Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A novel bio-inspired hybrid metaheuristic for unsolicited bulk email detection(Springer Science and Business Media Deutschland GmbH, 2020) Gangavarapu, T.; Jaidhar, C.D.With the recent influx of technology, Unsolicited Bulk Emails (UBEs) have become a potential problem, leaving computer users and organizations at the risk of brand, data, and financial loss. In this paper, we present a novel bio-inspired hybrid parallel optimization algorithm (Cuckoo-Firefly-GR), which combines Genetic Replacement (GR) of low fitness individuals with a hybrid of Cuckoo Search (CS) and Firefly (FA) optimizations. Cuckoo-Firefly-GR not only employs the random walk in CS, but also uses mechanisms in FA to generate and select fitter individuals. The content- and behavior-based features of emails used in the existing works, along with Doc2Vec features of the email body are employed to extract the syntactic and semantic information in the emails. By establishing an optimal balance between intensification and diversification, and reaching global optimization using two metaheuristics, we argue that the proposed algorithm significantly improves the performance of UBE detection, by selecting the most discriminative feature subspace. This study presents significant observations from the extensive evaluations on UBE corpora of 3, 844 emails, that underline the efficiency and superiority of our proposed Cuckoo-Firefly-GR over the base optimizations (Cuckoo-GR and Firefly-GR), dense autoencoders, recurrent neural autoencoders, and several state-of-the-art methods. Furthermore, the instructive feature subset obtained using the proposed Cuckoo-Firefly-GR, when classified using a dense neural model, achieved an accuracy of $$99\%$$. © Springer Nature Switzerland AG 2020.Item Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website(Springer Science and Business Media Deutschland GmbH, 2024) Mete, C.K.; Jaidhar, C.D.Web browsing has become an integral part of our daily lives, with most modern computer devices supporting easy access to online services and information. However, this convenience comes with a significant risk to user security. Web users are exposed to various types of cyberattacks, such as Phishing, malware, profiling, etc. These hazards have the potential to compromise individuals or organizations and deny lists. The traditional Phishing defense is no longer effective in shielding users from the constantly evolving nature of Phishing Uniform Resource Locators (URLs). To address this issue, this work proposes a One-Dimensional Convolutional Neural Networks (1D-CNN) and Feed-Forward Convolutional Neural Network (FF-CNN)-based Phishing URL detection approach. The proposed approach is trained with three different datasets: a URL-based feature dataset, an embedded feature-based dataset, and a combination of both feature datasets. Experiments show that the proposed 1D-CNN-based approach achieved detection accuracy of 98.83%, 98.09%, and 98.91% on the URL-based features dataset, embedded features dataset, and combined features dataset, respectively. Furthermore, the proposed FF-CNN-based approach achieved an accuracy of 98.87%, 97.18%, and 98.78% on the same datasets. This research provides an effective approach to combating the growing threat of web-based attacks and safeguarding the security of web users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.