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Semantic Based Greedy Levy Gradient Boosting Algorithm for phishing detection

Computer Systems Science & Engineering 2022年第5期41卷 525-538页

作者： R.Sakunthala Jenni S.Shankar Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India

The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are *** websites also create traffic in the entire *** phishing issue is the broadening malware of the entire network,thus highlighting the demand for their detection while massive datasets(i.e.,big data)are *** the application of boosting mechanisms in phishing detection,these methods are prone to significant errors in their output,specifically due to the combination of all website features in the training *** upcoming big data system requires MapReduce,a popular parallel programming,to process massive *** address these issues,a probabilistic latent semantic and greedy levy gradient boosting(PLS-GLGB)algorithm for website phishing detection using MapReduce is proposed.A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing ***,the missing data in each URL are identified and discarded for further processing to ensure data ***,with the preprocessed features(URLs),feature vectors are updated by the greedy levy divergence gradient(model)that selects the optimal features in the URL and accurately detects the ***,greedy levy efficiently differentiates between phishing websites and legitimate *** are conducted using one of the largest public corpora of a website phish tank *** show that the PLS-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection *** amounts of phishing detection time and errors are also saved during the detection of website phishing.

关键词： Web service providers probabilistic intersective latent semantic greedy levy divergence gradient phishing detection big data

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Architecture and algorithm for web phishing detection

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Journal of Southeast University(English Edition) 2010年第1期26卷 43-47页

作者：曹玖新王田峰时莉莉毛波东南大学计算机科学与工程学院南京210096 瑞典皇家理工大学结构与建筑环境学院斯德哥尔摩se10044

A phishing detection system, which comprises client-side filtering plug-in, analysis center and protected sites, is proposed. An image-based similarity detection algorithm is conceived to calculate the similarity of two web pages. The web pages are first converted into images, and then divided into sub-images with iterated dividing and shrinking. After that, the attributes of sub-images including color histograms, gray histograms and size parameters are computed to construct the attributed relational graph（ARG）of each page. In order to match two ARGs, the inner earth mover＇s distances（EMD）between every two nodes coming from each ARG respectively are first computed, and then the similarity of web pages by the outer EMD between two ARGs is worked out to detect phishing web pages. The experimental results show that the proposed architecture and algorithm has good robustness along with scalability, and can effectively detect phishing.

关键词： phishing detection image similarity attributed relational graph inner EMD outer EMD

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phishing Website URL’s detection Using NLP and Machine Learning Techniques

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Journal on Artificial Intelligence 2023年第1期5卷 145-162页

作者： Dinesh Kalla Sivaraju Kuraku Department of Computer Science Colorado Technical UniversityColorado SpringsCO80907USA Department of Computer Science University of the CumberlandsWilliamsburgKY40769USA

phishing websites present a severe cybersecurity risk since they can lead to financial losses,data breaches,and user privacy *** study uses machine learning approaches to solve the problem of phishing website *** artificial intelligence,the project aims to provide efficient techniques for locating and thwarting these dangerous *** study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website *** Regression,K-Nearest Neighbors,Decision Trees,Random Forests,Support Vector Classifiers,Linear Support Vector Classifiers,and Naive Bayes were all used in the *** research covers the benefits and drawbacks of several Machine Learning approaches,illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website *** insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing *** study evaluates and compares accuracy,precision and recalls of several machine learning models in detecting phishing website URL’s detection.

关键词： Cybersecurity artificial intelligence machine learning NLP phishing detection spam detection phinshing website URLs

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