Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Detection of phishing websites using an efficient feature-based machine learning framework(Springer London, 2019) Rao, R.S.; Pais, A.R.Phishing is a cyber-attack which targets naive online users tricking into revealing sensitive information such as username, password, social security number or credit card number etc. Attackers fool the Internet users by masking webpage as a trustworthy or legitimate page to retrieve personal information. There are many anti-phishing solutions such as blacklist or whitelist, heuristic and visual similarity-based methods proposed to date, but online users are still getting trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel classification model, based on heuristic features that are extracted from URL, source code, and third-party services to overcome the disadvantages of existing anti-phishing techniques. Our model has been evaluated using eight different machine learning algorithms and out of which, the Random Forest (RF) algorithm performed the best with an accuracy of 99.31%. The experiments were repeated with different (orthogonal and oblique) random forest classifiers to find the best classifier for the phishing website detection. Principal component analysis Random Forest (PCA-RF) performed the best out of all oblique Random Forests (oRFs) with an accuracy of 99.55%. We have also tested our model with the third-party-based features and without third-party-based features to determine the effectiveness of third-party services in the classification of suspicious websites. We also compared our results with the baseline models (CANTINA and CANTINA+). Our proposed technique outperformed these methods and also detected zero-day phishing attacks. © 2018, The Natural Computing Applications Forum.Item Efficient deep learning techniques for the detection of phishing websites(Springer, 2020) Somesha, M.; Pais, A.R.; Rao, R.S.; Rathour, V.S.Phishing is a fraudulent practice and a form of cyber-attack designed and executed with the sole purpose of gathering sensitive information by masquerading the genuine websites. Phishers fool users by replicating the original and genuine contents to reveal personal information such as security number, credit card number, password, etc. There are many anti-phishing techniques such as blacklist- or whitelist-, heuristic-feature- and visual-similarity-based methods proposed as of today. Modern browsers adapt to reduce the chances of users getting trapped into a vicious agenda, but still users fall as prey to phishers and end up revealing their secret information. In a previous work, the authors proposed a machine learning approach based on heuristic features for phishing website detection and achieved an accuracy of 99.5% using 18 features. In this paper, we have proposed novel phishing URL detection models using (a) Deep Neural Network (DNN), (b) Long Short-Term Memory (LSTM) and (c) Convolution Neural Network (CNN) using only 10 features of our earlier work. The proposed technique achieves an accuracy of 99.52% for DNN, 99.57% for LSTM and 99.43% for CNN. The proposed techniques utilize only one third-party service feature, thus making it more robust to failure and increases the speed of phishing detection. © 2020, Indian Academy of Sciences.Item A heuristic technique to detect phishing websites using TWSVM classifier(Springer Science and Business Media Deutschland GmbH, 2021) Rao, R.S.; Pais, A.R.; Anand, P.Phishing websites are on the rise and are hosted on compromised domains such that legitimate behavior is embedded into the designed phishing site to overcome the detection. The traditional heuristic techniques using HTTPS, search engine, Page Ranking and WHOIS information may fail in detecting phishing sites hosted on the compromised domain. Moreover, list-based techniques fail to detect phishing sites when the target website is not in the whitelisted data. In this paper, we propose a novel heuristic technique using TWSVM to detect malicious registered phishing sites and also sites which are hosted on compromised servers, to overcome the aforementioned limitations. Our technique detects the phishing websites hosted on compromised domains by comparing the log-in page and home page of the visiting website. The hyperlink and URL-based features are used to detect phishing sites which are maliciously registered. We have used different versions of support vector machines (SVMs) for the classification of phishing websites. We found that twin support vector machine classifier (TWSVM) outperformed the other versions with a significant accuracy of 98.05% and recall of 98.33%. © 2020, Springer-Verlag London Ltd., part of Springer Nature.