Theophile Fozin Fonzin^1,2, Halilou Claude Bobo Hamadjida², Aurelle Tchagna Kouanou^2,3,*, Valery Monthe⁴, Anicet Brice Mezatio⁵, Michael Sone Ekonde⁶

Journal of Cyber Security, Vol.6, pp. 155-177, 2024, DOI:10.32604/jcs.2024.059265 - 09 January 2025

Abstract Cloud computing is a transformational paradigm involving the delivery of applications and services over the Internet, using access mechanisms through microprocessors, smartphones, etc. Latency time to prevent and detect modern and complex threats remains one of the major challenges. It is then necessary to think about an intrusion prevention system (IPS) design, making it possible to effectively meet the requirements of a cloud computing environment. From this analysis, the central question of the present study is to minimize the latency time for efficient threat prevention and detection in the cloud. To design this IPS design… More >

Boadu Nkrumah^1,*, Michal Asante¹, Gaddafi Adbdul-Salam¹, Wofa K. Adu-Gyamfi²

Journal of Cyber Security, Vol.6, pp. 131-153, 2024, DOI:10.32604/jcs.2024.053954 - 17 December 2024

Abstract The changing nature of malware poses a cybersecurity threat, resulting in significant financial losses each year. However, traditional antivirus tools for detecting malware based on signatures are ineffective against disguised variations as they have low levels of accuracy. This study introduces Data Efficient Image Transformer-Malware Classifier (DeiT-MC), a system for classifying malware that utilizes Data-Efficient Image Transformers. DeiT-MC treats malware samples as visual data and integrates a newly developed Hybrid GridBay Optimizer (HGBO) for hyperparameter optimization and better model performance under varying malware scenarios. With HGBO, DeiT-MC outperforms the state-of-the-art techniques with a strong accuracy More >

Carlos Merlano^*

Journal of Cyber Security, Vol.6, pp. 89-116, 2024, DOI:10.32604/jcs.2024.056164 - 06 December 2024

Abstract The constantly increasing degree and frequency of cyber threats require the emergence of flexible and intelligent approaches to systems’ protection. Despite the calls for the use of artificial intelligence (AI) and machine learning (ML) in strengthening cyber security, there needs to be more literature on an integrated view of the application areas, open issues or trends in AI and ML for cyber security. Based on 90 studies, in the following literature review, the author categorizes and systematically analyzes the current research field to fill this gap. The review evidences that, in contrast to rigid rule-based… More >

Imran Khan, Meenakshi Megavarnam^*

Journal of Cyber Security, Vol.6, pp. 117-130, 2024, DOI:10.32604/jcs.2024.048332 - 06 December 2024

Abstract A URL (Uniform Resource Locator) is used to locate a digital resource. With this URL, an attacker can perform a variety of attacks, which can lead to serious consequences for both individuals and organizations. Therefore, attackers create malicious URLs to gain access to an organization’s systems or sensitive information. It is crucial to secure individuals and organizations against these malicious URLs. A combination of machine learning and deep learning was used to predict malicious URLs. This research contributes significantly to the field of cybersecurity by proposing a model that seamlessly integrates the accuracy of machine More >

Kaveh Heidary^1,*, Venkata Atluri¹, John Bland²

Journal of Cyber Security, Vol.6, pp. 69-87, 2024, DOI:10.32604/jcs.2024.051196 - 28 August 2024

Abstract This paper investigates the impact of reducing feature-vector dimensionality on the performance of machine learning (ML) models. Dimensionality reduction and feature selection techniques can improve computational efficiency, accuracy, robustness, transparency, and interpretability of ML models. In high-dimensional data, where features outnumber training instances, redundant or irrelevant features introduce noise, hindering model generalization and accuracy. This study explores the effects of dimensionality reduction methods on binary classifier performance using network traffic data for cybersecurity applications. The paper examines how dimensionality reduction techniques influence classifier operation and performance across diverse performance metrics for seven ML models. Four… More >

Jian Yu^1,2,*, Gaofeng Yu³, Xiangmei Xiao^1,2, Zhixing Lin^1,2

Journal of Cyber Security, Vol.6, pp. 41-68, 2024, DOI:10.32604/jcs.2024.049658 - 04 June 2024

Abstract With the rapid advancement of the Internet, network attack methods are constantly evolving and adapting. To better identify the network attack behavior, a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms. First, the algorithm designated the cluster set as vacant, with the introduction of a new object. Subsequently, a new cluster based on the given object was constructed. The dissimilarities between it and each existing cluster were calculated using a defined difference measure. The minimum dissimilarity was selected. Through comparing the proposed algorithm with the traditional Back More >

Lindah Sawe^*, Joyce Gikandi, John Kamau, David Njuguna

Journal of Cyber Security, Vol.6, pp. 25-39, 2024, DOI:10.32604/jcs.2023.045859 - 11 January 2024

Abstract Phishing emails have experienced a rapid surge in cyber threats globally, especially following the emergence of the COVID-19 pandemic. This form of attack has led to substantial financial losses for numerous organizations. Although various models have been constructed to differentiate legitimate emails from phishing attempts, attackers continuously employ novel strategies to manipulate their targets into falling victim to their schemes. This form of attack has led to substantial financial losses for numerous organizations. While efforts are ongoing to create phishing detection models, their current level of accuracy and speed in identifying phishing emails is less… More >

Adil Hussain¹, Amna Khatoon^2,*, Ayesha Aslam², Tariq¹, Muhammad Asif Khosa¹

Journal of Cyber Security, Vol.6, pp. 1-23, 2024, DOI:10.32604/jcs.2023.046915 - 03 January 2024

Abstract The importance of cybersecurity in contemporary society cannot be inflated, given the substantial impact of networks on various aspects of daily life. Traditional cybersecurity measures, such as anti-virus software and firewalls, safeguard networks against potential threats. In network security, using Intrusion Detection Systems (IDSs) is vital for effectively monitoring the various software and hardware components inside a given network. However, they may encounter difficulties when it comes to detecting solitary attacks. Machine Learning (ML) models are implemented in intrusion detection widely because of the high accuracy. The present work aims to assess the performance of More >

Shoaib Khan, Bilal Khan, Saifullah Jan^*, Subhan Ullah, Aiman

Journal of Cyber Security, Vol.5, pp. 47-66, 2023, DOI:10.32604/jcs.2023.045579 - 28 December 2023

Abstract Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the More >

Yih Bing Chu^*, Zhi Min Lim, Bryan Keane, Ping Hao Kong, Ahmed Rafat Elkilany, Osama Hisham Abusetta

Journal of Cyber Security, Vol.5, pp. 33-46, 2023, DOI:10.32604/jcs.2023.045422 - 03 November 2023

Abstract The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud, particularly in credit card transactions. Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising from potentially fraudulent activities. However, a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations. While sampling techniques can significantly reduce computational time, the resulting dataset relies on generated data and the accuracy of the pre-processing… More >