Enhancing The Performance of Intrusion Detection Using CNN And Reduction Techniques
DOI:
https://doi.org/10.29304/jqcm.2023.15.2.1234Keywords:
Artificial Intelligent, Artificial Intelligent CNN Algorithm, PCA, Deep Learning, Intrusion Detection UNSW-NB15Abstract
There have been several security solutions based on artificial intelligence (AI), such as intrusion detection systems (IDS),cyberattacks are increasing because big data is increasing by using the internet on all sides of life, therefore, unbalanced data poses a serious problem in intrusion detection systems. The proposed detection system that is based on deep learning Convolutional Neural Network(CNN )partitions data into training and testing., Creating the classifier model for the Principal Component Analysis (PCA )technique of reducing features, is required for the development of intelligent analytic tools that need data pre-treatments and deep learning algorithm-performance enhancement. The UNSW-NB15 data set is used According to experimental findings, We employed a number of evaluation tools to assess the proposed NIDCNN strategy relying on the UNSW-NB15 data set that takes 30% of it for testing and after processing this part of the data became used to evaluate the proposed system. Measures such as a classifier's F-Score, precision, and sensitivity (Recall) are evaluated. Classifier performs better than the other approaches at determining if the data stream is normal or malicious. which is used to assess deep learning's effectiveness, the suggested model results from a high level of accuracy. The experimental findings demonstrate the suggested system's ability to accelerate the intrusion detection process while reducing memory and CPU usage. Experimental results prove the theoretical considerations.Because the UNSW-NB15 data set contains a wide range of patterns that accurately represent contemporary real network traffic, New NIDS algorithms can therefore be assessed using it.
Downloads
References
[2]P. Garcı´a-Teodoro et al.,” Anomaly-based network intrusion detection: Techniques, systems and challenges” computers & security 28 (2009) 18–28.
[3] Salima Omar et al.,” Machine Learning Techniques for Anomaly Detection: An Overview” International Journal of Computer Applications (0975 – 8887), October 2013.
[4] M. Ferrag et al., “Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study,” Journal of Information Security and Applications, 2020.
[5]Mohammad Sazzadul Hoque et al.,” an implementation of intrusion detection system using genetic algorithm” international journal of network security & its applications (ijnsa), march 2012.
[6] M. e. al., "Deep Learning for Cyber Security Intrusion Detection :Approaches ,Datasets , and Comparative Study," Journal of Information Security and Applications, 2020.
[7] a. N. M. Hazem M.EL-Bakry, "Areal time intursion detection algorithm for network security," WSEAS Transactions on communications , pp. 1222-1234, december 2008.
[8] R. Almarshdi et al. “Hybrid convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning model for detecting shilling attack in the social-aware network “pages1197–1210,2021.
[9] M. Hassan et al. ,”A hybrid deep learning model for efficient intrusion detection in big data environment”, 386-396, March 2020.
[10] P. Wu and H. Guo, "LuNet: A Deep Neural Network for Network Intrusion Detection," [cs.AI] 6 Oct 2019.
[11] M. Azizjon et al., “1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data,” International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, 19-21 February 2020.
[12]A. Alsaheel et al., “ATLAS: A Sequence-Based Learning Approach or Attack Investigation,” Proceedings of the 30th USENIX Security Symposium, pp. 3005–3022, August 2021.
[13] L. Ashiku and C. Dagli, "Network Intrusion Detection System using Deep Learning," Complex Adaptive Systems Conference Theme: Big Data, IoT, and AI for a Smarter Future Malvern, Pennsylvania, June 16-18, 2021.
[14] M. Hooshmand and D. Hosahalli, “Network Anomaly Detection Using Deep Learning Techniques,” CAAI Transactions on Intelligence Technology, 228–243, 2022.
[15] F. Sultana et al., “Advancements in Image Classification Using Convolutional Neural Network,” in Proceedings of the 2018 4th International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), p122–129,Kolkata, India, November 2018.
[16] A. Oprea, “The Use of Principal Component Analysis (PCA) in Building Yield Curve Scenarios and Identifying Relative-Value Trading Opportunities on the Romanian Government Bond Market,” Journal of Risk and Financial Management. 247, 2022.
[17] Lin, Y., et al., “ The individual identification method of wireless device based on dimensionality reduction and machine learning”, The Journal of Supercomputing, 3010-3027,2017.
[18] Verleysen M., François D,”The Curse of Dimensionality in Data Mining and Time “,Computational Intelligence and Bioinspired Systems. , 2005.
[19] Zebari , R., etal.,.” A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction”. Journal of Applied Science and Technology Trends, 56 – 70,2020.
[20] Mahmood, M. R. and Abdulazeez, A. M. “Different Model for Hand Gesture Recognition with a Novel Line Feature Extraction”,. 52–57,2019, doi: 10.1109/ICOASE.2019.8723731.
[21] Arif, M. T. Book Review, Asia Pacific J. Public Heal., pp. 507–508,2010, doi: 10.1177/1010539510380245.
[22] Ning, C. and You, F. “Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods”, Comput. Chem. Eng., pp. 190–210, doi: 10.1016/j.compchemeng.2018.
[23] Gajjar, S., Kulahci, M. and Palazoglu, A. “Real-time fault detection and diagnosis using sparse principal component analysis,” J. Process Control, 112–128, 2018.
[24] M. L. e. al., "Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment,", p. 796, 2017.
[25] Pořízka, P., et ,al.,”. On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis”, , pp. 65–82, doi: 10.1016/j.sab.2018..
[26] Liu, J., Wang, et,al., “Advanced Energy Storage Devices Basic Principles”, Analytical Methods, and Rational Materials Design, 2017.
[27] Fujiwara, T., et,al.,. “Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning”,Comput. Graph., pp. 45–55,2020.
[28] Jackson, J. E. “A User’ s Guide to Principal Components”. by J. Edward Jackson Review by: Brian D. 1991.
[29] F. Sultana et al., “Advancements in Image Classification Using Convolutional Neural Network,” 4th International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 122–129, Kolkata, India, November 2018.
[30]S. Brunton and J. Kutz, “Singular Value Decomposition (SVD),” Data-Driven Science and Engineering (pp.3-46), 2019.
[31] Sadiq Hussain et al.,” Prediction Model on Student Performance based on Internal Assessment using Deep Learning”, iJET ‒ , 2019.
[32] Mathieu Lepot et al.,” Interpolation in Time Series: An IntroductiveOverview of Existing Methods, Their Performance Criteria and Uncertainty Assessment”, www.mdpi.com/journal/water, 2017.
