Sumit Kumar Tetarave

@kiit.ac.in

Assistant Professor- [II], School of Computer Applications
Kalinga Institute of Industrial Technology

Sumit Kumar Tetarave


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https://researchid.co/sumit_tetarave
17

Scopus Publications

107

Scholar Citations

6

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Enhancing network intrusion detection using graph convolutional networks fused with traditional systems
    Patrick Ansah, Caroline John, Sumit Kumar Tetarave, Ezhil Kalaimannan
    International Journal of Data Science and Analytics, 2026
  • A Fine-Tuned Personalized Movie Recommendation System Using Mask Language Models
    Julieth Remin Urassa, Hassan Poster Mahaba, Sumit Kumar Tetarave, Ezhil Kalaimannan
    2025 IEEE 16th Annual Ubiquitous Computing Electronics and Mobile Communication Conference Uemcon 2025, 2025
    Recommendation systems have become indispensable for personalizing user experiences across various digital platforms, particularly in online entertainment. Conventional methods, such as collaborative filtering and content-based filtering, have provided basic techniques for constructing such systems; however, these methods tend to be plagued by shortcomings in semantic comprehension, cold-start issues, and the inability to adapt to sophisticated user preferences. This work introduces a novel methodology that leverages the strengths of a fine-tuned masked language model (MLM), specifically BERT, to create a personalized movie recommendation system. The system is implemented through an interactive web app developed with Streamlit, allowing users to enter their preferences, view suggested movies with preview posters, and provide feedback, which is logged persistently for future model optimization. Our analysis, conducted using the TMDB 50000 dataset and user reviews, reveals that the MLM-based method achieves higher precision and recall than conventional content-based and collaborative filtering baselines.
  • Robust Network Anomaly Detection with K-Nearest Neighbors (KNN) Enhanced Digital Twins
    Peprah Obed Adjei, Sumit Kumar Tetarave, Caroline John, Madlyn Manneh, Parthasarathi Pattnayak
    Conference Proceedings IEEE SOUTHEASTCON, 2024
    Modern network security remains a critical concern in the digital landscape due to evolving cyber threats and increasingly sophisticated attack vectors such as Advanced Persistent Threats and Zero-Day Vulnerabilities. Leveraging advanced tech-nologies such as artificial intelligence (AI) and machine learning (ML) can enhance threat detection capabilities and improve incident response times when detecting and mitigating network security threats. On the other hand, an imbalanced dataset of network traffic in AI/ML models presents several challenges and can significantly impact the performance and effectiveness of the models to predict attacks. Our research aims to amplify the robustness of the imbalanced network traffic dataset to fit the analysis and adaptability of KNN-based Digital Twins dedicated to network anomaly detection. This paper capitalizes on the remarkable performance of the model, characterized by impeccable precision, recall, and F1-score, as indicated by the classification report with 99% accuracy. The confusion matrix further highlights the model's performance using the proposed robustness dataset, showing a minimal False Positive Rate (FPR) compared to similar works in the literature.
  • A Comparative Analysis of Random Forest and Support Vector Machine Techniques on the UNSW-NB15 Dataset
    Madlyn Manneh, Patrick Ansah, Sumit Kumar Tetarave, Manoj Ranjan Mishra, Ezhil Kalaimannan
    Lecture Notes in Networks and Systems, 2024
  • Fortifying Network Security: Pioneering Digital Twin Technology for Proactive Anomaly Detection
    Patrick Ansah, Sumit Kumar Tetarave, Jyoti kumari, Caroline John
    Proceedings 2023 International Conference on Computational Science and Computational Intelligence Csci 2023, 2023
