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Search Results (23)
  • 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

    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

    Phishing Website URL’s Detection Using NLP and Machine Learning Techniques

    Dinesh Kalla1,*, Sivaraju Kuraku2

    Journal on Artificial Intelligence, Vol.5, pp. 145-162, 2023, DOI:10.32604/jai.2023.043366

    Abstract Phishing websites present a severe cybersecurity risk since they can lead to financial losses, data breaches, and user privacy violations. This study uses machine learning approaches to solve the problem of phishing website detection. Using artificial intelligence, the project aims to provide efficient techniques for locating and thwarting these dangerous websites. The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification. Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Classifiers, Linear Support Vector Classifiers, and Naive Bayes were all used in the inquiry. This research… More >

  • Open Access

    REVIEW

    Survey on Deep Learning Approaches for Detection of Email Security Threat

    Mozamel M. Saeed1,*, Zaher Al Aghbari2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 325-348, 2023, DOI:10.32604/cmc.2023.036894

    Abstract Emailing is among the cheapest and most easily accessible platforms, and covers every idea of the present century like banking, personal login database, academic information, invitation, marketing, advertisement, social engineering, model creation on cyber-based technologies, etc. The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication. Therefore, this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security. This study compiles the literature related to the deep learning methodologies, which are applicable for providing safety in the field of cyber security of email in different… 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

    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

    Modelling an Efficient URL Phishing Detection Approach Based on a Dense Network Model

    A. Aldo Tenis*, R. Santhosh

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2625-2641, 2023, DOI:10.32604/csse.2023.036626

    Abstract The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing. The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator (URLs) analysis. The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions, which deals with the detection of phishing. Contrarily, the URLs in both classes from the login page due, considering the representation of a real… More >

  • Open Access

    ARTICLE

    Assessing Secure OpenID-Based EAAA Protocol to Prevent MITM and Phishing Attacks in Web Apps

    Muhammad Bilal1,*, Sandile C. Shongwe2, Abid Bashir3, Yazeed Y. Ghadi4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4713-4733, 2023, DOI:10.32604/cmc.2023.037071

    Abstract To secure web applications from Man-In-The-Middle (MITM) and phishing attacks is a challenging task nowadays. For this purpose, authentication protocol plays a vital role in web communication which securely transfers data from one party to another. This authentication works via OpenID, Kerberos, password authentication protocols, etc. However, there are still some limitations present in the reported security protocols. In this paper, the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern method. The proposed OpenID-based encrypted Email’s Authentication, Authorization, and Accounting (EAAA) protocol ensure security by relying on the email authenticity and… More >

  • Open Access

    ARTICLE

    Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

    S. Ashwini*, S. Magesh Kumar

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3043-3056, 2023, DOI:10.32604/iasc.2023.034551

    Abstract Increase in the use of internet of things owned devices is one of the reasons for increased network traffic. While connecting the smart devices with publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing system. The Phishing is the slow and resilient attack stacking techniques probe the users. The proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm (NWAT). Though Predictive mapping, Predictive Validation and Predictive analysis mechanism is developed. For the test purpose… More >

  • Open Access

    ARTICLE

    Machine Learning Techniques for Detecting Phishing URL Attacks

    Diana T. Mosa1,2, Mahmoud Y. Shams3,*, Amr A. Abohany2, El-Sayed M. El-kenawy4, M. Thabet5

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1271-1290, 2023, DOI:10.32604/cmc.2023.036422

    Abstract Cyber Attacks are critical and destructive to all industry sectors. They affect social engineering by allowing unapproved access to a Personal Computer (PC) that breaks the corrupted system and threatens humans. The defense of security requires understanding the nature of Cyber Attacks, so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks. Cyber-Security proposes appropriate actions that can handle and block attacks. A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information. One of the online security challenges… More >

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