Computer Networks and Communications, Computer Science, Computer Science Applications
8
Scopus Publications
24
Scholar Citations
3
Scholar h-index
1
Scholar i10-index
Scopus Publications
An optimized Bi-LSTM with deep learning-based intrusion detection system in healthcare using Blockchain Swathi Darla, Naveena C, B N Ajay, Muzammel Ahmed Information Security Journal, 2026 Healthcare service quality has significantly improved due to its integration with the Internet of Things (IoT). Cloud network-based servers provide storage, interaction, and problem-solving facilities here. Nevertheless, these also face cyber security threats and issues. So, Intrusion detection systems (IDS), which enable the detection of a wide range of hostile attempts against network security, are becoming a vital tool in healthcare services now. IDS protect patient health records and medical communications against threats in the network layer. This paper suggests a Deep Blockchain-based IDS (DBC-IDS) for ensuring data security and privacy in cloud networks used by healthcare services. A Hybrid Deep Neural Network with Optimized Bidirectional Long Short-Term Memory (HDNN OBi-LSTM) employs the IDS to identify attacks during network data transfer in cloud systems. To reduce the classification time, a feature vector selection technique based on Adaptive Gray Wolf Optimization (AGWO) generates a collection of feature vectors. Golden Search Optimization is then applied to select the best weights for the hidden layers and elevate the classifier’s Bidirectional Long Short-Term Memory (Bi-LSTM) classifier detection rate. The suggested method achieved an accuracy of 98.25%, demonstrating its superior performance and increased efficiency compared to the existing techniques.
Privacy-Preserving Machine Learning Framework for Secure Healthcare Applications Swathi Darla, Kiran Purushotham, G Pranathi, D. N Navya 5th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2025, 2025 The extensive shift toward digital healthcare technologies has greatly enhanced diagnostic workflows and expanded the availability of personalized medical support. Despite these benefits, they also raise significant concerns about safeguarding sensitive patient records against unauthorized access and cyber-attacks. To tackle these issues, this study introduces a Privacy-Preserving Machine Learning (PPML) framework that ensures secure data handling across storage, transmission, and computation phases. The framework incorporates AES-256 encryption to protect data, JSON Web Token (JWT) authentication for controlled access, and privacy-focused learning techniques such as differential privacy and homomorphic encryption to enable protected model training. The system aligns with healthcare policies including HIPAA and GDPR while preserving usability and interpretability. Experimental analysis indicates that the proposed approach maintains high prediction accuracy with minimal computational load and strong confidentiality safeguards. This research establishes a trusted foundation for secure, privacy-aware healthcare analytics, supporting ethical and reliable adoption of AI technologies in clinical environments.
Artificial Neural Network-Based Predictive Maintenance for Enhancing Smart Grid Stability Maruthiprasad K, Prateek Y B, Ashwini Kodipalli, Trupthi Rao, Gargi N, Swathi Darla Proceedings of IEEE International Conference for Women in Innovation Technology and Entrepreneurship Icwite 2025, 2025 This study explores how artificial neural networks (ANNs) significantly enhance the stability of smart grids through advanced models and optimization techniques. ANNs leverage real-time data from smart sensors, AMI, and PMUs to predict and manage disruptions in the grid, ensuring continuous operations during peak demand and unexpected events. The research highlights AI's role in fortifying grid security and stability amid complex data environments, exploring different Deep learning algorithms and optimization strategies. Notably, the study achieves an 87.31 % accuracy in assessing grid stability, a critical factor in developing sustainable electricity infrastructures. It consolidates insights from previous studies, addressing challenges such as data interpretability and cybersecurity, and proposes future research directions to enhance practical applications and reliability in smart grids. Ultimately, this study leads to the advancement of adaptive and resilient energy infrastructures necessary for future energy systems.
An Optimized Deep Learning Based Malicious Nodes Detection in Intelligent Sensor-Based Systems Using Blockchain Swathi Darla, C. Naveena Journal of Advances in Information Technology, 2023 —In this research work, a blockchain-based secure routing model is proposed for Internet of Sensor Things (IoST), with the assistance acquired from deep learning-based hybrid meta-heuristic optimization model. The proposed model includes three major phases: (a) optimal cluster head selection, (b) lightweight blockchain-based registration and authentication mechanism, (c) optimized deep learning based malicious node identification and (d) optimal path identification. Initially, the network is constructed with N number of nodes. Among those nodes certain count of nodes is selected as optimal cluster head based on the two-fold objectives (energy consumption and delay) based hybrid optimization model. The proposed Chimp social incentive-based Mutated Poor Rich Optimization (CMPRO) Algorithm is the conceptual amalgamation of the standard Chimp Optimization Algorithm (ChOA) and Poor and Rich Optimization (PRO) approach. Moreover, blockchain is deployed on the optimal CHs and base station because they have sufficient storage and computational resources. Subsequently, a lightweight blockchain-based registration and authentication mechanism is undergone. After the authentication of the network, the presence of malicious nodes in the network is detected using the new Optimized Deep Belief Network. To enhance the detection accuracy of the model, the hidden layers of Deep Belief Network (DBN) is optimized using the new hybrid optimization model (CMPRO). After the detection of malicious nodes, the source node selects the shortest path to the destination and performs secure routing in the absence of malicious node. In the proposed model, the optimal path for routing the data is identified using the Dijkstra algorithm. As a whole the network becomes secured. Finally, the performance of the model is validated to manifest its efficiency over the existing models