    Amidst the growing cybersecurity challenges of our digital age, this study leverages Digital Twin technology to fortify network security. We've meticulously developed a Keras-based Digital Twin model to replicate complex network behaviors within a simulated environment. In a landscape where cyber threats evolve rapidly, this endeavor is pivotal. It addresses current cybersecurity trends and offers proactive digital realm protection. The model enables real-time anomaly detection, em-powering organizations to strengthen their digital infrastructure. Rigorous evaluation, employing the confusion matrix, highlights exceptional performance. The model boasts an Accuracy of 99.43%, a Precision of 85.36%, a Recall of 99.58%, and an F1 Score of 91.93 %. These metrics accentuate its precision and effectiveness in identifying network anomalies. Additionally, it achieves a remarkable True Positive count of 134,694, effectively detecting positive cases while maintaining low False Positives (Type-I Error) at 774. It also has a False Negative (Type II Error) count of 19 and a True Negative count of 4,514. This success underscores Digital Twin technology's potential to revolutionize network security, providing preemptive defense against dynamic threats. Our work signifies a transformative leap towards a safer, resilient digital lands cane fortified by cutting-edge technology,
  • FogAutho: Leveraging a novel authentication scheme for securing fog-cloud systems in smart home applications
    Raj K. Gaur, Sumit K. Tetarave, Rabindra K. Barik, Ezhil Kalaimannan
    2023 IEEE 14th Annual Ubiquitous Computing Electronics and Mobile Communication Conference Uemcon 2023, 2023
    We propose a light-weighted authentication scheme FogAutho for securing fog-cloud systems in smart home applications. It uses a novel approach to generate a session key after authenticating both ends; Fog and IoT layers. The cloud server may revoke the session key. Our proposed approach minimizes the communication between end users, fog server, and cloud server during data authentication. FogAutho achieves security objectives such as mutual authentication for both ends, a secure session key generation, data confidentiality, and attack resistance with significantly reduced communication overhead compared to the existing authentication mechanisms in the fog-cloud system.
  • Enhancing Network Security Through Proactive Anomaly Detection: A Comparative Study of Auto-Encoder Models and K-Nearest Neighbours Algorithm
    Patrick Ansah, Sumit Kumar Tetarave, Ezhil Kalaimannan, Bibhuti Bhusan Dash, Caroline John
    2023 3rd Intelligent Cybersecurity Conference ICSC 2023, 2023
    In our interconnected digital landscape, safeguarding network security is paramount. This research juxtaposes two anomaly detection methods: an Auto-encoder model using Ten-sorFlow's Keras and the K-Nearest Neighbours (KNN) algorithm. Beyond assessing model performance, this study underscores the practical relevance of these techniques in real-world security contexts. The KNN results reveal 202,325 True Positives, 4,442 True Negatives, 960 (0.045%) False Positives (Type-I error), and 2,274 (1.08%) False Negatives (Type-II error), while the Auto-encoder model achieves 130,260 True Positives, 1,791 True Negatives, 5,208 (3.7%) False Positive (Type-I error), and 2,742 (1.96%) False Negatives (Type-II error). Crucially, this research emphasizes that timely anomaly detection is the linchpin in thwarting potential security breaches, with anomaly prevention serving as a proactive defense strategy. By harnessing machine learning and data-driven methodologies, this work contributes to fortifying network security. These findings provide security prac-titioners with valuable insights into the pivotal role of anomaly detection in intrusion prevention. Furthermore, this study paves the way for future advancements in network security, solidifying the position of proactive anomaly detection in cybersecurity.
  • Privacy preservation and security challenges: a new frontier multimodal machine learning research
    Santosh Kumar, Mithilesh Kumar Chaube, Srinivas Naik Nenavath, Sachin Kumar Gupta, Sumit Kumar Tetarave
    International Journal of Sensor Networks, 2022
