Faculty Publications
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Item PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices(Elsevier B.V., 2019) Rao, R.S.; Vaishnavi, T.; Pais, A.R.Phishing is a technique in which the attackers trick the online users to reveal the sensitive information by creating the phishing sites which look similar to that of legitimate sites. There exist many techniques to detect phishing sites in desktop computers. In recent years, the number of mobile users accessing the web has increased which lead to a rise in the number of attacks in mobile devices. Existing techniques designed for desktop computers may not be suitable for mobile devices due to their hardware limitations such as RAM, Screen size, low computational power etc. In this paper, we propose a mobile application named PhishDump to classify the legitimate and phishing websites in mobile devices. PhishDump is based on the multi-model ensemble of Long Short Term Memory (LSTM) and Support Vector Machine (SVM) classifier. As PhishDump focuses on the extraction of features from URL, it has several advantages over existing works such as fast computation, language independence and robust to accidental download of malwares. From the experimental analysis, we observed that our proposed multi-model ensemble outperformed traditional LSTM character and word-level models. PhishDump performed better than the existing baseline models with an accuracy of 97.30% on our dataset and 98.50% on the benchmark dataset. © 2019 Elsevier B.V.Item Enhanced Malicious Traffic Detection in Encrypted Communication Using TLS Features and a Multi-class Classifier Ensemble(Springer, 2024) Kondaiah, C.; Pais, A.R.; Rao, R.S.The use of encryption for network communication leads to a significant challenge in identifying malicious traffic. The existing malicious traffic detection techniques fail to identify malicious traffic from the encrypted traffic without decryption. The current research focuses on feature extraction and malicious traffic classification from the encrypted network traffic without decryption. In this paper, we propose an ensemble model using Deep Learning (DL), Machine Learning (ML), and self-attention-based methods. Also, we propose novel TLS features extracted from the network and perform experimentation on the ensemble model. The experimental results demonstrated that the ML-based (RF, LGBM, XGB) ensemble model achieved a significant accuracy of 94.85% whereas the other ensemble model using RF, LSTM, and Bi-LSTM with self-attention technique achieved an accuracy of 96.71%. To evaluate the efficacy of our proposed models, we curated datasets encompassing both phishing, legitimate and malware websites, leveraging features extracted from TLS 1.2 and 1.3 traffic without decryption. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Item An ensemble learning approach for detecting phishing URLs in encrypted TLS traffic(Springer, 2024) Kondaiah, C.; Pais, A.R.; Rao, R.S.Phishing is a fraudulent method used by hackers to acquire confidential data from victims, including security passwords, bank account details, debit card data, and other sensitive data. Owing to the increase in internet users, the corresponding network attacks have also grown over the last decade. Existing phishing detection methods are implemented for the application layer and are not effectively adapted to the transport layer. In this paper, we propose a novel phishing detection method that extends beyond traditional approaches by utilizing a multi-model ensemble of deep neural networks, long short term memory, and Random Forest classifiers. Our approach is distinguished by its unique feature extraction from transport layer security (TLS) 1.2 and 1.3 network traffic and the application of advanced deep learning algorithms to enhance phishing detection capabilities. To assess the effectiveness of our model, we curated datasets that include both phishing and legitimate websites, using features derived from TLS 1.2 and 1.3 traffic. The experimental results show that our proposed model achieved a classification accuracy of 99.61%, a precision of 99.80%, and a Matthews Correlation Coefficient of 99.22% on an in-house dataset. Our model excels at detecting phishing Uniform Resource Locator at the transport layer without data decryption. It is designed to block phishing attacks at the network gateway or firewall level. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.