Machine Learning-Driven Intrusion Detection for Next-Generation Information Security Systems Rabins Porwal, Manoj Singh Adhikari, Keerthi S, Anil Kumar Yadav, Mahesh Babu Ketha, et al. International Research Journal of Multidisciplinary Technovation, 2026 The proliferation of cloud, IoT, edge, and 5G infrastructures has dramatically expanded the attack surface of modern networks, while many existing intrusion detection systems (IDS) remain centralized, poorly interpretable, and brittle to concept drift and adversarial manipulation. Traditional machine learning-based IDS architectures demand centralization of raw data, offer limited decision transparency, degrade as traffic distributions evolve, and scale poorly to privacy-sensitive and resource-constrained deployments. In this paper, the Adaptive Explainable Federated Intrusion Detection System (AEF-IDS) is proposed, incorporating privacy-preserving federated learning, Kolmogorov-Smirnov (KS) test-based drift detection, differential privacy, adversarial robustness training, and multi-level explainability within a unified edge-oriented framework. Evaluated on three widely adopted benchmarks, namely NSL-KDD, UNSW-NB15, and CIC-IDS2018, AEF-IDS achieves detection accuracies of 96.74%, 93.92%, and 95.87%, false positive rates of 1.68%, 2.61%, and 2.19%, and AUC-ROC scores of 0.9781, 0.9573, and 0.9683, respectively. The system satisfies strict real-time performance requirements, achieving per-sample inference latencies of 47.3, 44.8, and 46.1 ms across the three benchmarks, all within the 50 ms operational threshold. AEF-IDS further demonstrates high resilience against white-box adversarial attacks, including FGSM, PGD, C&W, and DeepFool, maintaining a mean under-attack detection accuracy exceeding 88% across all evaluated datasets. Through federated optimization and KS-triggered adaptive retraining, the system effectively mitigates distributional shift while preserving local data sovereignty, and SHAP/LIME-based explanations provide both global and local attribution transparency for security analysts. These results collectively demonstrate that AEF-IDS constitutes a robust, privacy-preserving, and interpretable solution for next-generation IDS deployment at the network edge. Future work will address cross-domain generalization, online hyperparameter adaptation, and large-scale real-world field validation.
A Federated Learning–Enabled Secure and Scalable SDN Framework for Energy-Efficient VANETs S. Sathishkumar, S. Keerthi, R. Devi Priya, Sivanantham S International Journal of Communication Systems, 2026 Vehicular ad hoc networks (VANETs) play a central role in intelligent transportation systems (ITSs), yet their performance is often constrained by high mobility, limited scalability, privacy risks, and increasing security threats, including quantum‐enabled attacks. Existing approaches typically integrate software‐defined networking (SDN), federated learning (FL), blockchain, or cryptographic techniques in isolation, resulting in fragmented control and inconsistent security guarantees. To overcome these limitations, this work proposes FLEDGE‐SDVN , a unified FL‐enabled, blockchain‐secured, and postquantum‐resilient SDN framework for secure and energy‐efficient VANET operation. The framework employs dynamic hierarchical clustering for stable topology management and incorporates an FL‐based trust model to detect malicious vehicles without sharing raw data. A lightweight blockchain ensures tamper‐resistant maintenance of trust records, while Kyber‐ and Dilithium‐based postquantum cryptography provides long‐term secure authentication. SDN controllers enforce global routing policies, optimize traffic flow, and support adaptive decision‐making. Extensive NS‐3 and SUMO simulations demonstrate that FLEDGE‐SDVN significantly enhances network performance compared to CRAS‐FL, EECT, and DistB‐VNET. Specifically, the framework improves packet delivery ratio by 10%–22%, reduces end‐to‐end delay by 13%–28%, enhances energy efficiency by 17%–31%, and achieves trust accuracy up to 96%. These results confirm that FLEDGE‐SDVN offers a scalable, secure, and future‐ready solution for next‐generation vehicular communication systems.
Forensic Audit Trails and Biometric-Based Authentication Deepak Gupta, Raghu Nangunuri, Srinivasan Nagaraj, S. Keerthi, Pratish Rawat, et al. Exploring the Intersection of Forensics and Biometrics, 2026 Forensic audit trails combined with biometric-based authentication represent a critical convergence in modern cybersecurity infrastructure. This chapter examines the technical implementation, forensic methodologies, and investigative frameworks for biometric authentication systems. The integration of immutable audit trails with biometric verification creates comprehensive forensic evidence chains essential for digital investigations. We analyze forensic challenges including spoofing attacks, presentation attacks, and biometric template security. The chapter explores multimodal biometric systems, liveness detection mechanisms, and blockchain-based audit trail implementations. Critical examination of privacy-preserving forensic techniques, GDPR compliance, and admissibility of biometric evidence in legal proceedings provides practical guidance. Real-world case studies demonstrate forensic analysis of compromised biometric systems.
