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All Journal Ceddi Journal of Information System and Technology (JST)
Ismaila Idris
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Computer Science & IT Decision Sciences, Operations Research & Management

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Evaluating Deep Learning Models for Website Phishing Attack Detection: A Comparative Analysis Raji Egigogo, Abdullahi; Ismaila Idris; Olalere, Morufu; Opeyemi Aderiike, Abisoye
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 2 (2024): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i2.100

Abstract

Phishing attacks remain a significant security threat in cyberspace, targeting individuals and businesses to steal confidential information. Traditional detection methods often struggle to identify newly created or altered phishing sites, highlighting the need for more adaptive solutions. This study evaluates the performance of various deep learning (DL) models for detecting online phishing attacks. A comparative analysis of single and hybrid DL models, including CNN, LSTM, BiGRU, and their combinations, is conducted. The evaluation is based on metrics such as accuracy, precision, recall, and F1-score, derived from 17 peer-reviewed publications published between 2019 and 2024. Results indicate that hybrid models, particularly ODAE-WPDC, exhibit superior performance, achieving accuracy rates of up to 99.28% and robust results across all metrics. Single models, such as CNN and BiGRU, also demonstrate strong performance, with accuracy ranging from 97% to 99.5%. This research underscores the efficacy of deep learning architectures in phishing detection and offers practical guidance for selecting optimal models based on specific requirements.
A Review of Deep Belief Networks in Intrusion Detection Systems: Applications, Optimization Techniques, and Dataset Utilization Sule Aishat A.; Alhassan John K.; Ismaila Idris; Alabi Isiaq O.; Subairu Sikiru O.
Ceddi Journal of Information System and Technology (JST) Vol. 4 No. 1 (2025): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v4i1.114

Abstract

As reliance on the Internet and interconnected systems for essential services continues to grow, the need for strong cybersecurity defenses has become more pressing. Intrusion Detection Systems (IDS) are crucial in safeguarding these digital infrastructures. This paper investigates how Deep Belief Networks (DBNs) can enhance IDS capabilities, particularly in identifying advanced and dynamic threats such as Distributed Denial of Service (DDoS) attacks, SQL injections, and zero-day vulnerabilities. By reviewing recent research, we explore how DBNs have been applied in IDS contexts, examine optimization methods like layer-wise pre-training and dropout regularization that contribute to better detection performance, and evaluate commonly used benchmark datasets including UNSW-NB15, NSL-KDD, and CSE-CIC-IDS2018. This study compiles empirical evidence to assess DBNs' performance across varied network conditions and traffic types. Findings suggest that DBNs are effective in learning complex data patterns and improving the detection of anomalies. Nonetheless, challenges such as interpretability, high computational requirements, and the limitations of existing datasets continue to hinder widespread adoption. This work adds to the ongoing cybersecurity discourse by outlining major developments, constraints, and future directions for DBN-powered IDS. It ends by proposing strategic improvements, including the development of more efficient models, broader dataset coverage, and real-time, adaptive integration to support smarter and more responsive IDS solutions.
Co-Authors Alabi Isiaq O. Alhassan John K. Olalere, Morufu Opeyemi Aderiike, Abisoye Raji Egigogo, Abdullahi Subairu Sikiru O. Sule Aishat A.