Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review

Aqeel Hanash Younis, Adnan Mohsin Abdulazeez

Abstract


The worldwide internet continues to spread, presenting numerous escalating hazards with significant potential. Existing static detection systems necessitate frequent updates to signature-based databases and solely detect known malicious threats. Efforts are currently being made to develop network intrusion detection systems that can utilize machine learning techniques to accurately detect and classify hazardous content. This would result in a decrease in the overall workload required. Network Intrusion Detection Systems are created with a diverse range of machine learning algorithms. The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems.


Keywords


Intrusion Detection Systems, Machine learning, SVM, Random Forest.

Full Text:

PDF

References


Q.-V. Dang, “Active learning for intrusion detection systems,” presented at the 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), IEEE, 2020, pp. 1–3.

R. Singh, M. Kalra, and S. Solanki, “A hybrid approach for intrusion detection based on machine learning,” Int. J. Secur. Netw., vol. 15, no. 4, pp. 233–242, 2020.

J. Lee, J. Kim, I. Kim, and K. Han, “Cyber threat detection based on artificial neural networks using event profiles,” Ieee Access, vol. 7, pp. 165607–165626, 2019.

A. Abdulazeez, B. Salim, D. Zeebaree, and D. Doghramachi, “Comparison of VPN Protocols at Network Layer Focusing on Wire Guard Protocol,” 2020.

C. J. Ugochukwu, E. Bennett, and P. Harcourt, An intrusion detection system using machine learning algorithm. LAP LAMBERT Academic Publishing, 2019.

A. A. Salih and M. B. Abdulrazaq, “Combining best features selection using three classifiers in intrusion detection system,” presented at the 2019 International Conference on Advanced Science and Engineering (ICOASE), IEEE, 2019, pp. 94–99.

W. A. H. Ghanem, A. Jantan, S. A. A. Ghaleb, and A. B. Nasser, “An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons,” IEEE Access, vol. 8, pp. 130452–130475, 2020.

T. A. Alamiedy, M. Anbar, Z. N. Alqattan, and Q. M. Alzubi, “Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 9, pp. 3735–3756, 2020.

A. Bhumgara and A. Pitale, “Detection of network intrusions using hybrid intelligent systems,” presented at the 2019 1st International Conference on Advances in Information Technology (ICAIT), IEEE, 2019, pp. 500–506.

A. Rai, “Optimizing a new intrusion detection system using ensemble methods and deep neural network,” presented at the 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), IEEE, 2020, pp. 527–532.

A. H. Mirza, “Computer network intrusion detection using various classifiers and ensemble learning,” presented at the 2018 26th Signal processing and communications applications conference (SIU), IEEE, 2018, pp. 1–4.

S. Ahmad, F. Arif, Z. Zabeehullah, and N. Iltaf, “Novel approach using deep learning for intrusion detection and classification of the network traffic,” presented at the 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), IEEE, 2020, pp. 1–6.

D. A. Hasan and A. M. Abdulazeez, “A modified convolutional neural networks model for medical image segmentation,” learning, vol. 20, p. 22, 2020.

A. Phadke, M. Kulkarni, P. Bhawalkar, and R. Bhattad, “A review of machine learning methodologies for network intrusion detection,” presented at the 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), IEEE, 2019, pp. 272–275.

A. Golrang, A. M. Golrang, S. Yildirim Yayilgan, and O. Elezaj, “A novel hybrid IDS based on modified NSGAII-ANN and random forest,” electronics, vol. 9, no. 4, p. 577, 2020.

K. Shashank and M. Balachandra, “Review on network intrusion detection techniques using machine learning,” presented at the 2018 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), IEEE, 2018, pp. 104–109.

F. Yihunie, E. Abdelfattah, and A. Regmi, “Applying machine learning to anomaly-based intrusion detection systems,” presented at the 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT), IEEE, 2019, pp. 1–5.

A. Ghosal and S. Halder, “A survey on energy efficient intrusion detection in wireless sensor networks,” J. Ambient Intell. Smart Environ., vol. 9, no. 2, pp. 239–261, 2017.

C. Kalimuthan and J. A. Renjit, “Review on intrusion detection using feature selection with machine learning techniques,” Mater. Today Proc., vol. 33, pp. 3794–3802, 2020.

A. Phadke, M. Kulkarni, P. Bhawalkar, and R. Bhattad, “A review of machine learning methodologies for network intrusion detection,” presented at the 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), IEEE, 2019, pp. 272–275.

C. Kalimuthan and J. A. Renjit, “Review on intrusion detection using feature selection with machine learning techniques,” Mater. Today Proc., vol. 33, pp. 3794–3802, 2020.

M. Almseidin, M. Alzubi, S. Kovacs, and M. Alkasassbeh, “Evaluation of machine learning algorithms for intrusion detection system,” presented at the 2017 IEEE 15th international symposium on intelligent systems and informatics (SISY), IEEE, 2017, pp. 000277–000282.

