PhiUSIIL: A diverse security profile empowered phishing URL detection framework based on similarity index and incremental learning

Prasad, Arvind; Chandra, Shalini

doi:10.1016/j.cose.2023.103545

Cited by 10 publications

(1 citation statement)

References 32 publications

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“…The experiment utilized well-established datasets such as URL dataset (ISCX-URL2016) [19], UNB [20], and phistank [21]. Imbalanced data poses a common challenge in machine learning, where one class has significantly more samples than the others [21][22][23][24]. This imbalance can lead to biased models that prioritize the majority class and perform poorly with the minority class [25,26].…”

Section: Proposed Systemsmentioning

confidence: 99%

WCA: Integration of Natural Language Processing Methods and Machine Learning Model for Effective Analysis of Web Content to Classify Malicious Webpages

Shaheetha Liaquathali,

Vadivazhagan Kadirvelu

2024

ARASET

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Malicious websites have become a pervasive concern in the digital realm, targeting careless users as well as organizations. It may result in substantial financial losses, identity theft, data intrusions, and damage to reputation. In order to create efficient countermeasures, it is crucial to comprehend the effects of interacting with such websites. There are several ways to classify malicious webpages. Web content analysis is one such way for protecting internet users from malicious activities. It entails analysing websites to identify potential hazards, such as phishing attempts, malware distribution, and fraudulent activities. Traditional methods relied on rule-based systems, but recent advances in natural language processing and machine learning have opened up new avenues for increasing the precision and scalability of web content analysis to classify malicious webpages. Most of the exciting research work focuses more on URL alone for risk free processing. This paper introduces novel method for analysing web contents especially textual contents of the webpages for classification. Among various tags in web technology, proposed method focuses on div, paragraph and meta tags. The textual contents of these tags are extracted and vectorized using three different vectorizers in natural language processing and classify the webpages using machine learning models. Seven different machine learning models are used for performance evaluation. The result shows that a combined textual content of three distinct tags with count vectorizer + random forest achieves the higher accuracy of 93.46% with 1000 features.

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Section: Proposed Systemsmentioning

confidence: 99%