared to non-AI methods" [6]. For instance, by leveraging machine learning algorithms, AI can analyse vast datasets and identify subtle patterns indicative of malicious activity.…”

Section: Cybersecurity and Cybercrimesmentioning

confidence: 99%

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An Intelligence-Based Cybersecurity Approach: A Review

Mohammed,

Badara,

Dan’azumi

2024

J. Intell. Commun.

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Nowadays, cybersecurity stands out as a prominent topic frequently discussed by companies aiming to safeguard their data from hacking attempts. The rise of cyberspace has fuelled the expansion of electronic systems, creating a virtual digital realm that links computers and smartphones within the Internet of Things framework. Thus, the purpose of this study is to present the progress made thus far in applying AI-based intrusion detection systems (IDSs) to address cybercrimes, demonstrating their efficacy in detecting and preventing cyberattacks. The review utilized 4 scholarly databases; ScienceDirect, IEEE-Explore, Web of science, and Springer. 437 studies were extracted from the 4 chosen databases out of which only 54 studies were found to be relevant, thus included. The review found AI-based intrusion detection systems (IDSs) to be more robust and flexible compared to other conventional IDSs. Interestingly, the study results highlight how AI-based intrusion detection systems such as; ANN-based intrusion detection systems, agent-based intrusion detection systems, and Genetic-fuzzy intrusion detection systems, and other machine learning detection systems can be used to assist security experts in analyzing, designing, and developing security frameworks for combating cybercrimes. Lastly, the study proposed some areas for future studies.

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Section: Digital Crimes: Issues and Challengesmentioning

confidence: 99%

Section: Cybersecurity and Cybercrimesmentioning

confidence: 99%