Dr Deepa Rani is currently serving as an Assistant Professor in the School of Computer Science, UPES Dehradun. Previously served as a Teaching Faculty in the Department of Mathematics and Scientific Computing, NIT Hamirpur. She completed her PhD in Computer Science and Engineering at NIT Hamirpur, under the supervision of Dr. Rajeev Kumar.. Her research interests include Cyber Security, Internet of Things (IoT), Machine Learning, Smart Healthcare, Wireless Networks, and Computer Networks.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Computer Networks and Communications, Information Systems
Transformer-based Network Intrusion Detection: A Multi-dataset Analysis Grijesh Nemiwal, Deepa Rani, Rajeev Kumar Proceedings of the 2025 3rd International Conference on Inventive Computing and Informatics Icici 2025, 2025 The present study presents a novel transformerbased method for detecting network intrusions, tackling the difficulties of handling substantial network traffic data and identifying advanced cyberthreats. The proposed architecture employs multi-head attention mechanisms optimized for network traffic analysis, complemented by advanced preprocessing techniques for handling imbalanced classes. The system demonstrates exceptional performance across both KDD Cup and NSL-KDD datasets, achieving superior accuracy with balanced precision and recall metrics across diverse attack categories. Comprehensive evaluation against established baselines, including traditional machine learning approaches and modern deep learning architectures, validates the effectiveness of the proposed methodology. The research advances cybersecurity systems by demonstrating how transformer architectures can process network traffic data efficiently while maintaining high detection accuracy. The results show that the transformer-based architecture achieves 99.8 % accuracy on the KDD Cup 1999 dataset and 99.7 % accuracy on the NSL-KDD dataset, significantly outperforming other models in comparison.
Fuzzy logic-based delay efficient data collection technique for IoT environment Deepa Rani, Tanuj Wala, Rajeev Kumar, Naveen Chauhan International Journal of Communication Networks and Distributed Systems, 2023 The sensor nodes in WSNs are resource constraints and data collection is draining the sensor node's energy. Therefore, collecting data in a single hop by the mobile device helps in preserving the sensor node energy. This paper is introducing a fuzzy logic-based one hop data collection path (FLO-DCP) algorithm to find stop points from the set of intersecting points of the overlapped clusters and to reduce the data collection time by shorting the path length of the mobile device and increasing the lifetime of the network by preserving the sensor node's energy. The proposed method consists of three phases. First, fuzzy logic-based overlapped clusters are formed, thereafter the stop points and trajectory path for the mobile device is being computed, and last, the data collection process is done. Also, in comparison with NDCMC, CB, and ORLP-RP algorithms, simulation results show that the proposed algorithm has better performance.
Study and Comparision of Vectorization Techniques Used in Text Classification Deepa Rani, Rajeev Kumar, Naveen Chauhan 2022 13th International Conference on Computing Communication and Networking Technologies Icccnt 2022, 2022 Reviews on products and movies play an important role in predicting and formulating business strategies. Entertainment media, E-commerce, and social media use customers’ reviews to analyze customers’ requirements and level of satisfaction with the product. Business Analyst uses Sentiment Analysis for analyzing the attitude of the users from their reviews. E-commerce websites, entertainment and social media posts, tweets, comments, reviews, status, etc are the major sources of sentiment data (reviews). In the review system, users give the rating on a predefined scale of (1-5) i.e lowest to highest in terms of their satisfaction. As sentiment Analysis is one of the major applications of Machine Learning and machine learning deals with numeric data, so, textual-based review data needs to be converted into numeric data. Conversion of text to numeric form requires a large amount of memory and it is time-consuming also. This paper presents various vectorization techniques and their comparison in terms of memory management to convert text file into a vector file. The comparison shows gensim library-based Doc2Vec approach reduces memory requirements by up to 80%. This will also reduce the time consumption for task analysis and data processing of the model.
