TRANSFORMER BASED NETWORK INTRUSION DETECTION SYSTEM: A REVIEW
DOI:
https://doi.org/10.65009/av6mpe34Keywords:
Wireless Sensor Network, Intrusion Detection System, Energy Efficiency.,,Abstract
The paper's main aim, which is to observethe foundations of intrusion detection systems
and their contributions to network security, will be made crystal obvious in the introduction. It will
outline the precise facets of IDS—such as classifications, detection strategies, and best practices—
that the paper will discuss. It will draw attention to the main points that will be explored and the
paper's logical progression. The importance of researching intrusion detection systems will be
emphasised in the introduction, particularly considering the always changing cyberthreats. It will
highlight the possible effects on organisational resilience and data security of using efficient IDS
systems. The introduction will provide the groundwork for a thorough examination of intrusion
detection systems while highlighting their significance in the state of cybersecurity today. This
article intends to provide readers with essential information to improve their network security
policies and defend against the persistent and ever-evolving cyber threats by providing insights into
the intricacies of IDS and their capabilities.
References
D. M. Abdulqader, A. M. Abdulazeez and D. Q. Zeebaree, "Machine Learning Supervised
Algorithms of Gene Selection: A Review, vol. 62, no. 03. pp. 13. 2020.
Falcini GLami & Costanza, AM 2017, ‘Deep learning in automotive software’, IEEE Softw, vol.
, no. 3, pp. 56–63. [Online]. Available: http://ieeexplore. ieee. org/document/7927925/
Luckow M Cook, Ashcraft, N, Weill, E, Djerekarov, E & Vorster, B 2016, ‘Deep learning in the
automotive industry: Applications andtools’, in Proc. IEEE Int. Conf. Big Data, pp. 3759–3768.
Umar, Syed, Bommina Naveen Sai, Nagineni Sai Lasya,Doppalapudi Asutosh, and LohithaRani.
"Machine Learning based Sentiment Analysis of Product Reviews Using DeepEmbedding."
Journal of Optoelectronics Laser 41, no. 6(2022): 108-113.
Polishetty M Roopaei & Rad, P 2016, ‘A next-generation secure cloud based deep learning license
plate recognition for smart cities’, in Proc. 15th IEEE Int. Conf. Mach. Learn. Appl, Anaheim, CA,
USA, pp. 286–293.
Fausto, A., Gaggero, G., Patrone, F., & Marchese, M. (2022). Reduction of the Delays Within an
Intrusion Detection System (IDS) Based on Software Defined Networking (SDN). IEEE Access,
, 109850-109862.
Muhammad, A. R., Sukarno, P., & Wardana, A. A. (2023). Integrated Security Information and
Event Management (SIEM) with Intrusion Detection System (IDS) for Live Analysis based on
Machine Learning. Procedia Computer Science, 217, 1406-1415.
Naveen Sai Bommina, Uppu Lokesh, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Optimized AI Models for Real-Time Cyberattack Detection in Smart Homes and Cities",
International Journal of Applied Engineering & Technology, Vol. 4 No.1, June, 2022.
Ullah, M. U., Hassan, A., Asif, M., Farooq, M. S., & Saleem, M. (2022). Intelligent Intrusion
Detection System for Apache Web Server Empowered with Machine Learning Approaches.
International Journal of Computational and Innovative Sciences, 1(1), 21-27.
Adnan, A, Muhammed, A, Abd Ghani, AAA, Abdullah, A & Hakim, F2021, ‘An intrusion
detection system for the internet of things based on machine learning: review and challenges.
Symmetry’, vol. 13, no. 6, pp. 1-13.
Kasongo, SM & Sun, Y 2021, ‘A Deep Gated Recurrent Unit based model for wireless intrusion
detection system. ICT Express, vol. 7, no. 1, pp. 81-87.
Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed , Dr. Syed Umar,
"Privacy-Preserving Federated Learning for IoT Devices with Secure Model Optimization",
International Journal of Communication Networks and Information Security (IJCNIS), (2021),
(2), 396–405.
K Sankar, Divya Rohatgi, S Balakrishna Reddy, "COX Regressive Winsorized Correlated
Convolutional Deep Belief Boltzmann Network for Covid-19 Prediction with Big Data", Grenze
International Journal of Engineering & Technology (GIJET), Grenze ID: 01.GIJET.9.1.547, ©
Grenze Scientific Society, 2023.
Usman, M., Zubair, M., Hussein, H. S., Wajid, M., Farrag, M., Ali, S. J., ... & Habeeb, M. S.
(2021). Empirical mode decomposition for analysis and filtering of speech signals. IEEE Canadian
Journal of Electrical and Computer Engineering, 44(3), 343-349.

