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  • Open Access

    ARTICLE

    Sentence Level Analysis Model for Phishing Detection Using KNN

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

    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 than satisfactory. Additionally, there has… More >

  • Open Access

    ARTICLE

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

    Adil Hussain1, Amna Khatoon2,*, Ayesha Aslam2, Tariq1, Muhammad Asif Khosa1

    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 machine learning algorithms in the… More >

  • Open Access

    ARTICLE

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

    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

    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 potential of ML techniques in… More >

  • Open Access

    ARTICLE

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

    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

    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 machine learning models employed. Such… More >

  • Open Access

    ARTICLE

    Detecting Phishing Using a Multi-Layered Social Engineering Framework

    Kofi Sarpong Adu-Manu*, Richard Kwasi Ahiable

    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 web application system and browser… More >

  • Open Access

    ARTICLE

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

    Bilal Khan1, Muhammad Arshad2, Sarwar Shah Khan3,4,*

    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 PDF malware detection. The study… More >

  • Open Access

    ARTICLE

    Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method

    Abdulrahman M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*

    Journal of Cyber Security, Vol.4, No.4, pp. 287-298, 2022, DOI:10.32604/jcs.2023.042486

    Abstract Unmanned Aerial Vehicles (UAVs) proliferate quickly and play a significant part in crucial tasks, so it is important to protect the security and integrity of UAV communication channels. Intrusion Detection Systems (IDSs) are required to protect the UAV communication infrastructure from unauthorized access and harmful actions. In this paper, we examine a new approach for enhancing intrusion detection in UAV communication channels by utilizing the Long Short-Term Memory network (LSTM) combined with the Synthetic Minority Oversampling Technique (SMOTE) algorithm, and this integration is the binary classification method (LSTM-SMOTE). We successfully achieved 99.83% detection accuracy by using the proposed approach and… More >

  • Open Access

    ARTICLE

    Ensuring Information Security in Smart Door Lock Systems Using the Cuckoo Search Algorithm

    Arkan Kh Shakr Sabonchi1,*, Zainab Hashim Obaid2

    Journal of Cyber Security, Vol.4, No.4, pp. 269-286, 2022, DOI:10.32604/jcs.2023.041707

    Abstract The widespread use of Internet of Things (IoT) devices, such as smart appliances, phones, and watches, has brought about concerns regarding security and privacy. With the increasing prevalence of cyberattacks from both malicious and non-malicious sources, security has become a critical factor in the design of IoT systems. In particular, data security poses a significant challenge due to the growing amount of data stored in IoT systems. Inadequate security measures can allow hackers to take over IoT devices remotely, resulting in significant damage. To address these concerns and improve the security of IoT-based smart door lock systems, this research proposes… More >

  • Open Access

    REVIEW

    Phishing Attacks in Social Engineering: A Review

    Kofi Sarpong Adu-Manu*, Richard Kwasi Ahiable, Justice Kwame Appati, Ebenezer Essel Mensah

    Journal of Cyber Security, Vol.4, No.4, pp. 239-267, 2022, DOI:10.32604/jcs.2023.041095

    Abstract Organisations closed their offices and began working from home online to prevent the spread of the COVID-19 virus. This shift in work culture coincided with increased online use during the same period. As a result, the rate of cybercrime has skyrocketed. This study examines the approaches, techniques, and countermeasures of Social Engineering and phishing in this context. The study discusses recent trends in the existing approaches for identifying phishing assaults. We explore social engineering attacks, categorise them into types, and offer both technical and social solutions for countering phishing attacks which makes this paper different from similar works that mainly… More >

  • Open Access

    ARTICLE

    Seeded Transfer Learning for Enhanced Attack Trace and Effective Deception

    Jalaj Pateria1,*, Laxmi Ahuja1, Subhranil Som2

    Journal of Cyber Security, Vol.4, No.4, pp. 223-238, 2022, DOI:10.32604/jcs.2023.040186

    Abstract Cyberattacks have reached their peak during COVID-19, and intruders urge to gain the upper hand in the cybersecurity battlefield, even gaining dominance. Now intruders are trying harder to elude behavior analysis techniques, which in turn gets organization security to come for a toss. This phenomenon is even more prevalent in agentless environments (IOT devices, mobile devices), where we do not have any access to edge devices and rely on packet data to predict any attack and its actors. In this paper, we shall be discussing enhancing the accuracy of anomalous behavior detection techniques for efficient threat intelligence and revamping deception… More >

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