Mr. Jishnu K S

@srmist.edu.in

Assistant Professor, Engineering & Technology
Research Student, Engineering & Technology
Department Of Computing Technologies, School Of Computing,SRM University, Kattankulathur,

Mr. Jishnu K S


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https://researchid.co/jishnuk

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Science Applications, Computer Engineering
11

Scopus Publications

109

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • High-Performance Landslide Susceptibility Mapping via Stacking Ensemble
    Predicting Preventing and Mitigating Natural Disasters Through Advanced Technologies, 2026
  • Deep Learning Strategies for High-Accuracy Oral Cancer Classification: A Focus on Efficientnet
    K. S. Jishnu, Sreya John, Mary Oshin, Abin Sam Tharakan, P. S. Shijukumar, G. Bhargavi
    AI in Clinical Diagnosis Prediction and Patient Care, 2026
    Oral cancer stands as a significant global health issue, necessitating precise diagnosis and timely intervention. Deep learning, notably transfer learning, emerges as a critical tool in computer-aided systems for diagnosing oral malignant lesions research investigates the effectiveness of an EfficientNet- based system in classifying histopathology images of Oral Squamous Cell Carcinoma (OSCC). Our empirical results point out the excellence of EfficientNet, using a dataset of 5,192 photos, which resulted in an accuracy of 97.3%, and precision and recall of 96% and 98%, respectively. This outperforms other fine-tuned models and baseline models. These results show the power of transfer learning, especially through EfficientNet, for the precise classification of oral cancer tumors. Such advances could lead to improved clinical decision-making and better patient outcomes in the management of oral cancer.
  • AI-Driven Music Composition by Integrating RNNs and GAs for Personalized Pop Songs
    K. S. Jishnu, P. S. Shijukumar, G. Bhargavi, Vimal Sankar, P. S. Sujith Kumar, Nisha Thorakattu Madathil
    Artificial Intelligence in Music Production Innovations Practices and Industry Implications, 2026
    An AI-powered music composition framework is introduced, capable of generating complete pop songs by integrating Recurrent Neural Networks (RNNs) with Genetic Algorithms (GAs). RNNs produce melodically coherent MIDI sequences that reflect long-term musical dependencies, while GAs refine song structure based on user preferences such as variation, transition smoothness, and catchiness. Unlike the MAGMA framework, which is limited to instrumental MIDI creation, the proposed system incorporates vocals using a realistic Text-to-Speech (TTS) engine. Lyrics can be user provided or generated through NLP and are precisely synchronized with the MIDI output using tools such as librosa, pydub, and fluidsynth. The implementation uses Python libraries including tensorflow, torch, pretty-midi, music21, pandas, and matplotlib, and a feedback loop allows users to iteratively improve song quality. The framework aims to make high-quality, personalized music production accessible to musicians, content creators, and professionals.
  • Benchmarking Traditional ML Approaches in Phishing URL Detection
    T. S. Sangeetha, Keerthi Jayan, Sreya John, D. Vetriselvi, G. L. Swathi Mirthika, Nisha Thorakkattu Thorakattil Madathil, K. S. Jishnu
    Navigating Public Security in the Age of Post Truth Challenges and Implications, 2026
    Phishing attacks continue to pose a major cybersecurity challenge by exploiting deceptive URLs to obtain sensitive information. Although deep learning approaches such as CNNs, RNNs, and Transformers have demonstrated state of the art detection performance, traditional machine learning classifiers remain widely utilized because of their efficiency,interpretability, and relatively low resource requirements. In this study, we implement and evaluate ten machine learning models including Logistic Regression, Gradient Boosting, CatBoost, XGBoost, and Multi-Layer Perceptron on a publicly available phishing URL dataset from Kaggle. Experimental results show that ensemble based models,particularly XGBoost and Random Forest, achieve the highest accuracy, while Logistic Regression offers competitive performance with the advantages of simplicity, interpretability, and low computational overhead. The findings highlight the tradeoffs between accuracy, interpretability, and computational cost,providing practical guidance for selecting appropriate models in real world phishing detection systems.
  • Generative adversarial network-based phishing URL detection with variational autoencoder and transformer
    Jishnu Kaitholikkal Sasi, Arthi Balakrishnan
    Iaes International Journal of Artificial Intelligence, 2024
    Phishing attacks pose a constant threat to online security, necessitating the development of efficient tools for identifying malicious URLs. In this article, we propose a novel approach to detect phishing URLs employing a generative adversarial network (GAN) with a variational autoencoder (VAE) as the generator and a transformer model with self-attention as the discriminator. The VAE generator is trained to produce synthetic URLs. In contrast, the transformer discriminator uses its self-attention mechanism to focus on the different parts of the input URLs to extract crucial features. Our model uses adversarial training to distinguish between legitimate and phishing URLs. We evaluate the effectiveness of the proposed method using a large set of one million URLs that incorporate both authentic and phishing URLs. Experimental results show that our model is effective, with an impressive accuracy of 97.75%, outperforming the baseline models. This study significantly improves online security by offering a novel and highly accurate phishing URL detection method.
