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  • Open Access

    ARTICLE

    Phishing Scam Detection on Ethereum via Mining Trading Information

    Yanyu Chen1, Zhangjie Fu1,2,*

    Journal of Cyber Security, Vol.4, No.3, pp. 189-200, 2022, DOI:10.32604/jcs.2022.038401

    Abstract As a typical representative of web 2.0, Ethereum has significantly boosted the development of blockchain finance. However, due to the anonymity and financial attributes of Ethereum, the number of phishing scams is increasing rapidly and causing massive losses, which poses a serious threat to blockchain financial security. Phishing scam address identification enables to detect phishing scam addresses and alerts users to reduce losses. However, there are three primary challenges in phishing scam address recognition task: 1) the lack of publicly available large datasets of phishing scam address transactions; 2) the use of multi-order transaction information requires a large number of… More >

  • Open Access

    ARTICLE

    Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

    R. Brindha1, S. Nandagopal2, H. Azath3, V. Sathana4, Gyanendra Prasad Joshi5, Sung Won Kim6,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5901-5914, 2023, DOI:10.32604/cmc.2023.030784

    Abstract Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’ sensitive data. E-mails, instant messages and phone calls are some of the common modes used in cyberattacks. Though the security models are continuously upgraded to prevent cyberattacks, hackers find innovative ways to target the victims. In this background, there is a drastic increase observed in the number of phishing emails sent to potential targets. This scenario necessitates the importance of designing an effective classification model. Though numerous conventional models are available in the literature for proficient classification of phishing emails,… More >

  • Open Access

    ARTICLE

    Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

    Ashit Kumar Dutta1,*, T. Meyyappan2, Basit Qureshi3, Majed Alsanea4, Anas Waleed Abulfaraj5, Manal M. Al Faraj1, Abdul Rahaman Wahab Sait6

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2701-2713, 2023, DOI:10.32604/csse.2023.028984

    Abstract Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users’ private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the BBODL-PEDC model is to distinguish… More >

  • Open Access

    ARTICLE

    Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

    Hadil Shaiba1, Jaber S. Alzahrani2, Majdy M. Eltahir3, Radwa Marzouk4, Heba Mohsen5, Manar Ahmed Hamza6,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6425-6441, 2022, DOI:10.32604/cmc.2022.031625

    Abstract Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since… More >

  • Open Access

    ARTICLE

    URL Phishing Detection Using Particle Swarm Optimization and Data Mining

    Saeed M. Alshahrani1, Nayyar Ahmed Khan1,*, Jameel Almalki2, Waleed Al Shehri2

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5625-5640, 2022, DOI:10.32604/cmc.2022.030982

    Abstract The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research. Various techniques are adopted in the detection process, including neural networks, machine learning, or hybrid techniques. A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique (PSO) to increase and empower the method of detecting phishing URLs. Feature selection based on various techniques to identify the phishing candidates from the URL is conducted. In this approach, the features mined from the URL are extracted using data mining rules. The features are selected on the basis of URL… More >

  • Open Access

    ARTICLE

    Phish Block: A Blockchain Framework for Phish Detection in Cloud

    R. N. Karthika*, C. Valliyammai, M. Naveena

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 777-795, 2023, DOI:10.32604/csse.2023.024086

    Abstract The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy. But, deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes. In Spite of much technological advancement, phishing acts as the first step in a series of attacks. With technological advancements, availability and access to the phishing kits has improved drastically, thus making it an ideal tool for the hackers to execute the attacks. The phishing cases indicate use of foreign characters to disguise the original Uniform Resource Locator (URL), typosquatting the popular domain names, using reserved characters… More >

  • Open Access

    ARTICLE

    Impact Analysis of Resilience Against Malicious Code Attacks via Emails

    Chulwon Lee1, Kyungho Lee2,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4803-4816, 2022, DOI:10.32604/cmc.2022.025310

    Abstract The damage caused by malicious software is increasing owing to the COVID-19 pandemic, such as ransomware attacks on information technology and operational technology systems based on corporate networks and social infrastructures and spear-phishing attacks on business or research institutes. Recently, several studies have been conducted to prevent further phishing emails in the workplace because malware attacks employ emails as the primary means of penetration. However, according to the latest research, there appears to be a limitation in blocking email spoofing through advanced blocking systems such as spam email filtering solutions and advanced persistent threat systems. Therefore, experts believe that it… More >

  • Open Access

    ARTICLE

    Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

    R. Sakunthala Jenni*, S. Shankar

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 525-538, 2022, DOI:10.32604/csse.2022.019300

    Abstract The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable. Phishing websites also create traffic in the entire network. Another phishing issue is the broadening malware of the entire network, thus highlighting the demand for their detection while massive datasets (i.e., big data) are processed. Despite the application of boosting mechanisms in phishing detection, these methods are prone to significant errors in their output, specifically due to the combination of all website features in the training state. The upcoming big data system requires MapReduce, a popular… More >

  • Open Access

    ARTICLE

    Phishing Websites Detection by Using Optimized Stacking Ensemble Model

    Zeyad Ghaleb Al-Mekhlafi1, Badiea Abdulkarem Mohammed1,2,*, Mohammed Al-Sarem3, Faisal Saeed3, Tawfik Al-Hadhrami4, Mohammad T. Alshammari1, Abdulrahman Alreshidi1, Talal Sarheed Alshammari1

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 109-125, 2022, DOI:10.32604/csse.2022.020414

    Abstract Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for… More >

  • Open Access

    ARTICLE

    Impact of Human Vulnerabilities on Cybersecurity

    Maher Alsharif1, Shailendra Mishra2,*, Mohammed AlShehri1

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1153-1166, 2022, DOI:10.32604/csse.2022.019938

    Abstract Today, security is a major challenge linked with computer network companies that cannot defend against cyber-attacks. Numerous vulnerable factors increase security risks and cyber-attacks, including viruses, the internet, communications, and hackers. Internets of Things (IoT) devices are more effective, and the number of devices connected to the internet is constantly increasing, and governments and businesses are also using these technologies to perform business activities effectively. However, the increasing uses of technologies also increase risks, such as password attacks, social engineering, and phishing attacks. Humans play a major role in the field of cybersecurity. It is observed that more than 39%… More >

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