2023 Ph.D (Pursuing) in Presidency University, Bangalore
2008-2010 - M.Tech in Information and Communication Technology in Sri Jayachamarajendra College of Engineering, Mysuru
2001-2005 - BE - Computer Science and Engineering in Hindusthan College of Engineering and Technology, Coimbatore
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Computer Science, Artificial Intelligence, Computer Networks and Communications
9
Scopus Publications
11
Scholar Citations
2
Scholar h-index
Scopus Publications
Generative AI-based Phishing Text Generation Using Hybrid Prompt Design with Heuristic Algorithm for Multimodal Phishing Detection International Journal of Intelligent Engineering and Systems, 2025 In the real world, phishing attacks utilize vulnerabilities in automated systems and human behavior to pose serious security risks.Nevertheless, traditional detection systems have several shortcomings and challenges, including identifying the intricate and deceptive phishing emails that exploit generative intelligence and Uniform Resource Locator (URL) manipulation.Thus, this work addresses the gaps in traditional phishing detection systems against the evolving phishing tactics by developing a unique framework for Large Language Model (LLM)-Guided Phishing Text Generation and Detection.Initially, the proposed approach utilizes generative Artificial Intelligence (AI) to generate intelligent phishing emails using hybrid prompt engineering with prompt chaining and evaluation of a metric-aware genetic algorithm to enhance the phishing email content.Then, the URL tabular data for multimodal detection is constructed.Email data is enriched using the Genetic Algorithm (GA) to improve detection abilities, which selects highly relevant and coherent phishing descriptions through an optimization process for email enrichment.Extracting the phishing behavior-influential features from the URL structure precisely enforces multimodal phishing detection.In subsequence, by jointly learning the email content and structured URL data, the proposed approach enhances the precision of phishing detection, designed with the cross-attention associated multimodal transformer architecture.From the extensive experimental evaluations, the proposed approach yields a balanced score of recall as 96.47% and an accuracy of 96.91% by the generative phishing knowledge, accomplished by the prompt-based generation, LLM, heuristic intelligence, and URL tabular feature extraction in the phishing detection system.Finally, the results demonstrate the proposed system's effectiveness in recognizing diverse phishing texts while testing AI-enriched text.Compared to baseline models, it addresses challenges in dynamic threats and multimodal dependencies through generative AI and cross-attention transformer, respectively, ensuring robust phishing detection in diverse environments.
Enhancing Cybersecurity in Financial Systems Using a Stacked Ensemble Approach with Gradient Boosting and SVM Shammi L, Kanchana R, Chiranth M V, Sadhvi 2025 IEEE 2nd International Conference on Information Technology Electronics and Intelligent Communication Systems Iciteics 2025, 2025 Cybersecurity breaches in financial systems are becoming frequent and complicated, but conventional detection techniques are failing to adapt to changing attack patterns and imbalanced datasets. The proposed study handles the significant issue of real-time fraud and threat detection in digital finance using a stacked ensemble learning approach. The proposed system integrates Gradient Boosting methodologies specifically XGBoost, LightGBM, and CatBoost with a Support Vector Machine (SVM) to identify nonlinear and high-dimensional patterns in financial transactions. A Logistic Regression meta-learner is utilised to combine predictions from foundational models, improving generalisation and reducing overfitting. Preprocessing methods including SMOTE, entropy-based features, and SHAP explainability modules are utilised to enhance interpretability and model resilience. Comprehensive experimentation on benchmark Kaggle datasets demonstrates the proposed model's exceptional performance, attaining an accuracy of 99.31 %, precision of 98.94%, recall of 99.21 %, Fl-score of 99.07%, and AUC-ROC of 0.9987. The stacked ensemble presents significantly better data compared to baseline models such as Random Forest, SVM, and individual boosting algorithms, proving it very appropriate for scalable implementation in payment systems and fraud analytics. The proposed study presents an innovative, comprehensible, and extremely efficient approach to intelligent financial threat identification.