Survey on Securing Internet of Things through Block chain Technology Swathi Darla, C Naveena Proceedings of the International Conference on Electronics and Renewable Systems Icears 2022, 2022 Internet of Things (IoT) has found wider acceptance due to its real-time and high efficient collaboration; Wireless Sensor Network aka WSN is core component for supporting the IoT operation. The security issue is one of the major concerns and several research works have been considered. This research review aims to conduct a critical review of the Blockchain-based WSN) domain. Blockchain-WSN aims to focus on the different aspects of security concerns in a wireless sensor network.. Moreover, the novelty of this research review lies in critical analysis of the adoption of blockchain in WSN in comparison with other research reviews. At first, this paper discusses the WSN and its challenges, especially from the security perspective through exposing the vulnerabilities in WSN. Further, this research review work introduces a blockchain approach review and its integration with conventional WSNs. Furthermore, several existing blockchain-WSN mechanism is reviewed and analyzed. At last limitation and restriction of blockchain-WSN is highlighted that open the research gap for designing the new mechanism for WSN security with blockchain
RECENT SCHOLAR PUBLICATIONS
Advanced Persistent Threat Detection Through Machine Learning S Darla, A Shanmukha, DSS Suhas, HA Kalahal 2026 Contemporary Computing Innovations Conference (CCIC), 1-5 , 2026 2026
Detecting Insider Threats Using Unsupervised Machine Learning S Darla, Y Nag, VV Gaonkar, MG DK 2026 Contemporary Computing Innovations Conference (CCIC), 1-6 , 2026 2026
AI-Based Encrypted Traffic Analysis for IoT Forensics S Darla, J Vishesh, B Chandra, PP Saranya, P Tanmayee 2026 International Conference on Intelligent and Innovative Technologies in … , 2026 2026
Deepfake Image Detection System S Darla, Y Nag, A Kapoor, H Ranjan, S Kumar 2026 International Conference on Intelligent and Innovative Technologies in … , 2026 2026
Automated vehicle collision detection: multi-model computer vision framework S Darla, P Kiran, U Kumar, BR Purnesh, D Vamshi 2026 International Conference on Intelligent and Innovative Technologies in … , 2026 2026 Citations: 1
An optimized Bi-LSTM with deep learning-based intrusion detection system in healthcare using Blockchain S Darla, N C, BN Ajay, M Ahmed Information Security Journal: A Global Perspective 35 (1), 140-153 , 2026 2026 Citations: 1
Privacy-Preserving Machine Learning Framework for Secure Healthcare Applications S Darla, K Purushotham, G Pranathi, DN Navya 2025 5th International Conference on Mobile Networks and Wireless … , 2025 2025
Improved adaptive spiral seagull optimizer for intrusion detection and mitigation in wireless sensor network S Darla, C Naveena SN Computer Science 5 (4), 394 , 2024 2024 Citations: 11
An optimized deep learning based malicious nodes detection in intelligent Sensor-Based systems using blockchain S Darla, C Naveena J. Adv. Inform. Technol 14 (5) , 2023 2023 Citations: 5
Survey on securing internet of things through block chain technology S Darla, C Naveena 2022 International Conference on Electronics and Renewable Systems (ICEARS … , 2022 2022 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
Improved adaptive spiral seagull optimizer for intrusion detection and mitigation in wireless sensor network S Darla, C Naveena SN Computer Science 5 (4), 394 , 2024 2024 Citations: 11
Survey on securing internet of things through block chain technology S Darla, C Naveena 2022 International Conference on Electronics and Renewable Systems (ICEARS … , 2022 2022 Citations: 6
An optimized deep learning based malicious nodes detection in intelligent Sensor-Based systems using blockchain S Darla, C Naveena J. Adv. Inform. Technol 14 (5) , 2023 2023 Citations: 5
Automated vehicle collision detection: multi-model computer vision framework S Darla, P Kiran, U Kumar, BR Purnesh, D Vamshi 2026 International Conference on Intelligent and Innovative Technologies in … , 2026 2026 Citations: 1
An optimized Bi-LSTM with deep learning-based intrusion detection system in healthcare using Blockchain S Darla, N C, BN Ajay, M Ahmed Information Security Journal: A Global Perspective 35 (1), 140-153 , 2026 2026 Citations: 1
Advanced Persistent Threat Detection Through Machine Learning S Darla, A Shanmukha, DSS Suhas, HA Kalahal 2026 Contemporary Computing Innovations Conference (CCIC), 1-5 , 2026 2026
Detecting Insider Threats Using Unsupervised Machine Learning S Darla, Y Nag, VV Gaonkar, MG DK 2026 Contemporary Computing Innovations Conference (CCIC), 1-6 , 2026 2026
AI-Based Encrypted Traffic Analysis for IoT Forensics S Darla, J Vishesh, B Chandra, PP Saranya, P Tanmayee 2026 International Conference on Intelligent and Innovative Technologies in … , 2026 2026
Deepfake Image Detection System S Darla, Y Nag, A Kapoor, H Ranjan, S Kumar 2026 International Conference on Intelligent and Innovative Technologies in … , 2026 2026
Privacy-Preserving Machine Learning Framework for Secure Healthcare Applications S Darla, K Purushotham, G Pranathi, DN Navya 2025 5th International Conference on Mobile Networks and Wireless … , 2025 2025
