Cybersecurity Awareness: Investigating Students’ Susceptibility to Phishing Attacks for Sustainable Safe Email Usage in Academic Environment (A Case Study of a Nigerian Leading University)

Okokpujie, Kennedy; Kennedy, Chinyere G.; Nnodu, Kamsiyochukwu; Noma-Osaghae, Etinose

doi:10.18280/ijsdp.180127

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…After carrying out model training and validation, we evaluate the performance of the model using standard performance metrics such as accuracy, precision, recall, and F1-score (using Eqs. ( 1)-( 4)) [35,36]. 5, and it shows that, across the three models for the two experimental setups, LSTM achieve the best performance, particularly in experimental setup one, with an accuracy of 98.09%, precision of 98.14%, F1-score of 98.09%, and recall of 98.05%.…”

Section: Resultsmentioning

confidence: 90%

Development of a Malicious Network Traffic Intrusion Detection System Using Deep Learning

Isife,

Okokpujie,

Okokpujie

et al. 2023

IJSSE

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With the exponential surge in the number of internet-connected devices, the attack surface for potential cyber threats has correspondingly expanded. Such a landscape necessitates the evolution of intrusion detection systems to counter the increasingly sophisticated mechanisms employed by cyber attackers. Traditional machine learning methods, coupled with existing deep learning implementations, are observed to exhibit limited proficiency due to their reliance on outdated datasets. Their performance is further compromised by elevated false positive rates, decreased detection rates, and an inability to efficiently detect novel attacks. In an attempt to address these challenges, this study proposes a deep learning-based system specifically designed for the detection of malicious network traffic. Three distinct deep learning models were employed: Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). These models were trained using two contemporary benchmark intrusion detection datasets: the CICIDS 2017 and the Coburg Intrusion Detection Data Sets (CIDDS). A robust preprocessing procedure was conducted to merge these datasets based on common and essential features, creating a comprehensive dataset for model training. Two separate experimental setups were utilized to configure these models. Among the three models, the LSTM displayed superior performance in both experimental configurations. It achieved an accuracy of 98.09%, a precision of 98.14%, an F1-Score of 98.09%, a True Positive Rate (TPR) of 98.05%, a True Negative Rate (TNR) of 99.69%, a False Positive Rate (FPR) of 0.31%, and a False Negative Rate (FNR) of 1.95%.

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Section: Resultsmentioning

confidence: 90%

Development of a Malicious Network Traffic Intrusion Detection System Using Deep Learning

Isife,

Okokpujie,

Okokpujie

et al. 2023

IJSSE

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“…Another significant aspect of cybersecurity in Nigerian banks is the awareness and susceptibility of users to phishing attacks. Okokpujie et al (2023) conducted research aimed at investigating students' susceptibility to phishing attacks for sustainable safe email usage in an academic environment, which can be extrapolated to the banking sector. Their study reveals that a significant percentage of college students are susceptible to phishing attacks due to unawareness, underscoring the need for cybersecurity awareness in the banking sector.…”

Section: Review Of Specific Instances and Case Studies Where Cybersec...mentioning

confidence: 99%

Cybersecurity Dynamics in Nigerian Banking: Trends and Strategies Review

Oluwatosin Reis,

Johnson Sunday Oliha,

Femi Osasona

et al. 2024

Comput. sci. IT res. j.

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This paper provides an in-depth review of the cybersecurity dynamics within the Nigerian banking sector, emphasizing recent trends and strategic approaches to address emerging challenges. As a review paper, it synthesizes existing literature, reports, and case studies to offer a comprehensive understanding of the current cybersecurity landscape in Nigerian banks. The focus is on identifying the predominant cyber threats, analyzing the sector's response strategies, and evaluating the effectiveness of these measures in the context of Nigeria's unique socio-economic and regulatory environment. Our analysis reveals a notable escalation in cyber threats, particularly phishing, ransomware, and insider attacks, which have been intensified by the rapid digital transformation in banking services. The review identifies key factors contributing to these challenges, such as the increasing sophistication of cybercriminals, the digital literacy gap among customers, and the evolving nature of cyber threats. It also examines the strategic responses of Nigerian banks, including the adoption of advanced security technologies, enhanced staff training, and collaboration with government and international cybersecurity bodies.The paper concludes that Nigerian banks have made significant strides in fortifying their cybersecurity defenses. However, it also highlights the need for more robust regulatory frameworks, increased customer awareness initiatives, and a shift towards more integrated and proactive cybersecurity strategies. The findings of this review underscore the critical need for continuous evolution and investment in cybersecurity measures to effectively counter the dynamic and complex nature of cyber threats in the Nigerian banking sector. Keywords: Cybersecurity, Cybersecurity Dynamics, Nigerian Banking Sector, Digital Transformation, Cyber Threats, Phishing Attacks, Ransomware, Insider Threats, Regulatory Framework, Central Bank of Nigeria (CBN), Compliance Challenges, Security Technologies, Cybersecurity Awareness, Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology.

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