Intrusion Detection System through ID3-PCA-BP

Authors

  • L K Suresh Kumar

Keywords:

Intrusion Detection, Machine Learning, Iterative Dichotomiser 3, Principal Component Analysis, Back Propagation

Abstract

Currently, problems such as a slow processing speed, a low detection rate, a high false- positive rate, and a big feature dimension are all part of intrusion detection. To handle these issues, Iterative Dichotomiser 3 (ID3), Principal Component Analysis (PCA), and Back Propagation (BP) algorithms are available. Through a lower false-positive rate, and a higher detection rate, the research-based intrusion detection model ID3-PCA-BP increases the processing speed of intrusion detection systems. To decrease the overall data volume and accelerate processing, ID3 is used to initially differentiate the data. Differentiate ID3s and keep the temporary training sample set for intrusion data in order to retrain and optimize the ID3 and BP, treat the ID3 judges as standard data, and delete the added intermediate data. Then, we should reduce the dimension of the data using PCA and then introduce the data to BP for secondary discrimination. However, the ID3 algorithm operates a shallow structure in order to prevent an extreme quantity of intermediate numbers from being analyzed as intrusion data. As a result, further BP processing cannot effectively raise the accuracy. BP accelerates data processing by employing the ReLU activation function from the simplified neural network calculation approach and the faster convergence ADAM optimization algorithm.

References

K. Shaukat, S. Luo, V. Varadharajan, I. A. Hameed, and M. Xu, “A survey on machine learning techniques for cybersecurity in the last decade,” IEEE Access, vol. 8, pp. 222310– 222354, 2020.

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

I. Sembiring, “Implementation of honeypot to detect and prevent distributed denial of service attack,” in Proceedings of the 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 345– 350, Semarang, Indonesia, October 2016.

M. Crosbie and G. Spafford, “Defending a Computer System Using Autonomous Agents,” Technical Report No. 95-022, Department of Computer Sciences, Purdue University, West Lafayette, Indiana, March 1996.

G. Vigna, E. Jonsson, and C. Kruegel, Eds., RAID 2003, LNCS 2820, pp. 173–191, c Springer-Verlag, Berlin Heidelberg, 2003.

Y. Li, J. Xia, S. Zhang, J. Yan, X. Ai, and K. Dai, “An efficient intrusion detection system based on support vector machines and gradually feature removal method,” Expert Systems with Applications, vol. 39, no. 1, pp. 424–430, 2012.

N. P. Moorthi and V. Mathivanan, “Hybrid optimization for feature selection in opinion mining,” International Journal of Engineering & Technology, vol. 7, no. 1.3, p. 112, 2017.

G. Liu, Z. Yi, and S. Yang, “A hierarchical intrusion detection model based on the PCA neural networks,” Neurocomputing, vol. 70, no. 7-9, pp. 1561–1568, 2007.

J. Cao, C. Wu, L. Chen, H. Cui, and G. Feng, Hindawi Computational Intelligence and Neuroscience, vol. 2019, Article ID 2060796, 12 pages, 2019.

L. Ashiku and C. Dagli, “Network intrusion detection system using deep learning,” Procedia Computer Science, vol. 185, pp. 239–247, 2021.

Y. Gu, B. Zhou, and J. Zhao, “PCA-ICA ensembled intrusion detection system by pareto-optimal optimization,” Information Technology Journal, vol. 7, no. 3, pp. 510– 515, 2008.

M. Soni and D. K. Singh, “privacy preserving authentication and key management protocol for health information system,” Data Protection and Privacy in Healthcare: Research and Innovations, CRC Publication, vol. 37, 2021.

M. Soni and D. K. Singh, “LAKA: Lightweight Authentication and Key Agreement Protocol for Internet of Things Based Wireless Body Area Network,” Wireless Personal Communication, 2021.

M. Soni and D. K. Singh, “Blockchain Implementation for Privacy Preserving and Securing the Healthcare Data,” in Proceedings of the 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), pp. 729– 734, Bhopal, India, June 2021.

M. Soni, G. Dhiman, B. S. Rajput, R. Patel, and N. K. Tejra, “Energy-Effective and Secure Data Transfer Scheme for Mobile Nodes in Smart City Applications,” Wireless Pers Commun, 2021.

Resende P., Drummond A."A survey of random forest based methods for intrusion detection systems", ACM Comput. Surv., 51 (3) (2018), pp. 1-36.

Benkhelifa E., Welsh T., Hamouda W., "A critical review of practices and challenges in intrusion detection systems for IoT: Toward universal and resilient systems", IEEE Commun. Surv. Tutor., 20 (4) (2018), pp. 3496-3509, 10.1109/COMST.2018.2844742.

Liu Q., Li P., Zhao W., Cai W., Yu S., Leung V.C.M., "A survey on security threats and defensive techniques of machine learning: A datadriven view", IEEE Access, 6 (2018), pp. 12103-12117, 10.1109/ACCESS.2018.2805680.