Adversarial Attacks on 6G Networks - A Survey Keerthi S, Anitha M, Adith Rao, Advaith P. N., Akash Rajesh Nair, et al. 2025 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2025 Proceedings, 2025
NyxVigil: FPGA based Night Vision Spy Bot S Keerthi, D Pratheesha, Sanjana Bharadvaj, Santhosh Yadav, K Shashi Raj Proceedings 2024 5th International Conference on Image Processing and Capsule Networks Icipcn 2024, 2024 This research study intends to provide an innovative approach for image processing on Field Programmable Gate Array (FPGA) platforms to enable remote monitoring of robotic systems. Leveraging the capabilities of FPGA technology, this study proposes a solution to efficiently handle complex image processing tasks, including obstacle detection and environmental monitoring. By utilizing the FPGA-based hardware acceleration, the processing speed and resource utilization are significantly increased, guaranteeing prompt and precise image analysis. Additionally, the proposed system integrates with robotic vehicles equipped with sensors and cameras to enable continuous monitoring of remote or hazardous environments. Experimental results demonstrate the effectiveness and scalability of the FPGA-based approach, highlighting its potential for various applications in robotics, surveillance, and environmental monitoring. Overall, a useful solution for improving robotic systems’ remote monitoring capabilities is provided, along with a contribution to the field of image processing on FPGA platforms.
A Review on Brain Tumor Prediction using Deep Learning S Keerthi, Yukta N Shettigar, K Keerthana, K R Divyashree, S Bhargavi 2023 International Conference on Advancement in Computation and Computer Technologies Incacct 2023, 2023 Detection and segmentation of brain tumors is important in the healthcare domain. Since brain tumors can possibly lead to cancer, it is a crucial task to detect it early through Magnetic Resonance Imaging (MRI) or Computed Tomography (CT), which are the techniques that use radio waves and magnetic fields to present a detailed view of the body organs. The images obtained from the MRI makes it hard to locate the exact position of the tumor and hence it is a challenging task to detect the tumor accurately. Thus, computer-aided methods (segmentation, detection and classification processes) with better accuracy are required for early tumor diagnosis. The segmentation of brain tumor which is usually carried out manually by the radiologists through their expertise and skill is a highly prolonged task and there can be chances of some faulty predictions, hence, the semantic segmentation is proven to be an effective method to overcome this problem. Semantic segmentation method is applied to brain tumors which are automatically segmented with the aid of deep learning techniques (CNN, RNN, GAN, LSTMs, etc.). The usage of deep learning techniques with greater accuracy and robustness are proven to be effective for the precise diagnosis of brain tumor. The primary objective of this paper is to examine the previously published techniques using deep learning for the human brain tumor prediction.
RFID based Smart Traffic System for Emergency Vehicles Sathya D, C. Vinothini, S. Keerthi, Jagadeesan D, Nidhishree M S 2022 6th International Conference on Trends in Electronics and Informatics Icoei 2022 Proceedings, 2022 The traffic congestion problem is an issue that has had a significant influence on the country's transportation system. This creates a slew of issues particularly when there are emergencies at traffic signal junctions, which are constantly congested. These issues are addressed by a traffic light control system. This system was created to run when it received a signal from an emergency vehicle through radio frequency (RFID) transmission and utilized the Programmable Integrated Circuit Arduino microcontroller to return the sequence to normal before the emergency mode was activated. Because another vehicle had to follow a particular path to the emergency car, this technique will decrease accidents that occur often at traffic signal crossings. As a consequence, the project successfully studied and implemented wireless communication in the traffic light control system for emergency vehicles, as well as radio frequency (RFID) transmission. When emergency vehicles pass by a junction, the prototype of this project uses the 434 MHz frequency and operates with the sequence mode of a traffic light, shifting the sequence back to the usual sequence before the emergency mode is activated. This prototype system can be enhanced in the future by regulating real-world traffic conditions, effectively upgrading current traffic signal system technology.