Supervised Machine Learning Based Network Intrusion Detection System for Internet of Things Deepa Rani, Narottam Chand Kaushal 2020 11th International Conference on Computing Communication and Networking Technologies Icccnt 2020, 2020 The Internet of Things (IoT) is an innovative invention that can combine physical object to the Internet with an ability to transfer and access of the data through Internet, however with the rapid growth in the application and services of the IoT, the scope of network attack is also increasing exponentially. To secure data, device and IoT network, there is a need of an efficient, secure and accurate Intrusion Detection System (IDS). IDS basically monitors network and system activities and raises alarm when anything deviated from its normal behaviour is found. Classical intrusion detection system follows rule based detection approaches that fail to detect zero day or unknown attack is not suitable for dynamic and insecure IoT environment. This paper mainly proposes an efficient method with uniform detection system based on supervised machine learning technique by using Random Forest classifier. Also two different datasets, NSL-KDD and KDDCUP99 with minimal feature sets have been used that give lightweight attack detection strategy for IoT network. Simulation of proposed method with theses datasets has 99.9 percentage accuracy in intrusion detection with less amount of time and energy.
RECENT SCHOLAR PUBLICATIONS
Applying Ensemble Approach to Predict Maternal Health Risk S Thakur, R Thakur, D Rani, R Kumar Cambridge Scholars Publishing; https://www.cambridgescholars.com/product/978 … , 2026 2026
Transformer-based Network Intrusion Detection: A Multi-dataset Analysis G Nemiwal, D Rani, R Kumar 2025 3rd International Conference on Inventive Computing and Informatics … , 2025 2025
Applying Genetic Algorithms in Machine Learning to Predict Risk in Pregnancy R Thakur, S Thakur, D Rani, R Kumar International Conference on Information Technology and Artificial … , 2025 2025
Study Influencing Factors of Maternal Health and the Role of Internet of Things (IoT) to Improve Maternal Care D Rani, R Kumar, N Chauhan SN Computer Science 5 (6), 778 , 2024 2024 Citations: 3
A secure framework for IoT‐based healthcare using blockchain and IPFS D Rani, R Kumar, N Chauhan Security and Privacy 7 (2), e348 , 2024 2024 Citations: 22
Fuzzy logic-based delay efficient data collection technique for IoT environment D Rani, T Wala, R Kumar, N Chauhan International Journal of Communication Networks and Distributed Systems 29 … , 2023 2023 Citations: 1
Study and comparision of vectorization techniques used in text classification D Rani, R Kumar, N Chauhan 2022 13th international conference on computing communication and networking … , 2022 2022 Citations: 29
Supervised machine learning based network intrusion detection system for Internet of Things D Rani, NC Kaushal 2020 11th International conference on computing, communication and … , 2020 2020 Citations: 59
MOST CITED SCHOLAR PUBLICATIONS
Supervised machine learning based network intrusion detection system for Internet of Things D Rani, NC Kaushal 2020 11th International conference on computing, communication and … , 2020 2020 Citations: 59
Study and comparision of vectorization techniques used in text classification D Rani, R Kumar, N Chauhan 2022 13th international conference on computing communication and networking … , 2022 2022 Citations: 29
A secure framework for IoT‐based healthcare using blockchain and IPFS D Rani, R Kumar, N Chauhan Security and Privacy 7 (2), e348 , 2024 2024 Citations: 22
Study Influencing Factors of Maternal Health and the Role of Internet of Things (IoT) to Improve Maternal Care D Rani, R Kumar, N Chauhan SN Computer Science 5 (6), 778 , 2024 2024 Citations: 3
Fuzzy logic-based delay efficient data collection technique for IoT environment D Rani, T Wala, R Kumar, N Chauhan International Journal of Communication Networks and Distributed Systems 29 … , 2023 2023 Citations: 1
Applying Ensemble Approach to Predict Maternal Health Risk S Thakur, R Thakur, D Rani, R Kumar Cambridge Scholars Publishing; https://www.cambridgescholars.com/product/978 … , 2026 2026
Transformer-based Network Intrusion Detection: A Multi-dataset Analysis G Nemiwal, D Rani, R Kumar 2025 3rd International Conference on Inventive Computing and Informatics … , 2025 2025
Applying Genetic Algorithms in Machine Learning to Predict Risk in Pregnancy R Thakur, S Thakur, D Rani, R Kumar International Conference on Information Technology and Artificial … , 2025 2025