The Role of Machine Learning in Threat Detection System L. Shammi, Sujatha Kamepalli, Radha Ranjan, Manu Vasudevan Unni, J. A. Baskar, V. Bhoopathy Deep Learning Innovations for Securing Critical Infrastructures, 2025 Conventional detection approaches frequently fall behind the ever-changing complexity and frequency of cybersecurity threats. The use of machine learning (ML) has revolutionized threat detection in many different fields, including physical security systems, fraud detection, and network security. This chapter explores the use of ML models for threat identification, prediction, and mitigation, shedding light on important techniques, problems, and practical applications. It delves further into the topic by looking at potential trends, ethical concerns, and the necessity of a comprehensive strategy to protect vital systems and data. This chapter provides real-world examples of ML's application in threat detection. It explains the process and the challenges of these strategies. More and more, ML is being used in security contexts, which raises serious concerns about data privacy, model bias, and explainability. Emerging concepts such as federated learning, explainable AI, and edge computing are covered in this chapter, along with future advancements in ML-driven threat detection.
A Comprehensive Analysis on Multi-Layered Machine Learning Approaches for Detecting and Preventing Phishing in Email and Websites 15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
Improved Generative Adversarial Network for Phishing Attack Detection Shammi L, C Emilin Shyni Proceedings 2024 4th International Conference on Pervasive Computing and Social Networking Icpcsn 2024, 2024 A general form of attack that is happening over the internet is called phishing which could lead to identity theft and financial damages. Due to the increase of online electronic services and payment systems, the demand for accurate phishing detection tools has risen in recent times. However, the models often lead to high false detection rates. This research work introduces an Improved Generative Adversarial Network-based phishing attack detection, which has mainly two stages such as preprocessing and attack detection. Initially, for data preprocessing, a min-max normalization process is used. Following that, the attack detection process is done via Improved GAN, where a new discriminator loss function is adopted to enhance the detection performance. Finally, the performance of the proposed work is validated in terms of different performance measures.
A Novel Approach for Effective Detection and Prediction of Sophisticated Cyber Attacks Using the Stacked Attention GRU and BiLSTM Shammi L, C Emilin Shyni 1st International Conference on Electronics Computing Communication and Control Technology Iceccc 2024, 2024 The dynamic and sophisticated cyber threats of today's quickly expanding cybersecurity landscape are surpassing the effectiveness of traditional security solutions. Organizations must take a proactive stance in danger identification and mitigation if they are to successfully tackle these new difficulties. Threat intelligence (TI), or the real-time sharing of cyber threat data, is crucial for both proactive defense and quick reaction to cyberattacks. This study describes a comprehensive approach that includes feature selection, data preparation, and model training for threat intelligence-based cyber security. Normalization and standardization are two preprocessing methods that maximize data representation for efficient analysis. Principal component analysis is one of the feature selection techniques used to find the most pertinent variables for model training. A Stacked BiLSTM-A-GRU model is trained with careful respect to feature selection, guaranteeing optimal performance. A comparative examination shows that the suggested method outperforms the most advanced algorithms, with a remarkable accuracy rate of 98.71% This study emphasizes how crucial it is to use threat intelligence to have a strong cybersecurity defense against ever-changing threats.
Fraud Detection in Accounting and Finance Enhanced by Knowledge-Driven GAT Networks L Shammi, C. Emilin Shyni, S Vinayagam, S Aravindh, J Steephan Amalraj 2024 1st International Conference on Software Systems and Information Technology Ssitcon 2024, 2024 Accounting fraud detection (FAFD) has recently emerged as a popular topic in academic circles, industry, and government agencies as a direct result of the correlation between the present economic situation and an increase in financial accounting fraud. Forensic accountants bring their specialized knowledge to the table to help unearth financial accounting crimes when internal auditing systems fail to detect red flags. This method entails three stages: feature selection, model training, and preprocessing. Business organizations’ financial statements, whether fraudulent or not, as well as letters to shareholders and financial news, are pre-processed using the CKIP system. In this proposed approach to use the simplest statistical technique during the features selection phase. Throughout training, it made use of a knowledge GAT. This innovative method outperforms Knowledge Graph and Attention Network, with an average accuracy of 95.43%.
