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 >

Dinesh Kalla^1,*, 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 >

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 >

Hadil Shaiba¹, Jaber S. Alzahrani², Majdy M. Eltahir³, Radwa Marzouk⁴, Heba Mohsen⁵, Manar Ahmed Hamza^6,*

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 >

Saeed M. Alshahrani¹, Nayyar Ahmed Khan^1,*, 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 >

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 >

Guoyuan Lin^1,2,*, Bowen Liu¹, Pengcheng Xiao³, Min Lei⁴, Wei Bi^5,6

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 533-547, 2018, DOI:10.32604/cmc.2018.03720

Abstract Anti-detection is becoming as an emerging challenge for anti-phishing. This paper solves the threats of anti-detection from the threshold setting condition. Enough webpages are considered to complicate threshold setting condition when the threshold is settled. According to the common visual behavior which is easily attracted by the salient region of webpages, image retrieval methods based on texton correlation descriptor (TCD) are improved to obtain enough webpages which have similarity in the salient region for the images of webpages. There are two steps for improving TCD which has advantage of recognizing the salient region of images: (1) This paper proposed Weighted… More >