Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
3 results
Search Results
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.Item Applicability of machine learning in spam and phishing email filtering: review and approaches(Springer Science+Business Media B.V. editorial@springerplus.com, 2020) Gangavarapu, T.; Jaidhar, C.D.; Chanduka, B.With the influx of technological advancements and the increased simplicity in communication, especially through emails, the upsurge in the volume of unsolicited bulk emails (UBEs) has become a severe threat to global security and economy. Spam emails not only waste users’ time, but also consume a lot of network bandwidth, and may also include malware as executable files. Alternatively, phishing emails falsely claim users’ personal information to facilitate identity theft and are comparatively more dangerous. Thus, there is an intrinsic need for the development of more robust and dependable UBE filters that facilitate automatic detection of such emails. There are several countermeasures to spam and phishing, including blacklisting and content-based filtering. However, in addition to content-based features, behavior-based features are well-suited in the detection of UBEs. Machine learning models are being extensively used by leading internet service providers like Yahoo, Gmail, and Outlook, to filter and classify UBEs successfully. There are far too many options to consider, owing to the need to facilitate UBE detection and the recent advances in this domain. In this paper, we aim at elucidating on the way of extracting email content and behavior-based features, what features are appropriate in the detection of UBEs, and the selection of the most discriminating feature set. Furthermore, to accurately handle the menace of UBEs, we facilitate an exhaustive comparative study using several state-of-the-art machine learning algorithms. Our proposed models resulted in an overall accuracy of 99% in the classification of UBEs. The text is accompanied by snippets of Python code, to enable the reader to implement the approaches elucidated in this paper. © 2020, Springer Nature B.V.