Neural Network Based Attack Path Prediction Using Machine Learning in Cyber Security System
Keywords:
IDS, UNSW-NBIS, ANN, Cyber Security, ICF-GAN.Abstract
People are more susceptible to cyber dangers since our culture is becoming more and more dependent on internet access for everyday chores. The demand for strong security measures is growing as technology progresses. More and more people are looking for antivirus software or other systems that can identify new threats. An intrusion detection system is one popular option; it makes use of cutting-edge technology like ICF-GAN and Artificial Neural Network (ANN). Various types of network intrusions may be detected and prevented by this technology because it allows the monitoring of patterns of traffic and the identification of abnormalities, an Intrusion Detection System (IDS) is beneficial for any network. Increasing the precision of identifying breaches and facilitating preventative actions against possible dangers are the primary goals of the current body of research in this area. Datasets such as the UNSW-NB15 are often used for preliminary data analysis to help with this kind of study. For researchers interested in monitoring network traffic and finding patterns that might indicate security issues, this dataset is a great resource. The goal is to build a system that reliably detects problems, protects against attackers and immediately takes measures to solve information security problems.
Downloads
References
Kiran, S. W. Prakash, B. A. Kumar, Likhitha, T. Sameeratmaja and U. S. S. R. Charan, "Intrusion Detection System Using Machine Learning," 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2023, pp. 1-4, doi: 10.1109/ICCCI56745.2023.10128363.
M. Kohler and B. Kohler, "Analysis of Convolutional Neural Network Image Classifiers in a Rotationally Symmetric Model," in IEEE Transactions on Information Theory, vol. 69, no. 8, pp. 5203-5218, Aug. 2023, doi: 10.1109/TIT.2023.3262745.
P.Zhang, G. Tian and H. Dong,"Research on network intrusion detection based on Whitening PCA and CNN," 2023 7th International Conference on Smart Grid and Smart Cities (ICSGSC), Lanzhou, China, 2023, pp. 232-237, doi:10.1109/ICSGSC59580.2023.10319169.
G. de la Cruz, M. Lira, O. Luaces and B. Remeseiro, "Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection," in IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5130-5140, April 2024, doi: 10.1109/TNNLS.2022.3202643.
Xiang, C. Zhang, J. Wang and B. Wang, "Network Intrusion Detection Method for Secondary System of Intelligent Substation based on Semantic Enhancement," 2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT), Shanghai, China, 2022, pp. 796-800,doi:10.1109/CEECT55960.2022.10030264
J. Li, "Network Intrusion Detection Algorithm and Simulation of Complex System in Internet Environment," 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2022, pp. 520-523, doi: 10.1109/ICIRCA54612.2022.9985720.
Chen, X. Xu, G. Wang and L. Yang,"Network intrusion detection model based on neural network feature extraction and PSO-SVM," 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, 2022, pp. 1462- 1465, doi:10.1109/ICSP54964.2022.9778404.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol,Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion,‖ p. 38.
N. Moustafa and J. Slay, ―The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set,‖ Inf. Secur. J. Glob. Perspect., vol. 25, no. 1–3, pp. 18–31, Apr. 2016, doi: 10.1080/19393555.2015.1125974.
J. Ren, J. Guo, W. Qian, H. Yuan, X. Hao, and H. Jingjing, ―Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms,‖ Secur. Commun. Netw., vol. 2019, p. e7130868, Jun. 2019, doi: 10.1155/2019/7130868.
C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano,―RUSBoost: A Hybrid Approach to Alleviating Class Imbalance,‖ IEEE Trans. Syst. Man Cybern. - Part Syst. Hum., vol. 40, no. 1, pp. 185–197, Jan. 2010, doi: 10.1109/TSMCA.2009.2029559.
Y. Yang, K. Zheng, C. Wu, X. Niu, and Y. Yang, ―Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks,‖ Appl. Sci., vol. 9, no. 2, Art. no. 2, Jan. 2019, doi: 10.3390/app9020238.
K. Jiang, W. Wang, A. Wang, and H. Wu, ―Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network,‖ IEEE Access, vol. 8, pp. 32464–32476, 2020, doi: 10.1109/ ACCESS.2020.2973730.
S. M. Kasongo and Y. Sun, ―Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset,‖ J. Big Data, vol. 7, no. 1, p. 105, Dec. 2020, doi: 10.1186/s40537-020-00379-6.
S. Almogren, ―Intrusion detection in Edge-of-Things computing,‖ J. Parallel Distrib. Comput., vol. 137, pp. 259–265, Mar. 2020, doi: 10.1016/j.jpdc.2019.12.008.
S. Doshi, ―Analyze the data through data visualization using Seaborn,‖ Medium, Feb. 09, 2019. https://towardsdatascience.com/analyze-the-data- through-data-visualization-using-seaborn-255e1cd3948e (accessed Oct. 21, 2022).
Mohan, A., Dinesh Kumar, R. and J., S. 2023. Simulation for Modified Bitumen Incorporated with Crumb Rubber Waste for Flexible Pavement. International Journal of Intelligent Systems and Applications in Engineering. 11, 4s (Feb. 2023), 56–60.
R.Gopalakrishnan, Mohan, “Characterization on Toughness Property of Self-Compacting Fibre Reinforced Concrete”, Journal of Environmental Protection and Ecology 21, No 6, 2153–2163 (2020).
Vidhya Lakshmi Sivakumar, A.S. Vickram, Ragi Krishnan, Titus Richard, "AI-Enhanced Decision Support Systems for Optimizing Hazardous Waste Handling in Civil Engineering," SSRG International Journal of Civil Engineering, vol. 10, no. 11, pp. 1-8, 2023. Crossref, https://doi.org/10.14445/23488352/IJCE-V10I11P101
V. Sundaram, Vidhya Lakshmi Sivakumar, Anand Raju, A. Saravanan, Titus Richard, "The Role of 3D Printing in Engineering and its Historical Perspective in Civil Engineering - A Review," SSRG International Journal of Civil Engineering, vol. 10, Vidhya Lakshmi Sivakumar, A.S. Vickram, Ragi Krishnan, Titus Richard, "AI-Enhanced Decision Support Systems for Optimizing Hazardous Waste Handling in Civil Engineering," SSRG International Journal of Civil Engineering, vol. 10, no. 11, pp. 1-8, 2023. Crossref, https://doi.org/10.14445/23488352/IJCE-V10I11P101
V. Sundaram, Vidhya Lakshmi Sivakumar, Anand Raju, A. Saravanan, Titus Richard, "The Role of 3D Printing in Engineering and its Historical Perspective in Civil Engineering - A Review," SSRG International Journal of Civil Engineering, vol. 10, no. 12, pp. 58-68, 2023. Crossref, https://doi.org/10.14445/23488352/IJCE-V10I12P107 no. 12, pp. 58-68, 2023. Crossref, https://doi.org/10.14445/23488352/IJCE-V10I12P107
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