Safeguarding E Commerce and Preventing Financial Fraud with AdaBoost CNN Cybersecurity Approach V. Malathi, Shammi L, Manu Vasudevan Unni, Akabarsaheb Babulal Nadaf, S. Rukmani Devi, Thangam E International Conference on Distributed Systems Computer Networks and Cybersecurity Icdscnc 2024, 2024 Examining the cyber security risks faced by companies and e-commerce platforms is the focus of this research. People in the corporate world and those in academia are curious about the technology applications of e-commerce. There are now possibilities that were before unimaginable for both businesses and consumers. On the other hand, there were certain issues with its arrival, chief among them being cyber security. Among the many forms of cybercrime, this study focuses on social engineering, denial-of-service attacks, malware, and data breaches. This proposed approach consists of three phases, which are feature extraction, model training, and data preprocessing. Addressing missing values, normalizing features, and eliminating noise are all part of preprocessing, which aims to clean and organize raw data. When dealing with multivariate data, principal component analysis (PCA) is a common tool for feature extraction. Throughout the training process, we employed an AdaBoost-CNN. With an average accuracy of 90.65%, this innovative technique surpasses CNN and AdaBoost
Securing Biometric Data with Optimized Share Creation and Visual Cryptography Technique Shammi L, Milind, C. Emilin Shyni, Khair Ul Nisa, Ravi Kumar Bora, S. Saravanan 6th International Conference on Electronics Communication and Aerospace Technology Iceca 2022 Proceedings, 2022 Biometric security is the fastest growing area that receives considerable attention over the past few years. Digital hiding and encryption technologies provide an effective solution to secure biometric information from intentional or accidental attacks. Visual cryptography is the approach utilized for encrypting the information which is in the form of visual information for example images. Meanwhile, the biometric template stored in the databases are generally in the form of images, the visual cryptography could be employed effectively for encrypting the template from the attack. This study develops a share creation with improved encryption process for secure biometric verification (SCIEP-SBV) technique. The presented SCIEP-SBV technique majorly aims to attain security via encryption and share creation (SC) procedure. Firstly, the biometric images undergo SC process to produce several shares. For encryption process, homomorphic encryption (HE) technique is utilized in this work. To further improve the secrecy, an improved bald eagle search (IBES) approach was exploited in this work. The simulation values of the SCIEP-SBV system are tested on biometric images. The extensive comparison study demonstrated the improved outcomes of the SCIEP-SBV technique over compared methods.
RECENT SCHOLAR PUBLICATIONS
Enhancing Cybersecurity in Financial Systems Using a Stacked Ensemble Approach with Gradient Boosting and SVM L Shammi, R Kanchana, MV Chiranth 2025 IEEE 2nd International Conference on Information Technology … , 2025 2025
Safeguarding E Commerce and Preventing Financial Fraud with AdaBoost CNN Cybersecurity Approach V Malathi, S L, MV Unni, AB Nadaf, SR Devi, T E 2024 International Conference on Distributed Systems, Computer Networks and … , 2025 2025 Citations: 2
Generative AI-based Phishing Text Generation Using Hybrid Prompt Design with Heuristic Algorithm for Multimodal Phishing Detection S L International Journal of Intelligent Engineering and Systems 18 (2), 485-503 , 2025 2025
The Role of Machine Learning in Threat Detection System L Shammi, S Kamepalli, R Ranjan, MV Unni, JA Baskar, V Bhoopathy Deep Learning Innovations for Securing Critical Infrastructures, 333-348 , 2025 2025