RECENT SCHOLAR PUBLICATIONS
A Federated Learning–Enabled Secure and Scalable SDN Framework for Energy‐Efficient VANETs S Sathishkumar, S Keerthi, R Devi Priya, S S International Journal of Communication Systems 39 (4), e70409 , 2026 2026 Citations: 1
Forensic Audit Trails and Biometric-Based Authentication D Gupta, R Nangunuri, S Nagaraj, S Keerthi, P Rawat, C Umarani, S Siddi Exploring the Intersection of Forensics and Biometrics, 31-60 , 2026 2026
Adversarial Attacks on 6G Networks-A Survey S Keerthi, M Anitha, PN Advaith 2025 IEEE International Conference on Distributed Computing, VLSI … , 2025 2025
Invasive treatment strategy for older patients with non-ST-elevation acute coronary syndrome: a systematic review and meta-analysis of randomized controlled trials V Vats, RD Shahjehan, BS Kumar, K Sanapala, K Mittal, CAB Herazo, ... Frontiers in Cardiovascular Medicine 12, 1638932 , 2025 2025
A Review on Autism Spectrum Disorder Diagnosis using MRI data S Ranjan, MJ Keerthi, D Gowda, S Shet, S Ramesh 2025
Antioxidant & Antimicrobial Study of Ayurvedic Herbal Formulation: Kalyana Avaleha PN Bhirdi, A Lingayat, P Jyothirmai, S Kumar, E Metia, SV Keerthi REDVET-Revista electrónica de Veterinaria 25 (1), 2024 , 2025 2025
NyxVigil: FPGA based Night Vision Spy Bot S Keerthi, D Pratheesha, S Bharadvaj, S Yadav, KS Raj 2024 5th International Conference on Image Processing and Capsule Networks … , 2024 2024
A review on brain tumor prediction using deep learning S Keerthi, YN Shettigar, K Keerthana, KR Divyashree, S Bhargavi 2023 International Conference on Advancement in Computation & Computer … , 2023 2023 Citations: 8
A Review on DermAI Skin Recognition PS S.Keerthi Arnav Shrivastava 2023 International Conference on Advancement in Computation & Computer … , 2023 2023
A Systematic Literature Survey on Brain Tumour Detection From MRI Images PIKS 1Rohith M V, 2Palguni R H, 3Pavan Journal For Basic Sciences 23 (2), 581-591 , 2023 2023
Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine PS S. Keerthi1,* Intelligent Automation & Soft Computing 2023 36 (1173-1188), 1174 , 2022 2022 Citations: 8
A Literature Survey on Vaccine safe Health Tracker based on blockchain technology PKS Vishalkumar Mandal, Hardik Singh, Abhishek International Research Journal of Engineering and Technology (IRJET) 9 (06 … , 2022 2022
RFID based smart traffic system for emergency vehicles D Sathya, C Vinothini, S Keerthi, D Jagadeesan, MS Nidhishree 2022 6th International Conference on Trends in Electronics and Informatics … , 2022 2022 Citations: 4
A Higher-Level Security Scheme for Key Access ON Cloud Computing A Patra, S Verma, S Kumar, S Keerthi 2022
A Data Destruction in Cloud Based Multi-Tenant Database Deduplicatable S Keerthi Indian Journal of Natural Sciences 8 (47), 13338-13341 , 2018 2018
ONLINE USER’S BEHAVIOR DATA PRESERVING USING RSA BASED SELECTIVE AGGREGATION MN S. Keerthi, M. Mahita,J. Nandhini Devi International Journal of Pure and Applied Mathematics 118 (20), 1314-3395 , 2018 2018
ONLINE USER’S BEHAVIOR DATA PRESERVING USING RSA BASED SELECTIVE AGGREGATION MN S. Keerthi, M. Mahita,J. Nandhini Devi International Journal of Pure and Applied Mathematics 118 (20), 2347-2352 , 2018 2018
EFFICIENT DATA ACCESS AUTHORITY FOR MULTI DATA USERS AUTHENTICATION USING CLOUD SERVER S Keerthi, M Kandasamy, B Meiarasu, M Mohanraj 2018
Classification of tooth type from dental X-ray image using projection profile analysis KM Keerthana, B Rajeshwari, S Keerthi, HP Menon 2017 International Conference on Signal Processing and Communication (ICSPC … , 2017 2017 Citations: 9
An Optimistic Approach for Diagnosing Brain Tumor from MRI Image Using RVM and Histogram Based Segmentation DS Keerthi S Journal of Chemical and Pharmaceutical Sciences, 87 , 2017 2017
MOST CITED SCHOLAR PUBLICATIONS
