Jebin Bose S

@niuniv.com

Assistant Professor in Department of Computer Science and Engineering
Noorul Islam Centre for Higher Education, Kumaracoil

Jebin Bose S


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https://researchid.co/jebinbose
Jebin Bose S received the master’s degree in Master of Science in Software Engineering from Bethlahem Institute of Engineering, Karungal, (Anna University, Chennai) in 2017, and the master’s degree in Master of Engineering in Computer Science and Engineering from the Noorul Islam Centre for Higher Education, Kumaracoil in 2020. He has been a Researcher with the Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, since 2020. Presently he is an Assistant Professor in the Department of Computer Science and Engineering. His current research interests cover Malware Data Analytics, Deep Learning, Machine Learning, Artificial Intelligence, Network Security. He, in particular, focuses on the development and earlier detection of Malware Data Analytics.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Artificial Intelligence, Computer Science Applications
5

Scopus Publications

19

Scholar Citations

3

Scholar h-index

Scopus Publications

  • RETRACTION:An optimal deep learning-based framework for the detection and classification of android malware
    S. Jebin Bose, R. Kalaiselvi
    Journal of Intelligent and Fuzzy Systems, 2023
    The use of smartphones is increasing rapidly and the malicious intrusions associated with it have become a challenging task that needs to be resolved. A secure and effective technique is needed to prevent breaches and detect malicious applications. Through deep learning methods and neural networks, the earliest detection and classification of malware can be performed. Detection of Android malware is the process to identify malicious attackers and through the classification method of malware, the type is categorized as adware, ransomware, SMS malware, and scareware. Since there were several techniques employed so far for malware detection and classification, there were some limitations like a reduced rate of accuracy and so on. To overcome these limitations, a deep learning-based automated process is employed to identify the malware. In this paper, initially, the datasets are collected, and through the preprocessing method, the duplicate and noisy data are removed to improve accuracy. Then the separated malware and benign dataset from the preprocessing phase is dealt with in feature selection. The reliable features are extracted in this process by Meta-Heuristic Artificial Jellyfish Search Optimizer (MH-AJSO). Further by the process of classification, the type of malware is categorized. The classification method is performed by the proposed Dense Dilated ResNet101 (DDResNet101) classifier. According to the type of malware the breach is prevented and secured on the android device. Although several methods of malware detection are found in the android platform the accuracy is effectively derived in our proposed system. Various performance analysis is performed to compare the robustness of detection. The results show that better accuracy of 98% is achieved in the proposed model with effectiveness for identifying the malware and thereby breaches and intrusion can be prevented.
  • RETRACTION:An optimal detection of android malware using dynamic attention-based LSTM classifier
    S. Jebin Bose, R. Kalaiselvi
    Journal of Intelligent and Fuzzy Systems, 2023
    In today’s world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The feature selection is carried along with the optimization technique Recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained.
  • A state-of-the-art Analysis of Android Malware Detection Methods
    Jebin Bose S, Kalaiselvi. R
    2022 6th International Conference on Trends in Electronics and Informatics Icoei 2022 Proceedings, 2022
    Smartphones are constantly changing in today's world, and as a result, security has become a major concern. Security is a vital aspect of human life, and in a world where security is lacking, it becomes a concern for mobile users' safety. Malware is one of the most serious security risks to smartphones. Mobile malware attacks are becoming more sophisticated and widespread. Malware authors consider the open-source Android platform to be their preferred target as it came to lead the market. State-of-the-art mobile malware detection solutions in the literature use a variety of metrics and models, making cross-comparison difficult. In this paper various existing methods are compared and a significant effort is made to briefly address android malwares, various methods for detecting android malwares and to give a clear image of the progress of the android platform and various malware detection classifiers.
  • IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine
    Ganesh Kumar R., Srilatha Toomula, D. Paulraj, Jebin Bose S., Thulasi Bikku, et al.
    Wearable Telemedicine Technology for the Healthcare Industry Product Design and Development, 2021
  • Enabling authenticity and integrity with Information Hiding for secure communication in Internet of Things
    Jebin Bose S, Julia Punitha Malar Dhas, Sybi Cynthia
    International Conference on Emerging Trends in Information Technology and Engineering Ic Etite 2020, 2020
    Internet of Things (IoT) enhances the global connectivity to all the remote sensing devices. It enables the connectivity of communication and processing the real-time data that has been collected from an enormous number of connected sensing devices. There is an increase in the IoT technology that leads to various malicious attacks. It is more important to overcome the malicious attacks, mainly to stop attackers or intruders from taking all the control of devices. Ensuring the safety and accuracy of the sensing devices is a serious task. It is very much important to enabling the authenticity and integrity to obtain the safety of the devices. Dynamic tree chaining, Geometric star chaining and Onion encryption are the three solutions that has been proposed in this project for in order to enable authenticity and integrity with information hiding for secure communication. The simulation results are driven displays that the proposed system is very stable and much better than other existing solution in means of security, space and time.

