Home / Journals / JCS / Vol.6, No.1, 2024
  • Open AccessOpen Access


    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 AccessOpen Access


    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 >

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