    Multimodal machine learning is a vibrant multi-disciplinary field and achieved much attention due to its wide range of applications. A research problem is multimodal, for the impact of privacy preservation. It shields sensitive data in the cloud by using a single modality-based privacy system. The user's biometric features are always stored in the database, primarily present in the cloud server to validate the user and his access. This facet provides a beneficial quality but at the same time has raised crucial affairs in security and privacy of biometric feature set. The main concern is to manage and stop the privacy breaches in clouds. The article discusses the detailed analysis of security schemes with a multimodal-based learning framework over sensitive data and systems at both ends. The article also accentuates frameworks and schemes that may apply in various applications to ensure privacy preservation of individuals and data security by multimodal algorithms.
  • PJ-Sec: secure node joining in mobile P2P networks
    Sumit Kumar Tetarave, Somanath Tripathy
    Ccf Transactions on Pervasive Computing and Interaction, 2021
  • Enhancing quality of experience using peer-to-peer overlay on device-to-device communications
    Sumit Tetarave, Somanath Tripathy, Ratan Ghosh
    International Journal of Communication Systems, 2020
    SummaryWith the recent development of LTE‐A/5G technologies, data sharing among mobile devices offer an attractive opportunity to reduce Internet access. However, it requires smart strategies to share the data with low trade‐offs in time, cost, and energy. Several existing schemes offer a super‐peer‐based two‐tier model using a distributed hash table (DHT) organization for smart devices having device‐to‐device (D2D)/Bluetooth/WiFi capabilities. The primary focus of these schemes has been to reduce Internet usage by increased D2D content sharing. However, the real challenge is not in creating a two‐tier model, but evolving an efficient overlay that offers enhanced opportunities for D2D content sharing over the existing model. In this paper, we formulated a P‐median‐based selection of tier‐1 devices in a distribution network and solved it using the Lagrangian relaxation method. The tier‐2 devices become clients seeking content sharing services from tier‐1 devices. A strong motivation in this work is to raise a user's perception of the grade of service known as quality of experience (QoE). We analyzed the challenge for QoE assessment in resource‐constrained smartphones under the proposed model of enhanced D2D communication. Our focus is to establish a framework to evaluate QoE for applications and services over LTE‐A/5G networks with an improved D2D communication level. The simulation and the experimental results validate the claim that substantial improvements in QoE are possible with the proposed mathematical model for selecting and placing tier‐1 mobile devices and maintaining a DHT for D2D communication.
  • Secure opportunistic data exchange using smart devices in 5G/LTE-A networks
    Sumit Kumar Tetarave, Somanath Tripathy
    Communications in Computer and Information Science, 2019
  • A Routing Table Poisoning Model for Peer-to-Peer (P2P) Botnets
    Sumit Kumar Tetarave, Somanath Tripathy, Ezhil Kalaimannan, Caroline John, Anshika Srivastava
    IEEE Access, 2019
  • EBot: Approach Towards Modeling an Advanced P2P Botnet
    Sumit Kumar Tetarave, Somanath Tripathy, Ezhil Kalaimannan, Caroline John
    Proceedings 17th IEEE International Conference on Trust Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering Trustcom Bigdatase 2018, 2018
  • V-Chord: An efiicient file sharing on LTE/GSM network
    Sumit Kumar Tetarave, Somanath Tripathy, R. K. Ghosh
    ACM International Conference Proceeding Series, 2018
  • GMP2P: Mobile P2P over GSM for Efficient File Sharing
    Sumit Kumar Tetarave, Somanath Tripathy, R. K. Ghosh
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
  • S-gossip: Security enhanced gossip protocol for unstructured P2P networks
    Sumit Kumar Tetarave, Somanath Tripathy, Sathya Peri
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
  • MASAP: Mobile agent spam attack prevention for WSN environment
    Sumit Kumar Tetarave, Ashish Kumar Srivastava, Aditya Goel
    Communications in Computer and Information Science, 2011

