Faculty Publications
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Publications by NITK Faculty
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Item Intrusion Detection Techniques for Detection of Cyber Attacks(Springer Science and Business Media Deutschland GmbH, 2021) Ahmed, S.S.; Kankar, M.; Rudra, B.Intrusion detection system (IDS) is a software-related application where we can detect the system or network activities and notice if any suspicious task happens. Excellent broadening and the use of the Internet lift examine the communication and save the digital information securely. Nowadays, attackers use variety of attacks for fetching private data. Most of the IDS techniques, algorithms, and methods assist to find those various attacks. The central aim of the project is to come up with an overall study about the intrusion detection mechanism, various types of attacks, various tools and techniques, and challenges. We used various machine learning algorithms and found performance metrics like accuracy, recall, and F-measure and compared with the existing work. After this research, we got good results that can help to detect the cyber attacks being performed in the network. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Designing Scalable Intrusion Detection Systems with Stacking Based Ensemble Learning(Springer Science and Business Media Deutschland GmbH, 2022) Sujan Reddy, A.S.; Akashdeep, S.; Kamath S․, S.; Rudra, B.Network Intrusion Detection Systems monitor the network traffic and reports any malicious activity. In this paper, a combination of feature engineering techniques and Ensemble Learning is proposed to build an effective Intrusion Detection System. The zero importance feature selection method is used to extract 23 features. Random forests, Feed Forward Neural Networks and Auto encoders are used as the base models and the predictions from these base models are combined using Extreme Gradient Boosting (XGB). To ensure that the proposed ensemble model is scalable as well, parallel programming is used for parallel computation of class probabilities from each model of the ensemble. The NSL-KDD dataset is used to train our models. To test our models, we use KDD+test dataset. Experimental results show that the proposed ensemble model outperforms several state-of-the-art works. The proposed parallel programming approach decreases the average prediction time of the model ensuring that the model is scalable. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.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 Hardware Security in Evolving FinTech Landscape(Springer Science and Business Media Deutschland GmbH, 2025) Bhowmik, B.; Dongala, J.R.; Sudhama, K.K.; Antony, R.T.; Girish, K.K.The assimilation of technology into the financial sector, often referred to as FinTech, has brought about a significant transformation. This shift has not only widened the scope of financial inclusivity but has also fundamentally reshaped the contours of financial solutions delivered. As FinTech solutions continue to empower individuals with greater control over their finances through mobile banking, digital wallets, and advanced data analytics, the security of these innovations becomes paramount. While software security has traditionally received more attention, this paper underscores the significance of hardware security, which serves as the foundational infrastructure for software security measures. It delves into the factors used to evaluate hardware security and outlines various categories of hardware attacks. A case study, focusing on point-of-sale (PoS) systems, exemplifies the importance of hardware security in FinTech. Ultimately, this research contributes to a comprehensive understanding of the evolving FinTech landscape and its implications for both financial inclusion and cybersecurity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Classification of Phishing Email Using Word Embedding and Machine Learning Techniques(River Publishers, 2022) Somesha, M.; Pais, A.R.Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used. © 2022 River Publishers.Item RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification(Institute of Electrical and Electronics Engineers Inc., 2024) S, S.; Lal, S.; Pratap Singh, M.; Raghavendra, B.S.Malware analysis and detection are still essential for maintaining the security of networks and computer systems, even as the threat landscape shifts. Traditional approaches are insufficient to keep pace with the rapidly evolving nature of malware. Artificial Intelligence (AI) assumes a significant role in propelling its design to unprecedented levels. Various Machine Learning (ML) based malware detection systems have been developed to combat the ever-changing characteristics of malware. Consequently, there is a growing interest in exploring advanced techniques that leverage the power of Deep Learning (DL) to effectively analyze and detect malicious software. DL models demonstrate enhanced capabilities for analyzing extensive sequences of system calls. This paper proposes a Robust Malware Detection Network (RMDNet) for effective malware detection and classification. The proposed RMDNet model branches the input and performs depth-wise convolution and concatenation operations. The experimental results of the proposed RMDNet and existing DL models are evaluated on 48240 malware and binary visualization image dataset with RGB format. Also on the multi-class malimg and dumpware-10 datasets with grayscale format. The experimental results on each of these datasets demonstrate that the proposed RMDNet model can effectively and accurately categorize malware, outperforming the most recent benchmark DL algorithms. © 2013 IEEE.Item Dimensionality reduction using neural networks for lattice-based cryptographic keys(Taylor and Francis Ltd., 2024) Wahlang, R.; Chandrasekaran, K.Post Quantum Cryptography has received an increasing amount of active research. This has been made prominent by the ever-growing field of quantum computing which poses a formidable threat to the modern cybersecurity landscape. Recently, post quantum schemes have been standardized for adoption into existing security services and protocols. In comparison to contemporary cryptographic schemes, these approaches are lagging behind in terms of performance with regards to functional speed and package sizes. A study into the various applications of neural networks used in classical and post quantum cryptographic schemes has been explored to demonstrate their different possible applications within the various fields of cybersecurity. The main contribution of this work is to investigate the feasibility of dimensionality reduction using the autoencoder neural network on lattice-based keys generated by the Kyber key encapsulation mechanism scheme. Moreover, this work also presents a comparative analysis of the different implemented autoencoder models to showcase their relative performance. © 2024 Informa UK Limited, trading as Taylor & Francis Group.