Phishing attacks remain one of the most prevalent cybersecurity threats, affecting individuals and organizations globally. The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns (...) to enhance the detection system's decision-making process. (shrink)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web (...) page phishing attacks are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and (...) reduced false positives. (shrink)

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time.

The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced false positives. The proposed model outperformed traditional machine learning algorithms, showing promise for real-world deployment in phishing detection systems. We conclude with suggestions for future improvements, such as incorporating more behavioral data and deploying the system in realtime monitoring applications.

The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. The Genetic Ranking Optimization Algorithm (GROA) is used to (...) rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this (...) research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their (...) vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

The internet has made the world more linked than ever before. While taking advantage of this online transition, cybercriminals target flaws in online systems, networks, and infrastructure. Businesses, government organizations, people, and communities all across the world, particularly in African countries, are all severely impacted on an economic and social level. Many African countries focused more on developing secure electricity and internet networks; yet, cybersecurity usually receives less attention than it should. One of Africa's major issues is the lack of (...) adequate digital security infrastructure, which has harmed businesses, governmental institutions, and individual communities more than it has helped. The majority of African countries operate without cybersecurity measures in place to combat cyberattacks. Only a few examples of today's cyber risks include digital extortion, business email intrusion, data breaches, online fraud, ransomware, and phishing, and new types of cybercrime are always developing. Due to the advent of new technology, cybercriminals have become more organized and quicker in their attacks and alliance creation. To maintain a secure digital environment for all internet users, this study focused on the challenges, solutions, and need for African countries to improve their online safety by tackling cybercrime, online fraud, and cybersecurity concerns. The objective of this study is to offer practical and long-term answers to the problems posed by cybercrime, with a continuing emphasis on enhancing online safety. It will assess the effectiveness of cutting-edge cybersecurity measures, legislative frameworks, and cross-border cooperative efforts, as well as potential areas for improvement. Additionally, the study will examine cutting-edge methods like blockchain technology, machine learning, and other cutting-edge methods that could improve our using digital defences to stop cybercrime. (shrink)

To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the existing state-of-the-art (...) machine learning models depends on different algorithms for training which also impact their suitability for detection of a particular type of cyberattack. In this research, we analyzed each of the current state-of-the art machine learning models for different types of cyberattack detection from the past 10 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack. (shrink)