R. Nian, G. Ji, and M. Verleysen, “An unsupervised Gaussian mixture classification mechanism based on statistical learning analysis,” in Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 14–18, Shandong, China, October 2008.

R. Aggarwal and Y. Song, “Artificial neural networks in power systems. Part 1: general introduction to neural computing,” Power Engineering Journal, vol. 11, no. 3, pp. 129– 134, June 1997.

Z.-H. Zhou, Machine Learning, Springer Nature Singapore Pte Ltd, Singapore, 2021.

V. R. Sargsyan, “Formation of human higher nervous activity and new biological theories, HSOA journal of brain & neuroscience research, Sargsyan VR,” J Brain Neursci, vol. 2, p. 004, 2018.

S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” 2017 International Conference on Engineering and Technology (ICET), in Proceedings of the 2017 International Conference on Engineering and Technology (ICET), pp. 1–6, Antalya, Turkey, August 2017.

V. K. Gupta, S. K. Shukla, and R. S. Rawat, “Crime tracking system and people’s safety in India using machine learning approaches,” International Journal of Modern Research, vol. 2, no. 1, pp. 1–7, 2022.

P. K. Vaishnav, S. Sharma, and P. Sharma, “Analytical review analysis for screening COVID-19 disease,” International Journal of Modern Research, vol. 1, no. 1, pp. 22– 29, 2021.

I. Chatterjee, “Artificial intelligence and patentability: review and discussions,” International Journal of Modern Research, vol. 1, no. 1, pp. 15–21, 2021.

KDD cup 1999 data (2021) http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, October 2007.

Tavallaee M., Bagheri E., Lu W., Ghorbani A.A. A detailed analysis of the KDD CUP 99 data set Proceedings of 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, IEEE (2009), pp. 1-6.

Özgür A., Erdem H. A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015 PeerJ Preprints, 4 (2016), p. e1954v1.

X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, “An adaptive ensemble machine learning model for intrusion detection,” IEEE Access, vol. 7, pp. 82512–82521, 2019.

Q. Qin, K. Poularakis, K. K. Leung, and L. Tassiulas, “Line-speed and scalable intrusion detection at the network edge via federated learning,” in Proceedings of the 2020 IFIP Networking Conference (Networking), pp. 352–360, Paris, France, June 2020.

. P R Anisha, Dr. C Kishor Kumar Reddy, “Early Detection of Diabetes using Machine Learning Algorithms and Internet of Things: ADPA”, Springer INDIA, 2019, India.

. Dr B V Ramana Murthy , Kishor Kumar Reddy C, Anisha P R, Rajasekhar Shastry, “IOT Based Smart Stale Food Detector”, Springer INDIA, 2019, India.

. Kishor Kumar Reddy C, Anisha P R, Rajasekhar Shastry, Dr B V Ramana Murthy and Dr Vuppu Padmakar “Automated rainwater Harvesting System”, IEEE ICCES, 2019. [34]. Kishor Kumar Reddy C, Anisha P R, Rajasekhar Shastry, Dr B V Ramana “Comparative Study on Internet of Things: Enablers and Constraints”, Springer-ICDECT, 2019.

. Rajasekhar Shastry, Dr B V Ramana Murthy, Kishor Kumar Reddy C, Anisha P R “ Automated Lighting Smart Parking Using Internet of Things”, Springer-ICICCS, 2019 [36]. R Gangadhara Reddy, M Ramesh Babu, CH jaya Prakash Rao, Kishor Kumar Reddy C , PPSIC: Pulsated Power Supply Inverter Circuit”, IEEE CICN, 2017

. Kishor Kumar Reddy C and Vijaya Babu B, “ISPM-OC: Improved Snow Prediction Model Using Optimal k-Means Clustering and Decision Tree to Nowcast Snow/No-Snow”,

SCESM 2017

. Kishor Kumar Reddy C and Vijaya Babu B, “ISLIQ-OC: Improved Supervised Learning in Quest using Optimal k-means Clustering Mechanism to Nowcast Snow/No- Snow”, SCESM , 2017.

. Kishor Kumar Reddy C, Anisha P R and G V S Raju, “A Novel Methodology to Detect Bone Cancer Stage Using Mean Intensity of MRI Imagery and Region Growing Algorithm”, Springer International Congress on Information and Communication Technology, 2016

. Kishor Kumar Reddy C, Anisha P R and G V S Raju, “Detection of Pancreatic Cancer using Clustering and Wavelet Transform Techniques”, IEEE International Conference on Computational Intelligence and Communication Networks , December 2015.

. Kishor Kumar Reddy C, “A Novel Approach for Detecting the Tumor Size and Bone Cancer Stage using Region Growing Algorithm”, IEEE International Conference on Computational Intelligence and Communication Networks , December 2015.

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Published

2021-12-30

How to Cite

Kumar, L. K. S. . (2021). Intrusion Detection System through ID3-PCA-BP. The Journal of Contemporary Issues in Business and Government, 27(6), 1802–1821. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/2274

Issue

Vol. 27 No. 6 (2021): The journal of contemporary issues in business and government

Section

Articles

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