Faculty Publications
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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 CatchPhish: detection of phishing websites by inspecting URLs(Springer, 2020) Rao, R.S.; Vaishnavi, T.; Pais, A.R.There exists many anti-phishing techniques which use source code-based features and third party services to detect the phishing sites. These techniques have some limitations and one of them is that they fail to handle drive-by-downloads. They also use third-party services for the detection of phishing URLs which delay the classification process. Hence, in this paper, we propose a light-weight application, CatchPhish which predicts the URL legitimacy without visiting the website. The proposed technique uses hostname, full URL, Term Frequency-Inverse Document Frequency (TF-IDF) features and phish-hinted words from the suspicious URL for the classification using the Random forest classifier. The proposed model with only TF-IDF features on our dataset achieved an accuracy of 93.25%. Experiment with TF-IDF and hand-crafted features achieved a significant accuracy of 94.26% on our dataset and an accuracy of 98.25%, 97.49% on benchmark datasets which is much better than the existing baseline models. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.Item Two level filtering mechanism to detect phishing sites using lightweight visual similarity approach(Springer, 2020) Rao, R.S.; Pais, A.R.The visual similarity-based techniques detect the phishing sites based on the similarity between the suspicious site and the existing database of resources such as screenshots, styles, logos, favicons etc. These techniques fail to detect phishing sites which target non-whitelisted legitimate domain or when phishing site with manipulated whitelisted legitimate content is encountered. Also, these techniques are not well adaptable at the client-side due to their computation and space complexity. Thus there is a need for light weight visual similarity-based technique detecting phishing sites targeting non-whitelisted legitimate resources. Unlike traditional visual similarity-based techniques using whitelists, in this paper, we employed a light-weight visual similarity based blacklist approach as a first level filter for the detection of near duplicate phishing sites. For the non-blacklisted phishing sites, we have incorporated a heuristic mechanism as a second level filter. We used two fuzzy similarity measures, Simhash and Perceptual hash for calculating the similarity score between the suspicious site and existing blacklisted phishing sites. Each similarity measure generates a unique fingerprint for a given website and also differs with less number of bits with a similar website. All three fingerprints together represent a website which undergoes blacklist filtering for the identification of the target website. The phishing sites which bypassed from the first level filter undergo second level heuristic filtering. We used comprehensive heuristic features including URL and source code based features for the detection of non-blacklisted phishing sites. The experimental results demonstrate that the blacklist filter alone is able to detect 55.58% of phishing sites which are either replicas or near duplicates of existing phishing sites. We also proposed an ensemble model with Random Forest (RF), Extra-Tree and XGBoost to evaluate the contribution of both blacklist and heuristic filters together as an entity and the model achieved a significant accuracy of 98.72% and Matthews Correlation Coefficient (MCC) of 97.39%. The proposed model is deployed as a chrome extension named as BlackPhish to provide real time protection against phishing sites at the client side. We also compared BlackPhish with the existing anti-phishing techniques where it outperformed existing works with a significant difference in accuracy and MCC. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.Item Application of word embedding and machine learning in detecting phishing websites(Springer, 2022) Rao, R.S.; Umarekar, A.; Pais, A.R.Phishing is an attack whose aim is to gain personal information such as passwords, credit card details etc. from online users by deceiving them through fake websites, emails or any legitimate internet service. There exists many techniques to detect phishing sites such as third-party based techniques, source code based methods and URL based methods but still users are getting trapped into revealing their sensitive information. In this paper, we propose a new technique which detects phishing sites with word embeddings using plain text and domain specific text extracted from the source code. We applied various word embedding for the evaluation of our model using ensemble and multimodal approaches. From the experimental evaluation, we observed that multimodal with domain specific text achieved a significant accuracy of 99.34% with TPR of 99.59%, FPR of 0.93%, and MCC of 98.68% © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.