Open Access

ARTICLE

Analyzing Human Trafficking Networks Using Graph-Based Visualization and ARIMA Time Series Forecasting

Journal of Cyber Security, Vol.7, pp. 135-163, 2025, DOI:10.32604/jcs.2025.064019 - 18 June 2025
Abstract In a world driven by unwavering moral principles rooted in ethics, the widespread exploitation of human beings stands universally condemned as abhorrent and intolerable. Traditional methods employed to identify, prevent, and seek justice for human trafficking have demonstrated limited effectiveness, leaving us confronted with harrowing instances of innocent children robbed of their childhood, women enduring unspeakable humiliation and sexual exploitation, and men trapped in servitude by unscrupulous oppressors on foreign shores. This paper focuses on human trafficking and introduces intelligent technologies including graph database solutions for deciphering unstructured relationships and entity nodes, enabling the comprehensive More >

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29

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ARTICLE

Phishing Forensics: A Systematic Approach to Analyzing Mobile and Social Media Fraud

Journal of Cyber Security, Vol.7, pp. 109-134, 2025, DOI:10.32604/jcs.2025.064429 - 30 May 2025
Abstract This paper explores the methodologies employed in the study of mobile and social media phishing, aiming to enhance the understanding of these evolving threats and develop robust countermeasures. By synthesizing existing research, we identify key approaches, including surveys, controlled experiments, data mining, and machine learning, to gather and analyze data on phishing tactics. These methods enable us to uncover patterns in attacker behavior, pinpoint vulnerabilities in mobile and social platforms, and evaluate the effectiveness of current detection and prevention strategies. Our findings highlight the growing sophistication of phishing techniques, such as social engineering and deceptive More >

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134

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ARTICLE

A Conceptual Framework for Cybersecurity Awareness

Journal of Cyber Security, Vol.7, pp. 79-108, 2025, DOI:10.32604/jcs.2025.059712 - 20 May 2025
Abstract Financial support, government support, cyber hygiene, and ongoing education and training as well as parental guidance and supervision are all essential components of cybersecurity awareness (CSA) identified in this study among the youth. It’s critical to realize that adequate funding is needed to effectively increase CSA, particularly among South African youth. Previous studies have demonstrated several ways to address inadequate CSA by utilizing various cybersecurity frameworks, ideas, and models. To increase CSA, this literature review seeks to emphasize the significance of integrating cybersecurity education throughout the entire school curriculum. This paper identified ethical issues, protection… More >

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267

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ARTICLE

Detecting Ransomware Using a Hybrid Detection Scheme

Journal of Cyber Security, Vol.7, pp. 71-78, 2025, DOI:10.32604/jcs.2025.063711 - 14 May 2025
Abstract Ransomware is a variant of malicious software that aims to encrypt data or whole systems to lock out the intended users. The attackers then demand a ransom for the decryption key to allow the intended users access to their data or system again. Ransomware attacks have the potential to be used against industries like healthcare and finance, as well as against the public sector, have threatened and forced the operations of key infrastructure used by millions to cease, and extorted millions and millions of dollars from victims. Automated methods have been designed and implemented to More >

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200

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ARTICLE

Improving Security-Sensitive Deep Learning Models through Adversarial Training and Hybrid Defense Mechanisms

Journal of Cyber Security, Vol.7, pp. 45-69, 2025, DOI:10.32604/jcs.2025.063606 - 08 May 2025
Abstract Deep learning models have achieved remarkable success in healthcare, finance, and autonomous systems, yet their security vulnerabilities to adversarial attacks remain a critical challenge. This paper presents a novel dual-phase defense framework that combines progressive adversarial training with dynamic runtime protection to address evolving threats. Our approach introduces three key innovations: multi-stage adversarial training with TRADES (Tradeoff-inspired Adversarial Defense via Surrogate-loss minimization) loss that progressively scales perturbation strength, maintaining 85.10% clean accuracy on CIFAR-10 (Canadian Institute for Advanced Research 10-class dataset) while improving robustness; a hybrid runtime defense integrating feature manipulation, statistical anomaly detection, and… More >

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212

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ARTICLE

Securing Electronic Health Records with Cryptography and Lion Optimization

Journal of Cyber Security, Vol.7, pp. 21-43, 2025, DOI:10.32604/jcs.2025.059645 - 18 February 2025
Abstract With the internet and modern mobile technologies, health-related information is readily available, and thus, the security aspect of health information is at great risk. Confidentiality and protection of medical information regarding patients are of prime concern in the context of sharing such data with different healthcare providers. On one hand, Electronic Health Record Systems (EHRS) and online sites have proved to be hassle-free ways of exchanging medical information between health professionals. On the other hand, data security issues remain a concern. The proposed paper presents an improvement in the security mechanism of EHRS by utilizing… More >

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480

Open Access

REVIEW

Quick Response Code Security Attacks and Countermeasures: A Systematic Literature Review

Journal of Cyber Security, Vol.7, pp. 1-20, 2025, DOI:10.32604/jcs.2025.059398 - 18 February 2025
Abstract A quick response code is a barcode that allows users to instantly access information via a digital device. Quick response codes store data as pixels in a square-shaped grid. QR codes are prone to cyber-attacks. This assault exploits human vulnerabilities, as users can scarcely discern what is concealed in the quick response code prior to usage. The aim of the study was to investigate Quick Response code attack types and the detection techniques. To achieve the objective, 50 relevant studies published between the year 2010 and 2024 were identified. The articles were obtained from the… More >

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492

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ARTICLE

A Comparative Performance Analysis of Machine Learning Models for Intrusion Detection Classification

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

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1162

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ARTICLE

Comparative Analysis of Machine Learning Models for PDF Malware Detection: Evaluating Different Training and Testing Criteria

Journal of Cyber Security, Vol.5, pp. 1-11, 2023, DOI:10.32604/jcs.2023.042501
Abstract The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for More >

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1238

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REVIEW

Enhancing Cyber Security through Artificial Intelligence and Machine Learning: A Literature Review

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

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595

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ARTICLE

Sentence Level Analysis Model for Phishing Detection Using KNN

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

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1

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ARTICLE

Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

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

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986

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ARTICLE

Detecting Phishing Using a Multi-Layered Social Engineering Framework

Journal of Cyber Security, Vol.5, pp. 13-32, 2023, DOI:10.32604/jcs.2023.043359
Abstract As businesses develop and expand with a significant volume of data, data protection and privacy become increasingly important. Research has shown a tremendous increase in phishing activities during and after COVID-19. This research aimed to improve the existing approaches to detecting phishing activities on the internet. We designed a multi-layered phish detection algorithm to detect and prevent phishing applications on the internet using URLs. In the algorithm, we considered technical dimensions of phishing attack prevention and mitigation on the internet. In our approach, we merge, Phishtank, Blacklist, Blocklist, and Whitelist to form our framework. A More >

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851

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ARTICLE

Enhancing Private Cloud Based Intrusion Prevention and Detection System: An Unsupervised Machine Learning Approach

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

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443

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ARTICLE

Performance Evaluation of Machine Learning Algorithms in Reduced Dimensional Spaces

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

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1048

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657

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ARTICLE

An Intrusion Detection Method Based on a Universal Gravitation Clustering Algorithm

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

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864

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647

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ARTICLE

Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

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

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