A Study of Malicious URL Detection Using Machine Learning and Heuristic Approaches

Begum, Aliya; Badugu, Srinivasu

doi:10.1007/978-3-030-24318-0_68

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…Both folds above are required for this. While the first portion of feature representation is based on domain knowledge and heuristics, the second half focuses on training the classification model through data-driven optimization [ 2 , 5 , 10 ]. Malicious URL detection uses several features.…”

Section: Proposed Malicious Url Detection Modelmentioning

confidence: 99%

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Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks

Darwish,

Farhan,

Elzoghabi

2023

Biomimetics

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To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding malicious domains by passively analyzing DNS data. The proposed model builds a real-time, accurate, middleweight, and fast classifier by combining a genetic algorithm for selecting DNS data features with a two-step quantum ant colony optimization (QABC) algorithm for classification. The modified two-step QABC classifier uses K-means instead of random initialization to place food sources. In order to overcome ABCs poor exploitation abilities and its convergence speed, this paper utilizes the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach (K-mean and QABC) to deal with the large size of uniform resource locator (URL) data is one of the main contributions of this paper. The major point is that blacklists, heavyweight classifiers (those that use more features), and lightweight classifiers (those that use fewer features and consume the features from the browser) may all be improved with the use of the suggested machine learning method. The results showed that the suggested model could work with more than 96.6% accuracy for more than 10 million query–answer pairs.

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Section: Proposed Malicious Url Detection Modelmentioning

confidence: 99%

“…If people were warned before visiting a harmful URL, this issue may be reduced. The security community has responded by building blacklisting toolbars, appliances, and search engines [ 1 , 2 ]. Many harmful sites are not banned because they are too new, have never been examined, or are evaluated wrongly (e.g., cloaking).…”

Section: Introductionmentioning

confidence: 99%

Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks

Darwish,

Farhan,

Elzoghabi

2023

Biomimetics

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“…Authors in Begum and Badugu [14] discussed some approaches which are useful to detect a phishing attack. They performed a detailed survey of existing techniques such as Machine Learning (ML) based approaches, Nonmachine Learning-based approaches, Neural Network-based approaches, and Behavior-based detection approaches for phishing attack detection.…”

Section: Scenario-based Phishing Attack Detectionmentioning

confidence: 99%

A comprehensive survey of AI-enabled phishing attacks detection techniques

et al. 2020

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In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

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A Comparative Study of Malicious URL Detection: Regular Expression Analysis, Machine Learning, and VirusTotal API

Misquitta,

Kannan

2024

EAI/Springer Innovations in Communication and Computing

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In this paper, malicious URL detection was done in 3 ways. In the first method, self-written code was used that breaks up the URL into several fragments and then using functions and checkers analyses whether the URL is malicious. The second approach involved utilizing a fixed csv dataset containing website URLs from over 11,000 websites. Each entry in the dataset included 30 parameters describing the website and a class label indicating whether it was classified as a phishing website (1) or not (-1). 9 machine learning models were compared on this dataset to see which models gave the highest accuracy and F1-score. The last method was done using VirusTotal API Key. The user has to input the URL of a website and the code classifies it as malicious or not. A report is also generated if the URL is detected as malicious. The report is a compilation of the analysis of that website by several security vendors.

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A Study of Malicious URL Detection Using Machine Learning and Heuristic Approaches

Cited by 12 publications

References 22 publications

Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks

Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks

A comprehensive survey of AI-enabled phishing attacks detection techniques

A Comparative Study of Malicious URL Detection: Regular Expression Analysis, Machine Learning, and VirusTotal API

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