  • Exploring GRU-based approaches with attention mechanisms for accurate phishing URL detection
    Jishnu K S, Arthi B
    Intelligent Decision Technologies, 2024
    In the dynamic realm of digital advancements, the persistent menace of phishing attacks continues to jeopardize the security landscape for both individuals and organizations. As cyber attacks continue to proliferate, URL-based phishing attacks are growing rapidly. This paper presents an exploratory study aimed at enhancing cybersecurity measures through the detection of phishing URLs. Our approach involves exploring the integration of Gated Recurrent Units (GRU) with various attention mechanisms to bolster accuracy in discerning between legitimate and phishing URLs. Notably, our study reveals that the implementation of the Bahdanau attention mechanism with GRU yields remarkable results, achieving an accuracy of 98.14%. We conducted experiments on a comprehensive dataset comprising 95,913 URLs. Our primary objectives include fortifying cybersecurity defenses against phishing threats, innovating through the integration of diverse attention mechanisms with GRU, and substantiating the efficacy of our model through rigorous evaluation metrics. As the realm of cybersecurity confronts escalating challenges, our research not only offers valuable insights but also charts a promising trajectory for future advancements in cybersecurity strategies.
  • Real-time phishing URL detection framework using knowledge distilled ELECTRA
    K. S. Jishnu, B. Arthi
    Automatika, 2024
    The rise of cyber threats, particularly URL-based phishing attacks, has tarnished the digital age despite its unparalleled access to information. These attacks often deceive users into disclosing confidential information by redirecting them to fraudulent websites. Existing browser-based methods, predominantly relying on blacklist approaches, have failed to effectively detect phishing attacks. To counteract this issue, we propose a novel system that integrates a deep learning model with a user-centric Chrome browser extension to detect and alert users about potential phishing URLs instantly. Our approach introduces a Knowledge Distilled ELECTRA model for URL detection and achieves remarkable performance metrics of 99.74% accuracy and a 99.43% F1-score on a diverse dataset of 450,176 URLs. Coupled with the browser extension, our system provides real-time feedback, empowering users to make informed decisions about the websites they visit. Additionally, we incorporate a user feedback loop for continuous model enhancement. This work sets a precedent by offering a seamless, robust, and efficient solution to mitigate phishing threats for internet users.
  • Phishing URL Detection Using BiLSTM With Attention Mechanism
    Jishnu K. S., B. Arthi
    Machine Intelligence Applications in Cyber Risk Management, 2024
    Phishing attacks have emerged as a formidable threat to cyber security, continuously evolving to exploit vulnerabilities in users' online behavior. These attacks often employ deceptive URLs and malicious websites to deceive individuals into divulging sensitive information, making effective detection crucial for safeguarding digital environments. To address this pressing need this approach leverages a deep learning model based on Bidirectional Long Short-Term Memory networks with attention mechanism. A dynamic learning rate scheduling technique is also incorporated to optimize model convergence. Empirical evaluations on a dataset comprising 11,430 URLs demonstrate that this methodology significantly improves Accuracy and robustness in identifying phishing URLs, achieving an impressive accuracy of 96.63%. This substantial enhancement contributes to bolstering cyber security measures. This research equips cyber security practitioners and researchers with practical tools to combat evolving phishing threats in today's digital landscape.
  • Review of the effectiveness of machine learning based phishing prevention systems
    K. S. Jishnu, B. Arthi
    Aip Conference Proceedings, 2023
  • Phishing URL detection by leveraging RoBERTa for feature extraction and LSTM for classification
    Jishnu K S, Arthi B
    Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2023, 2023
    A serious cybersecurity threat is phishing attacks, which use bogus URLs to fool users into disclosing critical information. These attacks affect human vulnerability and potentially result in significant data breaches and financial damages. Phishing attack prevention is vital for protecting individuals and organizations from falling for fraudulent schemes and maintaining internet security. This study introduces a novel method for phishing URL detection employing the RoBERTa transformer-based model for feature extraction and the LSTM for classification. RoBERTa extracts semantic and contextual information from URLs during the feature extraction step. By encoding the URLs into contextualized embeddings, RoBERTa successfully learns to represent the URLs in a way that captures their complicated meaning and surrounding context. The LSTM layer accurately categorizes URLs by capturing their sequential relationships using the collected features. The dataset is extensive, with 3,00,000 URLs. After comprehensive training and testing, the proposed system successfully differentiates between legitimate and phishing URLs with an accuracy of 97.14%. The results highlight the importance of incorporating LSTM for classification and RoBERTa for feature extraction in phishing URL detection. The findings of this study broaden phishing detection techniques and offer practical countermeasures.
  • Enhanced Phishing URL Detection Using Leveraging BERT with Additional URL Feature Extraction
    K S Jishnu, B Arthi
    Proceedings of the 5th International Conference on Inventive Research in Computing Applications Icirca 2023, 2023