Comparison of RVM and SVM Classifier Performance in Analysing the Tuberculosis in Chest X Ray S Keerthi, S Dhivya International Journal of Control theory and Applications 10 (36), 269-276 , 2017 2017 Citations: 15
Classification of tooth type from dental X-ray image using projection profile analysis KM Keerthana, B Rajeshwari, S Keerthi, HP Menon 2017 International Conference on Signal Processing and Communication (ICSPC … , 2017 2017 Citations: 9
A review on brain tumor prediction using deep learning S Keerthi, YN Shettigar, K Keerthana, KR Divyashree, S Bhargavi 2023 International Conference on Advancement in Computation & Computer … , 2023 2023 Citations: 8
Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine PS S. Keerthi1,* Intelligent Automation & Soft Computing 2023 36 (1173-1188), 1174 , 2022 2022 Citations: 8
RFID based smart traffic system for emergency vehicles D Sathya, C Vinothini, S Keerthi, D Jagadeesan, MS Nidhishree 2022 6th International Conference on Trends in Electronics and Informatics … , 2022 2022 Citations: 4
Automation of Lab with Attendance Monitoring, Screen Capturing and Performance Analysis PR Kanna, S Keerthi International Journal of Pure and Applied Mathematics 118 (18), 2765-2770 , 2017 2017 Citations: 4
A Federated Learning–Enabled Secure and Scalable SDN Framework for Energy‐Efficient VANETs S Sathishkumar, S Keerthi, R Devi Priya, S S International Journal of Communication Systems 39 (4), e70409 , 2026 2026 Citations: 1
Location Based Image Retrieval System on Ranking User Clicks PR Kanna, S Keerthi Indian Journal of Natural Sciences 8 (47), 13426-13429 , 2017 2017 Citations: 1
Forensic Audit Trails and Biometric-Based Authentication D Gupta, R Nangunuri, S Nagaraj, S Keerthi, P Rawat, C Umarani, S Siddi Exploring the Intersection of Forensics and Biometrics, 31-60 , 2026 2026
Adversarial Attacks on 6G Networks-A Survey S Keerthi, M Anitha, PN Advaith 2025 IEEE International Conference on Distributed Computing, VLSI … , 2025 2025
Invasive treatment strategy for older patients with non-ST-elevation acute coronary syndrome: a systematic review and meta-analysis of randomized controlled trials V Vats, RD Shahjehan, BS Kumar, K Sanapala, K Mittal, CAB Herazo, ... Frontiers in Cardiovascular Medicine 12, 1638932 , 2025 2025
A Review on Autism Spectrum Disorder Diagnosis using MRI data S Ranjan, MJ Keerthi, D Gowda, S Shet, S Ramesh 2025
Antioxidant & Antimicrobial Study of Ayurvedic Herbal Formulation: Kalyana Avaleha PN Bhirdi, A Lingayat, P Jyothirmai, S Kumar, E Metia, SV Keerthi REDVET-Revista electrónica de Veterinaria 25 (1), 2024 , 2025 2025
NyxVigil: FPGA based Night Vision Spy Bot S Keerthi, D Pratheesha, S Bharadvaj, S Yadav, KS Raj 2024 5th International Conference on Image Processing and Capsule Networks … , 2024 2024
A Review on DermAI Skin Recognition PS S.Keerthi Arnav Shrivastava 2023 International Conference on Advancement in Computation & Computer … , 2023 2023
A Systematic Literature Survey on Brain Tumour Detection From MRI Images PIKS 1Rohith M V, 2Palguni R H, 3Pavan Journal For Basic Sciences 23 (2), 581-591 , 2023 2023
A Literature Survey on Vaccine safe Health Tracker based on blockchain technology PKS Vishalkumar Mandal, Hardik Singh, Abhishek International Research Journal of Engineering and Technology (IRJET) 9 (06 … , 2022 2022
A Higher-Level Security Scheme for Key Access ON Cloud Computing A Patra, S Verma, S Kumar, S Keerthi 2022
A Data Destruction in Cloud Based Multi-Tenant Database Deduplicatable S Keerthi Indian Journal of Natural Sciences 8 (47), 13338-13341 , 2018 2018
ONLINE USER’S BEHAVIOR DATA PRESERVING USING RSA BASED SELECTIVE AGGREGATION MN S. Keerthi, M. Mahita,J. Nandhini Devi International Journal of Pure and Applied Mathematics 118 (20), 1314-3395 , 2018 2018