I. S. Thaseen and C. A. Kumar, “Intrusion detection model using fusion of chi-square feature selection and multi class SVM,” J. King Saud Univ.-Comput. Inf. Sci., vol. 29, no. 4, pp. 462–472, 2017.

S. Ganapathy, N. Jaisankar, P. Yogesh, and A. Kannan, “An intelligent intrusion detection system using outlier detection and multiclass SVM,” Int. J. Recent Trends Eng. Technol., vol. 5, no. 01, 2011.

B. Gupta, A. Rawat, A. Jain, A. Arora, and N. Dhami, “Analysis of various decision tree algorithms for classification in data mining,” Int. J. Comput. Appl., vol. 163, no. 8, pp. 15–19, 2017.

S. Latha and S. J. Prakash, “A survey on network attacks and Intrusion detection systems,” presented at the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 2017, pp. 1–7.

J. Zhang and M. Zulkernine, “Network Intrusion Detection using Random Forests.,” presented at the Pst, Citeseer, 2005.

S. Aljawarneh, M. B. Yassein, and M. Aljundi, “An enhanced J48 classification algorithm for the anomaly intrusion detection systems,” Clust. Comput., vol. 22, pp. 10549–10565, 2019.

I. Abrar, Z. Ayub, F. Masoodi, and A. M. Bamhdi, “A machine learning approach for intrusion detection system on NSL-KDD dataset,” presented at the 2020 international conference on smart electronics and communication (ICOSEC), IEEE, 2020, pp. 919–924.

K. S. Kiran, R. K. Devisetty, N. P. Kalyan, K. Mukundini, and R. Karthi, “Building a intrusion detection system for IoT environment using machine learning techniques,” Procedia Comput. Sci., vol. 171, pp. 2372–2379, 2020.

N. Elmrabit, F. Zhou, F. Li, and H. Zhou, “Evaluation of machine learning algorithms for anomaly detection,” presented at the 2020 international conference on cyber security and protection of digital services (cyber security), IEEE, 2020, pp. 1–8.

M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Multi-stage optimized machine learning framework for network intrusion detection,” IEEE Trans. Netw. Serv. Manag., vol. 18, no. 2, pp. 1803–1816, 2020.

O. J. Mebawondu, A. O. Adetunmbi, J. O. Mebawondu, and O. D. Alowolodu, “Feature Weighting and Classification Modeling for Network Intrusion Detection Using Machine Learning Algorithms,” presented at the International Conference on Information and Communication Technology and Applications, Springer, 2020, pp. 315–327.

I. S. Thaseen, B. Poorva, and P. S. Ushasree, “Network intrusion detection using machine learning techniques,” presented at the 2020 International conference on emerging trends in information technology and engineering (IC-ETITE), IEEE, 2020, pp. 1–7.

N. Islam et al., “Towards Machine Learning Based Intrusion Detection in IoT Networks.,” Comput. Mater. Contin., vol. 69, no. 2, 2021.

G. Xu, “Research on network intrusion detection method based on machine learning,” presented at the Journal of Physics: Conference Series, IOP Publishing, 2021, p. 012034.

J. Carneiro, N. Oliveira, N. Sousa, E. Maia, and I. Praça, “Machine learning for network-based intrusion detection systems: an analysis of the CIDDS-001 dataset,” presented at the International Symposium on Distributed Computing and Artificial Intelligence, Springer, 2021, pp. 148–158.

K. Kumar and V. Bhatnagar, “Machine Learning Algorithms Performance Evaluation for Intrusion Detection,” J. Inf. Technol. Manag., vol. 13, no. 1, pp. 42–61, 2021.

S. V. Amanoul, A. M. Abdulazeez, D. Q. Zeebare, and F. Y. Ahmed, “Intrusion detection systems based on machine learning algorithms,” presented at the 2021 IEEE international conference on automatic control & intelligent systems (I2CACIS), IEEE, 2021, pp. 282–287.

S. Krishnaveni, S. Sivamohan, S. Sridhar, and S. Prabakaran, “Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing,” Clust. Comput., vol. 24, no. 3, pp. 1761–1779, 2021.

N. Pise, “Application of machine learning for intrusion detection system,” Inf. Technol. Ind., vol. 9, no. 1, pp. 314–323, 2021.

Z. A. Aziz and A. M. Abdulazeez, “Application of Machine Learning Approaches in Intrusion Detection System,” J. Soft Comput. Data Min., vol. 2, no. 2, pp. 1–13, 2021.

A. H. Azizan et al., “A machine learning approach for improving the performance of network intrusion detection systems,” Ann. Emerg. Technol. Comput. AETiC, vol. 5, no. 5, pp. 201–208, 2021.

H. A. Ahmed, A. Hameed, and N. Z. Bawany, “Network intrusion detection using oversampling technique and machine learning algorithms,” PeerJ Comput. Sci., vol. 8, p. e820, 2022.

S. Mekala, R. Jatothu, S. Kodati, K. Pradeep Reddy, and N. Sreekanth, “Network Intrusion Detection Using Machine Learning for Virtualized Data,” in Innovations in Signal Processing and Embedded Systems: Proceedings of ICISPES 2021, Springer, 2022, pp. 235–244.