RECENT SCHOLAR PUBLICATIONS

  • AI-Driven Music Composition by Integrating RNNs and GAs for Personalized Pop Songs
    KS Jishnu, PS Shijukumar, G Bhargavi, V Sankar, PSS Kumar, ...
    Artificial Intelligence in Music Production: Innovations, Practices, and … , 2026
    2026.0
  • Deep Learning Strategies for High-Accuracy Oral Cancer Classification: A Focus on Efficientnet
    KS Jishnu, S John, M Oshin, AS Tharakan, PS Shijukumar, G Bhargavi
    AI in Clinical Diagnosis, Prediction, and Patient Care, 151-182 , 2026
    2026.0
  • Benchmarking Traditional ML Approaches in Phishing URL Detection
    TS Sangeetha, K Jayan, S John, D Vetriselvi, GLS Mirthika, NTT Madathil, ...
    Navigating Public Security in the Age of Post-Truth: Challenges and … , 2026
    2026.0
  • High-Performance Landslide Susceptibility Mapping via Stacking Ensemble
    KS Jishnu, G Bhargavi, PS Shijukumar, R Pareek, V Monish, A Sundar
    Predicting, Preventing, and Mitigating Natural Disasters Through Advanced … , 2026
    2026.0
  • Phishing URL detection using BiLSTM with attention mechanism
    KS Jishnu, B Arthi
    Machine Intelligence Applications in Cyber-Risk Management, 159-184 , 2025
    2025.0
    Citations: 10
  • Improving Prompt Detection of Oral Cancer: A Transfer Learning-Based OSCC Histopathological Image Classification
    RK Pongiannan, P Harish, K Srivatsan, KS Jishnu, R Brindha, ...
    International Conference on Evolutionary Artificial Intelligence, 579-592 , 2024
    2024.0
    Citations: 6
  • Brain Tumor Classification Using RESNET-50 and Efficientnet-B0
    PU Poornima, S Tamilalagan, D Praveen Raj, RK Pongiannan, KS Jishnu, ...
    International Conference on Evolutionary Artificial Intelligence, 27-39 , 2024
    2024.0
    Citations: 6
  • Real-time phishing URL detection framework using knowledge distilled ELECTRA
    KS Jishnu, B Arthi
    Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i … , 2024
    2024.0
    Citations: 13
  • Exploring GRU-based approaches with attention mechanisms for accurate phishing URL detection
    J KS
    Intelligent Decision Technologies 18 (2), 1-24 , 2024
    2024.0
    Citations: 4
  • Generative adversarial network-based phishing URL detection with variational autoencoder and transformer
    JK Sasi, A Balakrishnan
    Int J Artif Intell 13 (2), 2165-2172 , 2024
    2024.0
    Citations: 9
  • Salivary and serum interleukin-6: A credible marker for predicting oral leukoplakia and oral squamous cell carcinoma by enzyme-linked immunosorbent assay (ELISA)
    M Oshin, PG Kulkarni, G Deepthi, P Kulkari
    Cureus 16 (4) , 2024
    2024.0
    Citations: 13
  • Phishing URL dataset
    JKS Kaitholikkal, B Arthi
    Mendeley Data, Apr 2 , 2024
    2024.0
    Citations: 10
  • Review of the effectiveness of machine learning based phishing prevention systems
    KS Jishnu, B Arthi
    AIP Conference Proceedings 2917 (1), 050006 , 2023
    2023.0
    Citations: 1
  • Phishing URL detection by leveraging RoBERTa for feature extraction and LSTM for classification
    KS Jishnu, B Arthi
    2023 Second International Conference on Augmented Intelligence and … , 2023
    2023.0
    Citations: 21
  • Enhanced phishing URL detection using leveraging BERT with additional URL feature extraction
    KS Jishnu, B Arthi
    2023 5th International Conference on Inventive Research in Computing … , 2023
    2023.0
    Citations: 14
  • BA (2024)
    JKS Kaitholikkal
    Phishing URL dataset , 0