Cybercrime Analysis and Online Fraud Prevention Using Advanced Machine Learning Models S L Journal of Informatics Education and Research 5 (1), 1534-1543 , 2025 2025
Fraud Detection in Accounting and Finance Enhanced by Knowledge-Driven GAT Networks L Shammi, CE Shyni, S Vinayagam, S Aravindh, JS Amalraj 2024 First International Conference on Software, Systems and Information … , 2024 2024 Citations: 2
Improved generative adversarial network for phishing attack detection L Shammi, CE Shyni 2024 4th International Conference on Pervasive Computing and Social … , 2024 2024 Citations: 3
A Novel Approach for Effective Detection and Prediction of Sophisticated Cyber Attacks using the Stacked Attention GRU and BiLSTM L Shammi, CE Shyni 2024 International Conference on Electronics, Computing, Communication and … , 2024 2024 Citations: 4
Managing and Evaluating Data Integrity in machine Learning-Based Cyber Intrusion Detection L Shammi, PS Krishnakant, YS Varsha, R Kumar 2024
Generative Adversarial Networks for Malware Detection in Cloud Computing Environments L Shammi, H Kousar 2024
A Comprehensive Analysis on Multi-Layered Machine Learning Approaches for Detecting and Preventing Phishing in Email and Websites S L 15th International Conference on Advances in Computing, Control, and … , 2024 2024
Securing Biometric Data with Optimized Share Creation and Visual Cryptography Technique L Shammi, CE Shyni, KU Nisa, RK Bora, S Saravanan 2022 6th International Conference on Electronics, Communication and … , 2022 2022
MOST CITED SCHOLAR PUBLICATIONS
A Novel Approach for Effective Detection and Prediction of Sophisticated Cyber Attacks using the Stacked Attention GRU and BiLSTM L Shammi, CE Shyni 2024 International Conference on Electronics, Computing, Communication and … , 2024 2024 Citations: 4
Improved generative adversarial network for phishing attack detection L Shammi, CE Shyni 2024 4th International Conference on Pervasive Computing and Social … , 2024 2024 Citations: 3
Safeguarding E Commerce and Preventing Financial Fraud with AdaBoost CNN Cybersecurity Approach V Malathi, S L, MV Unni, AB Nadaf, SR Devi, T E 2024 International Conference on Distributed Systems, Computer Networks and … , 2025 2025 Citations: 2
Fraud Detection in Accounting and Finance Enhanced by Knowledge-Driven GAT Networks L Shammi, CE Shyni, S Vinayagam, S Aravindh, JS Amalraj 2024 First International Conference on Software, Systems and Information … , 2024 2024 Citations: 2
Enhancing Cybersecurity in Financial Systems Using a Stacked Ensemble Approach with Gradient Boosting and SVM L Shammi, R Kanchana, MV Chiranth 2025 IEEE 2nd International Conference on Information Technology … , 2025 2025
Generative AI-based Phishing Text Generation Using Hybrid Prompt Design with Heuristic Algorithm for Multimodal Phishing Detection S L International Journal of Intelligent Engineering and Systems 18 (2), 485-503 , 2025 2025
The Role of Machine Learning in Threat Detection System L Shammi, S Kamepalli, R Ranjan, MV Unni, JA Baskar, V Bhoopathy Deep Learning Innovations for Securing Critical Infrastructures, 333-348 , 2025 2025
Cybercrime Analysis and Online Fraud Prevention Using Advanced Machine Learning Models S L Journal of Informatics Education and Research 5 (1), 1534-1543 , 2025 2025
Managing and Evaluating Data Integrity in machine Learning-Based Cyber Intrusion Detection L Shammi, PS Krishnakant, YS Varsha, R Kumar 2024
Generative Adversarial Networks for Malware Detection in Cloud Computing Environments L Shammi, H Kousar 2024
A Comprehensive Analysis on Multi-Layered Machine Learning Approaches for Detecting and Preventing Phishing in Email and Websites S L 15th International Conference on Advances in Computing, Control, and … , 2024 2024
Securing Biometric Data with Optimized Share Creation and Visual Cryptography Technique L Shammi, CE Shyni, KU Nisa, RK Bora, S Saravanan 2022 6th International Conference on Electronics, Communication and … , 2022 2022