RECENT SCHOLAR PUBLICATIONS

  • Enhancing network intrusion detection using graph convolutional networks fused with traditional systems
    P Ansah, C John, SK Tetarave, E Kalaimannan
    International journal of data science and analytics 21 (1), 31 , 2026
    2026
    Citations: 1
  • A Fine-Tuned Personalized Movie Recommendation System Using Mask Language Models
    JR Urassa, HP Mahaba, SK Tetarave, E Kalaimannan
    2025 IEEE 16th Annual Ubiquitous Computing, Electronics & Mobile … , 2025
    2025
  • A comparative analysis of random forest and support vector machine techniques on the UNSW-NB15 dataset
    M Manneh, P Ansah, SK Tetarave, MR Mishra, E Kalaimannan
    The International Conference on Innovations in Computing Research, 194-203 , 2024
    2024
    Citations: 8
  • Robust network anomaly detection with K-nearest neighbors (KNN) enhanced digital twins
    PO Adjei, SK Tetarave, C John, M Manneh, P Pattnayak
    SoutheastCon 2024, 421-426 , 2024
    2024
    Citations: 11
  • Tomato disease fusion and classification using Deep Learning
    P Ansah, SK Tetarave, E Kalaimannan, C John, T Hirzel, SK Tetarave
    International Journal on Cybernetics & Informatics 12 (7), 31-43 , 2023
    2023
    Citations: 1
  • Fortifying Network Security: Pioneering Digital Twin Technology for Proactive Anomaly Detection
    P Ansah, SK Tetarave, C John
    2023 International Conference on Computational Science and Computational … , 2023
    2023
    Citations: 4
  • Enhancing Network Security Through Proactive Anomaly Detection: A Comparative Study of Auto-Encoder Models and K-Nearest Neighbours Algorithm
    P Ansah, SK Tetarave, E Kalaimannan, BB Dash, C John
    2023 3rd Intelligent Cybersecurity Conference (ICSC), 119-126 , 2023
    2023
    Citations: 4
  • FogAutho: Leveraging a novel authentication scheme for securing fog-cloud systems in smart home applications
    RK Gaur, SK Tetarave, RK Barik, E Kalaimannan
    2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile … , 2023
    2023
  • Privacy preservation and security challenges: a new frontier multimodal machine learning research
    S Kumar, MK Chaube, SN Nenavath, SK Gupta, SK Tetarave
    International Journal of Sensor Networks 39 (4), 227-245 , 2022
    2022
    Citations: 28
  • PJ-Sec: secure node joining in mobile P2P networks
    SK Tetarave, S Tripathy
    CCF Transactions on Pervasive Computing and Interaction 3 (1), 13-24 , 2021
    2021
    Citations: 7
  • Enhancing quality of experience using peer‐to‐peer overlay on device‐to‐device communications
    S Tetarave, S Tripathy, R Ghosh
    International Journal of Communication Systems 33 (15), e4546 , 2020
    2020
    Citations: 2
  • Techniques for Efficient and Secure File-sharing in Peer to Peer Mobile Networks
    SK Tetarave
    IIT Patna , 2020
    2020
  • Robust Node ID Assignment for Mobile P2P Networks
    SK Tetarave, S Tripathy
    arXiv preprint arXiv:1905.05388 , 2019
    2019
    Citations: 1
  • Enhancing Quality of Experience using DHT Overlay on Device-to-Device Communications in LTE-A Networks
    SK Tetarave, S Tripathy, RK Ghosh
    arXiv preprint arXiv:1905.02381 , 2019
    2019
  • A routing table poisoning model for peer-to-peer (P2P) botnets
    SK Tetarave, S Tripathy, E Kalaimannan, C John, A Srivastava
    IEEE access 7, 67983-67995 , 2019
    2019
    Citations: 6
  • Secure opportunistic data exchange using smart devices in 5G/LTE-A networks
    SK Tetarave, S Tripathy
    International Conference on Security & Privacy, 3-16 , 2019
    2019
    Citations: 4
  • eBot: Approach towards modeling an advanced P2P botnet
    SK Tetarave, S Tripathy, E Kalaimannan, C John
    2018 17th IEEE International Conference On Trust, Security And Privacy In … , 2018
    2018
    Citations: 5
  • V-Chord: an efficient file sharing on LTE/GSM network
    SK Tetarave, S Tripathy, RK Ghosh
    Proceedings of the 19th International Conference on Distributed Computing … , 2018
    2018
    Citations: 8
  • GMP2P: mobile P2P over GSM for efficient file sharing
    SK Tetarave, S Tripathy, RK Ghosh
    International Conference on Distributed Computing and Internet Technology … , 2017
    2017
    Citations: 6
  • S-Gossip: Security enhanced gossip protocol for unstructured P2P networks
    SK Tetarave, S Tripathy, S Peri
    International Conference on Distributed Computing and Internet Technology … , 2015
    2015
    Citations: 6

