A machine learning based approach for phishing detection using hyperlinks information

Jain, Ankit Kumar; Gupta, Brij B.

doi:10.1007/s12652-018-0798-z

Cited by 172 publications

(92 citation statements)

References 24 publications

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“…The oldest approach included manual blacklisting of known phishing websites' URLs in centralized databases, later used by Internet browsers to alert users about possible threats. The negative aspect of the blacklisting method is that these databases do not include newly launched phishing websites and therefore do not protect from "the zero hour" attacks, as most of the phishing URLs are inserted in centralized databases only 12 hours after the first phishing attack (Jain and Gupta, 2018a). More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Classification Algorithms for Detection of Phishing Websites

Vaitkevicius¹,

Marcinkevičius²

2020

Informatica

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Phishing activities remain a persistent security threat, with global losses exceeding 2.7 billion USD in 2018, according to the FBI's Internet Crime Complaint Center. In literature, different generations of phishing websites detection methods have been observed. The oldest methods include manual blacklisting of known phishing websites' URLs in the centralized database, but they have not been able to detect newly launched phishing websites. More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem on phishing datasets, designed on features extracted from phishing websites' URLs. These studies have shown some classification algorithms performing better than others on differently designed datasets but have not distinguished the best classification algorithm for the phishing websites detection problem in general. The purpose of this research is to compare classic supervised machine learning algorithms on all publicly available phishing datasets with predefined features and to distinguish the best performing algorithm for solving the problem of phishing websites detection, regardless of a specific dataset design. Eight widely used classification algorithms were configured in Python using the Scikit Learn library and tested for classification accuracy on all publicly available phishing datasets. Later, classification algorithms were ranked by accuracy on different datasets using three different ranking techniques while testing the results for a statistically significant difference using Welch's T-Test. The comparison results are presented in this paper, showing ensembles and neural networks outperforming other classical algorithms.

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Section: Introductionmentioning

confidence: 99%

Comparison of Classification Algorithms for Detection of Phishing Websites

Vaitkevicius¹,

Marcinkevičius²

2020

Informatica

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show abstract

“…It is a domain of computer science that permits processors to concoct and read from data. It is considered a category of artificial intelligence as it facilitates the attainment of significant patterns being an element of human intelligence (Jain and Gupta 2019). The computer solves assigned task reliably and diligently, but this is less precise when handled by a human being.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

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Technology play a substantial function in exemplifying technical potentials that are unconsidered in the past to include intelligent behaviour. Computers in machine learning involve the modelling of intelligent behaviour with the least human mediation. Computational intelligence signifies a set of vital information processing methods for information managing and decision accomplishing. Major critical trials in technical exploration is the supervision of data obtained from various resources. Presently, Scanning Electron Microscopy (SEM) images are categorized as per user's measures, which greatly restrict misuse and reliability, owing for the need in definite classification algorithm. The foremost objective is to evaluate the exactness with each algorithm in terms of performance measures such as accuracy, precision, sensitivity, F-measure, kappa, computational time and confusion matrix. In this work, a recital assessment among three classification algorithms on SEM image dataset was conducted. Experimental results revealed that the Random forest algorithm gives the highest accuracy comparing to other two approach. Hence, the researchers would be able to perform automatic SEM image classification evading the necessity to categorize image and for providing a searchable database to identify a definite group of images.

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“…The approach proposed in [19] utilized hyperlinks features extracted from pages' source codes. Since their approach completely relied on the analysis of features extracted from the source code, therefore, the longer the source code, the higher the time-complexity.…”

Section: B Content-based Techniquesmentioning

confidence: 99%

PhishHaven—An Efficient Real-Time AI Phishing URLs Detection System

2020

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Different machine learning and deep learning-based approaches have been proposed for designing defensive mechanisms against various phishing attacks. Recently, researchers showed that phishing attacks can be performed by employing a deep neural network-based phishing URL generating system called DeepPhish. To prevent this kind of attack, we design an ensemble machine learning-based detection system called PhishHaven to identify AI-generated as well as human-crafted phishing URLs. To the best of our knowledge, this is the first study to consider detecting phishing attacks by both AI and human attackers. PhishHaven employs lexical analysis for feature extraction. To further enhance lexical analysis, we introduce URL HTML Encoding to classify URL on-the-fly and proactively compare with some of the existing methods. We also introduce a URL Hit approach to deal with tiny URLs, which is an open problem yet to be solved. Moreover, the final classification of URLs is made on an unbiased voting mechanism in PhishHaven, which aims to avoid misclassification when the number of votes is equal. To speed up the ensemble-based machine learning models, PhishHaven employs a multi-threading approach to execute the classification in parallel, leading to real-time detection. Theoretical analysis of our solution shows that (1) it can always detect tiny URLs, and (2) it can detect future AI-generated Phishing URLs based on our selected lexical features with 100% accuracy. Through experiments, we analyze our solution with a benchmark dataset of 100,000 phishing and normal URLs. The results show that PhishHaven can achieve 98.00% accuracy, outperforming the existing lexical-based human-crafted phishing URLs detection systems. INDEX TERMS AI-generated phishing URLs, ensemble machine learning, human-crafted phishing URLs, lexical features, multi-threading, tiny URLs, URL HTML encoding, voting.

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A machine learning based approach for phishing detection using hyperlinks information

Cited by 172 publications

References 24 publications

Comparison of Classification Algorithms for Detection of Phishing Websites

Comparison of Classification Algorithms for Detection of Phishing Websites

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

PhishHaven—An Efficient Real-Time AI Phishing URLs Detection System

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