Detection of Phishing URLs Using Heuristics-Based Approach

Salihu, Shakirat Aderonke; Oladipo, Idowu Dauda; Wojuade, Abdul Afeez; AbdulRaheem, Muyideen; Babatunde, Abdulrauph Olanrewaju; Ajiboye, Adeleke Raheem; Balogun, Ghaniyyat Bolanle

doi:10.1109/ited56637.2022.10051199

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…One method includes using Machine Learning models to specifically detect phishing URLs by analyzing their characteristics, with the goal of evaluating and choosing the most effective model. [6] The other methods can also be explored here such as using of Machine learning and artificial intelligence which is most prominent nowadays it helps with algorithm and analyze vast amount of data which covers most of the digital or cyber space which individuals are actively involved in order to identify and analyze different patterns and trends. The other most commonly used is Real-Time Detection Technique which helps us filtering the received emails and can be sophisticated phishing attacks, this includes analyzing suspicious URLs, emails content and also user behavior, this point is explained above citated material.…”

Section: Strategiesmentioning

confidence: 99%

Phishing - Unvielling Its Techniques

R G

2024

IJSREM

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This study offers a detailed investigation of phishing attacks, a common cybersecurity danger aimed at individuals and organizations globally. It explores the reasons behind phishing attacks, which vary from making money to taking advantage of people's psychology. The research underscores the necessity for thorough detection systems and protective measures in order to effectively combat phishing. A study among knowledgeable individuals in the cybersecurity sector provides information on their awareness and vulnerability to phishing attempts. The results highlight how common phishing attacks are and the significance of grasping various attack vectors. KEYWORDS : Cyber Threat, Cyber Security, Phishing, Social Engineering, Identity Theft.

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Section: Strategiesmentioning

confidence: 99%

Phishing - Unvielling Its Techniques

R G

2024

IJSREM

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An Enhanced K-Means Clustering Algorithm for Phishing Attack Detections

Al-Sabbagh,

Hamze,

Khan

et al. 2024

Electronics

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Phishing attacks continue to pose a significant threat to cybersecurity, employing increasingly sophisticated techniques to deceive victims into revealing sensitive information or downloading malware. This paper presents a comprehensive study on the application of Machine Learning (ML) techniques for identifying phishing websites, with a focus on enhancing detection accuracy and efficiency. We propose an approach that integrates the CfsSubsetEval attribute evaluator with the K-Means Clustering algorithm to improve phishing detection capabilities. Our method was evaluated using datasets of varying sizes (2000, 7000, and 10,000 samples) from a publicly available repository. Simulation results demonstrate that our approach achieves an accuracy of 89.2% on the 2000-sample dataset, outperforming the traditional kernel K-Means algorithm, which achieved an accuracy of 51.5%. Further analysis using precision, recall, and F1-score metrics corroborates the effectiveness of our method. We also discuss the scalability and real-world applicability of our approach, addressing limitations and proposing future research directions. This study contributes to the ongoing efforts to develop robust, efficient, and adaptable phishing detection systems in the face of evolving cyber threats.

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Detection of Phishing URLs Using Heuristics-Based Approach

Cited by 2 publications

References 14 publications

Phishing - Unvielling Its Techniques

Phishing - Unvielling Its Techniques

An Enhanced K-Means Clustering Algorithm for Phishing Attack Detections

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