A. P. Singh, S. Kumar, A. Kumar, and M. Usama, “Machine learning based intrusion detection system for minority attacks classification,” presented at the 2022 international conference on computational intelligence and sustainable engineering solutions (CISES), IEEE, 2022, pp. 256–261.

S. Chishakwe, N. Moyo, B. M. Ndlovu, and S. Dube, “Intrusion Detection System for IoT environments using Machine Learning Techniques,” presented at the 2022 1st Zimbabwe Conference of Information and Communication Technologies (ZCICT), IEEE, 2022, pp. 1–7.

A. A. Yilmaz, “Intrusion detection in computer networks using optimized machine learning algorithms,” presented at the 2022 3rd International Informatics and Software Engineering Conference (IISEC), IEEE, 2022, pp. 1–5.

R. Tahri, Y. Balouki, A. Jarrar, and A. Lasbahani, “Intrusion detection system using machine learning algorithms,” presented at the ITM Web of Conferences, EDP Sciences, 2022, p. 02003.

M. A. Rajput, M. Umar, A. Ahmed, A. R. Bhangwar, and K. S. Memon, “Evaluation of Machine Learning based Network Attack Detection,” Sukkur IBA J. Emerg. Technol., vol. 5, no. 2, pp. 57–66, 2022.

T.-H. Chua and I. Salam, “Evaluation of machine learning algorithms in network-based intrusion detection system,” ArXiv Prepr. ArXiv220305232, 2022.

M. Mehmood et al., “A hybrid approach for network intrusion detection,” CMC-Comput Mater Contin, vol. 70, pp. 91–107, 2022.

I. Ahmad, Q. E. Ul Haq, M. Imran, M. O. Alassafi, and R. A. AlGhamdi, “An efficient network intrusion detection and classification system,” Mathematics, vol. 10, no. 3, p. 530, 2022.

T.-H. Chua and I. Salam, “Evaluation of Machine Learning Algorithms in Network-Based Intrusion Detection Using Progressive Dataset,” Symmetry, vol. 15, no. 6, p. 1251, 2023.

N. ANAND, M. SAIFULLA, and P. K. Aakula, “High-performance Intrusion Detection Systemusing eBPF with Machine Learning algorithms,” 2023.

M. Bacevicius and A. Paulauskaite-Taraseviciene, “Machine Learning Algorithms for Raw and Unbalanced Intrusion Detection Data in a Multi-Class Classification Problem,” Appl. Sci., vol. 13, no. 12, p. 7328, 2023.

M. Paricherla, M. Ritonga, S. R. Shinde, S. M. Chaudhari, R. Linur, and A. Raghuvanshi, “Machine learning techniques for accurate classification and detection of intrusions in computer network,” Bull. Electr. Eng. Inform., vol. 12, no. 4, pp. 2340–2347, 2023.

H. Somashekar and R. Boraiah, “Network intrusion detection and classification using machine learning predictions fusion,” Indones. J. Electr. Eng. Comput. Sci., vol. 31, no. 2, pp. 1147–1153, 2023.

F. M. Alotaibi, “Network Intrusion Detection Model Using Fused Machine Learning Technique.,” Comput. Mater. Contin., vol. 75, no. 2, 2023.

A. Abeshek, S. Venkatraman, S. Aravintakshan, V. Santhosh, and R. Manoharan, “Network Intrusion Detection Using Machine Learning Approach,” EasyChair, 2516–2314, 2023.

H. Güney, “Preprocessing Impact Analysis for Machine Learning-Based Network Intrusion Detection,” Sak. Univ. J. Comput. Inf. Sci., vol. 6, no. 1, pp. 67–79, 2023.

S. M. Sulaiman and A. M. Abdulazeez, “Leveraging of Gradient Boosting Algorithm in Misuse Intrusion Detection using KDD Cup 99 Dataset,” Indones. J. Comput. Sci., vol. 13, no. 1, 2024.




DOI: https://doi.org/10.31326/jisa.v7i1.2056

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Aqeel Hanash Younis, Adnan Mohsin Abdulazeez

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOURNAL IDENTITY

Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Program Studi Teknik Informatika Universitas Trilogi
Publication Schedule: June and December 
Language: Indonesia & English
APC: The Journal Charges Fees for Publishing 
Indexing:  EBSCO , DOAJ, Google Scholar, Arsip Relawan Jurnal Indonesia, Directory of Research Journals Indexing, Index Copernicus International, PKP Index, Science and Technology Index (SINTA, S4) , Garuda Index
OAI address: http://trilogi.ac.id/journal/ks/index.php/JISA/oai
Contact: jisa@trilogi.ac.id
Sponsored by: DOI – Digital Object Identifier Crossref, Universitas Trilogi

In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE), Relawan Jurnal Indonesia, Jurnal Teknologi dan Sistem Komputer (JTSiskom)

 

 

JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.