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Phishing URL detection by leveraging RoBERTa for feature extraction and LSTM for classification
    KS Jishnu, B Arthi
    2023 Second International Conference on Augmented Intelligence and … , 2023
    2023.0
    Citations: 21
  • Enhanced phishing URL detection using leveraging BERT with additional URL feature extraction
    KS Jishnu, B Arthi
    2023 5th International Conference on Inventive Research in Computing … , 2023
    2023.0
    Citations: 14
  • Real-time phishing URL detection framework using knowledge distilled ELECTRA
    KS Jishnu, B Arthi
    Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i … , 2024
    2024.0
    Citations: 13
  • Salivary and serum interleukin-6: A credible marker for predicting oral leukoplakia and oral squamous cell carcinoma by enzyme-linked immunosorbent assay (ELISA)
    M Oshin, PG Kulkarni, G Deepthi, P Kulkari
    Cureus 16 (4) , 2024
    2024.0
    Citations: 13
  • Phishing URL detection using BiLSTM with attention mechanism
    KS Jishnu, B Arthi
    Machine Intelligence Applications in Cyber-Risk Management, 159-184 , 2025
    2025.0
    Citations: 10
  • Phishing URL dataset
    JKS Kaitholikkal, B Arthi
    Mendeley Data, Apr 2 , 2024
    2024.0
    Citations: 10
  • Generative adversarial network-based phishing URL detection with variational autoencoder and transformer
    JK Sasi, A Balakrishnan
    Int J Artif Intell 13 (2), 2165-2172 , 2024
    2024.0
    Citations: 9
  • Improving Prompt Detection of Oral Cancer: A Transfer Learning-Based OSCC Histopathological Image Classification
    RK Pongiannan, P Harish, K Srivatsan, KS Jishnu, R Brindha, ...
    International Conference on Evolutionary Artificial Intelligence, 579-592 , 2024
    2024.0
    Citations: 6
  • Brain Tumor Classification Using RESNET-50 and Efficientnet-B0
    PU Poornima, S Tamilalagan, D Praveen Raj, RK Pongiannan, KS Jishnu, ...
    International Conference on Evolutionary Artificial Intelligence, 27-39 , 2024
    2024.0
    Citations: 6
  • Exploring GRU-based approaches with attention mechanisms for accurate phishing URL detection
    J KS
    Intelligent Decision Technologies 18 (2), 1-24 , 2024
    2024.0
    Citations: 4
  • BA (2024)
    JKS Kaitholikkal
    Phishing URL dataset , 0
    Citations: 2
  • Review of the effectiveness of machine learning based phishing prevention systems
    KS Jishnu, B Arthi
    AIP Conference Proceedings 2917 (1), 050006 , 2023
    2023.0
    Citations: 1
  • AI-Driven Music Composition by Integrating RNNs and GAs for Personalized Pop Songs
    KS Jishnu, PS Shijukumar, G Bhargavi, V Sankar, PSS Kumar, ...
    Artificial Intelligence in Music Production: Innovations, Practices, and … , 2026
    2026.0
  • Deep Learning Strategies for High-Accuracy Oral Cancer Classification: A Focus on Efficientnet
    KS Jishnu, S John, M Oshin, AS Tharakan, PS Shijukumar, G Bhargavi
    AI in Clinical Diagnosis, Prediction, and Patient Care, 151-182 , 2026
    2026.0
  • Benchmarking Traditional ML Approaches in Phishing URL Detection
    TS Sangeetha, K Jayan, S John, D Vetriselvi, GLS Mirthika, NTT Madathil, ...
    Navigating Public Security in the Age of Post-Truth: Challenges and … , 2026
    2026.0
  • High-Performance Landslide Susceptibility Mapping via Stacking Ensemble
    KS Jishnu, G Bhargavi, PS Shijukumar, R Pareek, V Monish, A Sundar
    Predicting, Preventing, and Mitigating Natural Disasters Through Advanced … , 2026
    2026.0