MOST CITED SCHOLAR PUBLICATIONS

  • Privacy preservation and security challenges: a new frontier multimodal machine learning research
    S Kumar, MK Chaube, SN Nenavath, SK Gupta, SK Tetarave
    International Journal of Sensor Networks 39 (4), 227-245 , 2022
    2022
    Citations: 28
  • Robust network anomaly detection with K-nearest neighbors (KNN) enhanced digital twins
    PO Adjei, SK Tetarave, C John, M Manneh, P Pattnayak
    SoutheastCon 2024, 421-426 , 2024
    2024
    Citations: 11
  • A comparative analysis of random forest and support vector machine techniques on the UNSW-NB15 dataset
    M Manneh, P Ansah, SK Tetarave, MR Mishra, E Kalaimannan
    The International Conference on Innovations in Computing Research, 194-203 , 2024
    2024
    Citations: 8
  • V-Chord: an efficient file sharing on LTE/GSM network
    SK Tetarave, S Tripathy, RK Ghosh
    Proceedings of the 19th International Conference on Distributed Computing … , 2018
    2018
    Citations: 8
  • PJ-Sec: secure node joining in mobile P2P networks
    SK Tetarave, S Tripathy
    CCF Transactions on Pervasive Computing and Interaction 3 (1), 13-24 , 2021
    2021
    Citations: 7
  • A routing table poisoning model for peer-to-peer (P2P) botnets
    SK Tetarave, S Tripathy, E Kalaimannan, C John, A Srivastava
    IEEE access 7, 67983-67995 , 2019
    2019
    Citations: 6
  • GMP2P: mobile P2P over GSM for efficient file sharing
    SK Tetarave, S Tripathy, RK Ghosh
    International Conference on Distributed Computing and Internet Technology … , 2017
    2017
    Citations: 6
  • S-Gossip: Security enhanced gossip protocol for unstructured P2P networks
    SK Tetarave, S Tripathy, S Peri
    International Conference on Distributed Computing and Internet Technology … , 2015
    2015
    Citations: 6
  • eBot: Approach towards modeling an advanced P2P botnet
    SK Tetarave, S Tripathy, E Kalaimannan, C John
    2018 17th IEEE International Conference On Trust, Security And Privacy In … , 2018
    2018
    Citations: 5
  • FOREST GUARD: A complete safety for Wildlife using Mobile Agents and Sensor Clouds in WSN
    SK Tetarave, AK Srivastava
    International Journal of Computer Science Issues (IJCSI) 9 (6), 310 , 2012
    2012
    Citations: 5
  • Fortifying Network Security: Pioneering Digital Twin Technology for Proactive Anomaly Detection
    P Ansah, SK Tetarave, C John
    2023 International Conference on Computational Science and Computational … , 2023
    2023
    Citations: 4
  • Enhancing Network Security Through Proactive Anomaly Detection: A Comparative Study of Auto-Encoder Models and K-Nearest Neighbours Algorithm
    P Ansah, SK Tetarave, E Kalaimannan, BB Dash, C John
    2023 3rd Intelligent Cybersecurity Conference (ICSC), 119-126 , 2023
    2023
    Citations: 4
  • Secure opportunistic data exchange using smart devices in 5G/LTE-A networks
    SK Tetarave, S Tripathy
    International Conference on Security & Privacy, 3-16 , 2019
    2019
    Citations: 4
  • Enhancing quality of experience using peer‐to‐peer overlay on device‐to‐device communications
    S Tetarave, S Tripathy, R Ghosh
    International Journal of Communication Systems 33 (15), e4546 , 2020
    2020
    Citations: 2
  • Enhancing network intrusion detection using graph convolutional networks fused with traditional systems
    P Ansah, C John, SK Tetarave, E Kalaimannan
    International journal of data science and analytics 21 (1), 31 , 2026
    2026
    Citations: 1
  • Tomato disease fusion and classification using Deep Learning
    P Ansah, SK Tetarave, E Kalaimannan, C John, T Hirzel, SK Tetarave
    International Journal on Cybernetics & Informatics 12 (7), 31-43 , 2023
    2023
    Citations: 1
  • Robust Node ID Assignment for Mobile P2P Networks
    SK Tetarave, S Tripathy
    arXiv preprint arXiv:1905.05388 , 2019
    2019
    Citations: 1
  • A Fine-Tuned Personalized Movie Recommendation System Using Mask Language Models
    JR Urassa, HP Mahaba, SK Tetarave, E Kalaimannan
    2025 IEEE 16th Annual Ubiquitous Computing, Electronics & Mobile … , 2025
    2025
  • FogAutho: Leveraging a novel authentication scheme for securing fog-cloud systems in smart home applications
    RK Gaur, SK Tetarave, RK Barik, E Kalaimannan
    2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile … , 2023
    2023
  • Techniques for Efficient and Secure File-sharing in Peer to Peer Mobile Networks
    SK Tetarave
    IIT Patna , 2020
    2020