RECENT SCHOLAR PUBLICATIONS

  • Fundamentals of Artificial Intelligence
    J Bose S
    2025
  • MH-ASO Based Deep NNET Classifier Scheme for Effective Android Malware Recognition & Classification Strategy
    JBS Kalaiselvi. R
    Journal of Electrical Systems 20 (5s), 1790-1800 , 2024
    2024
  • An optimal deep learning-based framework for the detection and classification of android malware
    S Jebin Bose, R Kalaiselvi
    Journal of Intelligent & Fuzzy Systems 44 (6), 9297-9310 , 2023
    2023
    Citations: 1
  • An optimal detection of android malware using dynamic attention-based LSTM classifier
    S Jebin Bose, R Kalaiselvi
    Journal of Intelligent & Fuzzy Systems 44 (1), 1425-1438 , 2023
    2023
    Citations: 3
  • A state-of-the-art Analysis of Android Malware Detection Methods
    KR Jebin Bose S
    2022 6th International Conference on Trends in Electronics and Informatics … , 2022
    2022
    Citations: 4
  • METHOD TO DETECT AND FORECAST WEATHER
    JBS Pooja S.B, Anoop Sreekumar R.S, D.S. Misbha, S. Albin Jose, Sam Abraham ...
    IN Patent App. 202,241,009,794 , 2022
    2022
  • IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine
    S Toomula, D Paulraj, J Bose, T Bikku, D Sivabalaselvamani
    Wearable Telemedicine Technology for the Healthcare Industry, 137-152 , 2022
    2022
    Citations: 8
  • Survey on Malware Prediction and Detection
    JPMD Jebin Bose S
    National Conference on Computational and Scientific Era , 2021
    2021
  • Fault registration and Detection of road cracks using Artificial Intelligence
    J Bose S
    International Research Journal of Science Engineering Technology and … , 2021
    2021
  • Enabling authenticity and integrity with Information Hiding for secure communication in Internet of Things
    J Bose, JPM Dhas, S Cynthia
    2020 International Conference on Emerging Trends in Information Technology … , 2020
    2020
    Citations: 1
  • An enhanced security mechanism for secure communication in internet of things
    JPMD Jebin Bose S
    IN Patent 202,041,039,286 , 2020
    2020
  • Efficient and Secure Data Transfer in IoT
    J Bose, JPM Dhas
    International Journal of Recent Technology and Engineering (IJRTE) 8 (4) , 2019
    2019
    Citations: 2
  • Advanced Secured Pin Entry System for Automated Teller Machine
    J Bose S
    National Conference in Innovative Computing Science and Engineering , 2019
    2019
  • Secured Pin Entry System for Automated Teller Machine
    JBS Shine Rajesh D
    IEEE - International Conference on Telecommunication, Power Analysis and … , 2017
    2017
  • Memory Enhancement Techniques
    JBS Juliet Jemila C
    Personality & Performance: Pivot to Teacher Education, 161 , 2014
    2014
  • Analysis and Research of System Security Based on Android
    J Bose S
    International Conference on Recent Advances in Computer Science, Software … , 2014
    2014
  • Maximizing the broadcast operation using MAX Lifetime Algorithm
    J Bose S
    National Conference on Recent Advancement in Computer and Communication … , 2014
    2014

MOST CITED SCHOLAR PUBLICATIONS

  • IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine
    S Toomula, D Paulraj, J Bose, T Bikku, D Sivabalaselvamani
    Wearable Telemedicine Technology for the Healthcare Industry, 137-152 , 2022
    2022
    Citations: 8
  • A state-of-the-art Analysis of Android Malware Detection Methods
    KR Jebin Bose S
    2022 6th International Conference on Trends in Electronics and Informatics … , 2022
    2022
    Citations: 4
  • An optimal detection of android malware using dynamic attention-based LSTM classifier
    S Jebin Bose, R Kalaiselvi
    Journal of Intelligent & Fuzzy Systems 44 (1), 1425-1438 , 2023
    2023
    Citations: 3
  • Efficient and Secure Data Transfer in IoT
    J Bose, JPM Dhas
    International Journal of Recent Technology and Engineering (IJRTE) 8 (4) , 2019
    2019
    Citations: 2
  • An optimal deep learning-based framework for the detection and classification of android malware
    S Jebin Bose, R Kalaiselvi
    Journal of Intelligent & Fuzzy Systems 44 (6), 9297-9310 , 2023
    2023
    Citations: 1
  • Enabling authenticity and integrity with Information Hiding for secure communication in Internet of Things
    J Bose, JPM Dhas, S Cynthia
    2020 International Conference on Emerging Trends in Information Technology … , 2020
    2020
    Citations: 1
  • Fundamentals of Artificial Intelligence
    J Bose S
    2025
  • MH-ASO Based Deep NNET Classifier Scheme for Effective Android Malware Recognition & Classification Strategy
    JBS Kalaiselvi. R
    Journal of Electrical Systems 20 (5s), 1790-1800 , 2024
    2024
  • METHOD TO DETECT AND FORECAST WEATHER
    JBS Pooja S.B, Anoop Sreekumar R.S, D.S. Misbha, S. Albin Jose, Sam Abraham ...
    IN Patent App. 202,241,009,794 , 2022
    2022
  • Survey on Malware Prediction and Detection
    JPMD Jebin Bose S
    National Conference on Computational and Scientific Era , 2021
    2021
  • Fault registration and Detection of road cracks using Artificial Intelligence
    J Bose S
    International Research Journal of Science Engineering Technology and … , 2021
    2021
  • An enhanced security mechanism for secure communication in internet of things
    JPMD Jebin Bose S
    IN Patent 202,041,039,286 , 2020
    2020
  • Advanced Secured Pin Entry System for Automated Teller Machine
    J Bose S
    National Conference in Innovative Computing Science and Engineering , 2019
    2019
  • Secured Pin Entry System for Automated Teller Machine
    JBS Shine Rajesh D
    IEEE - International Conference on Telecommunication, Power Analysis and … , 2017
    2017
  • Memory Enhancement Techniques
    JBS Juliet Jemila C
    Personality & Performance: Pivot to Teacher Education, 161 , 2014
    2014
  • Analysis and Research of System Security Based on Android
    J Bose S
    International Conference on Recent Advances in Computer Science, Software … , 2014
    2014
  • Maximizing the broadcast operation using MAX Lifetime Algorithm
    J Bose S
    National Conference on Recent Advancement in Computer and Communication … , 2014
    2014