A Review of Machine Learning-based Intrusion Detection System


  • Nilamadhab Mishra Biju Patnaik University of Technology
  • Sarojananda Mishra Indira Gandhi Institute of Technology image/svg+xml




Intrusion Detection, Machine Learning, Support Vector Machine, Dataset Attacks


Intrusion detection systems are mainly prevalent proclivity within our culture today. Interference exposure systems function as countermeasures to identify web-based protection threats. This is a computer or software program that monitors unauthorized network activity and sends alerts to administrators. Intrusion detection systems scan for known threat signatures and anomalies in normal behaviour. This article also analyzed different types of infringement finding systems and modus operandi, focusing on support-vector-machines; Machine-learning; fuzzy-logic; and supervised-learning. For the KDD dataset, we compared different strategies based on their accuracy. Authors pointed out that using support vector machine and machine learning together improves accuracy. 


Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">


XIAOYAN WANG, HANWEN WANG. A High Performance Intrusion Detection Method Based on Combining Supervised and Unsupervised Learning. IEEE Smart World, Ubiquitous Intelligence & Computing Advanced & Trusted Computing, Scalable Computing, Internet of People and Smart City Innovations. 2018. DOI: https://doi.org/10.1109/SmartWorld.2018.00304

MOHAMMAD EI BOUJNOUNI and MOHAMED JEDRA. New Intrusion Detection System Based on Support Vector Domain Description with Information Metric. International Journal of Network Security. 2018.

KARUNA S.BHOSALE, Assoc. Prof. MARIA, Data Mining Based Advanced algorithm for intrusion detection in Communication Networks. International conference on Computational Techniques, Electronics & Mechanical System (CTEMS). 2018. DOI: https://doi.org/10.1109/CTEMS.2018.8769173

P.AMALA, G. GAYATHRI, S.DINESH. Effective Intrusion Detection System Using Support Vector Machine Learning. International Journal of Advanced Science and Engineering Research. 2018.

ELMER C. MATEL, ARIEL M.SISAN. Optimization of Network Intrusion Detection System using Genetic Algorithm with Improved Feature Selection Technique. Technological Institute of the Pilippines Quezon City, Phillipines. 2019. DOI: https://doi.org/10.1109/HNICEM48295.2019.9073439

LUKMAN HAKIM, RAHILLA FATMA NOVRIANDI. Influence Analysis of Feature Selection to Network Intrusion Detection System Performance Using NSL-KDD Dataset. ICOMITEE 2019; October 16th-17th 2019; Jember, Indonesia in 2019. DOI: https://doi.org/10.1109/ICOMITEE.2019.8920961

AFREEN BHUMGARA, ANAND PITALE. Detection of Network Intrusions Using Hybrid Intelligent System. International Conferences on Advances in Information Technology . 2019. DOI: https://doi.org/10.1109/ICAIT47043.2019.8987368

RITUMBHRA UIKEY, Dr. MANARI CYANCHANDANI. Survey on Classification Techniques Applied to Intrusion Detection System and its Comparative Analysis. 4th International Conference on Communication and Electronics System (ICCES 2019) IEEE Conference Record #45898; IEEE Xplore ISBN; 978-1-7281-1261-9 . 2019. DOI: https://doi.org/10.1109/ICCES45898.2019.9002129

T.SREE KALA, A.CHRISTY, An Intrusion Detection System Using Opposition Based Particle Swarm Optimization Algorithm and PNN. International conference on Machine Learning, Big Data, Cloud and Parallel Computing, India 14th-16th feb 2019. DOI: https://doi.org/10.1109/COMITCon.2019.8862237

KUNAL SINGH, Dr. K.JAMES MATHAI. Performance Comparison of Intrusion Detection System between DBN and SPELM Algorithm. National Institute of Technical Teacher Training $ Research, Bhopal India in 2019.

ZHIYOU ZHANG, PEISHANG PAN. A Hybrid Intrusion Detection Method Based on Improved Fuzzy C-Means and SVM. International Conference on Communication Information System and Computer Engineer [CISCE] in 2019. DOI: https://doi.org/10.1109/CISCE.2019.00056

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6

Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9. https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016

Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937

Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023. https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052

Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603

Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579

Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484

Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470

ZAKARIA EI MRABET. A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid. National Institute of Posts and Telecommunication Rabat, Morocco in 2019 DOI: https://doi.org/10.1109/EIT.2019.8834255

ADITYA PHADKE, MOHIT KULKARNI, PRANAV BHAWALKAR AND RASHMI BHATTAD . A Review of Machine Learning Methodologies for Network Intrusion Detection. 3rd National Conference on Computing Methodologies and Communication (ICCMC 2019) IEEE Xplore Part Number: cfp19k25-art; isbn; 978-1-5386-7807- 4 in 2019. DOI: https://doi.org/10.1109/ICCMC.2019.8819748

S.SIVANTHAM, R.ABIRAMI, R.GOWSALYA. Comparing the Performance of Adaptive Boosted Classifiers in Anomaly Based Intrusion Detection System for Networks. at International Conference on Vision towards Emerging Trends in Communication and Networking (ViTECoN) in 2019. DOI: https://doi.org/10.1109/ViTECoN.2019.8899368

RAJESH THOMAS, DEEPA PAVITHRAN . A Survey of Intrusion Detection Models Based on NSL-KDD Data Sets. 5th HCT INFORMATION TECHNOLOGY TRENDS (ITT 2018), Dubai, UAE, Nov, 2018. DOI: https://doi.org/10.1109/CTIT.2018.8649498

HASSAN AZWAR, MUHMMAD MURTAZ, MEHWISH SIDDIQUIE, SAAD REHMAN. Intrusion Detection in Secure Network for Cyber security Systems Using Machine Learning and Data Mining. IEEE 5th International Conference on Engineering Technologies & Applied Sciences, 22-23 Nov 2018, Bangkok Thailand in 2018. DOI: https://doi.org/10.1109/ICETAS.2018.8629197

AZAR ABID SALIH, MAIWAN BAHJAT ABDULRAZAQ. Combining Best Features Selection Using Three Classifiers in Intrusion Detection System. International Conference on Advanced Science and Engineering (ICOASE), University of Zakho, Duhok Polytechnic University, Kurdistan Region, Iraq in 2019. DOI: https://doi.org/10.1109/ICOASE.2019.8723671

Dr. UMA KUMARI, UMA SONI. A Review of Intrusion Detection using Anomaly Based Detection. 2nd International Conference on Communication and Electronics Systems (ICCES 2017) IEEE Xplore Compliant – Part Number: CFP17AWO-ART,ISBN:978-1-5090-5013- 0 IN 2017. DOI: https://doi.org/10.1109/CESYS.2017.8321199




How to Cite

N. Mishra and S. Mishra, “A Review of Machine Learning-based Intrusion Detection System”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.