Machine Learning: Research on Detection of Network Security Vulnerabilities by Extracting and Matching Features
DOI:
https://doi.org/10.13052/jcsm2245-1439.1254Keywords:
machine learning, network security, vulnerability detection, extracting features, convolutional neural network, matching featuresAbstract
The existence of vulnerabilities is a serious threat to the security of networks, which needs to be detected timely. In this paper, machine learning methods were mainly studied. Firstly, network security vulnerabilities were briefly introduced, and then a Convolutional Neural Network (CNN) + Long Short-Term Memory (LSTM) method was designed to extract and match vulnerability features by preprocessing vulnerability data based on National Vulnerability Database. It was found that the CNN-LSTM method had high training accuracy, and its recall rate, precision, F1, and Mathews correlation coefficient (MCC) values were better than those of support vector machine and other methods in detecting the test set; its F1 and MCC values reached 0.8807 and 0.9738, respectively; the F1 value was above 0.85 in detecting different categories of vulnerabilities. The results demonstrate the reliability of the CNN-LSTM method for vulnerability detection. The CNN-LSTM method can be applied to real networks.
Downloads
References
M. R. Neamah, J. Kh-Madhloom, O. A. Hassen, Z. Z. Abidin, O. A. Hassen, ‘Fuzzy Logic Integrated Security Aware Algorithm for Vulnerability Avoidance in Network Environment’, J. Adv. Res. Dyn. Control Syst., 10(10-Special Issue), pp. 785–794, 2018.
L. Wang, R. Abbas, F. M. Almansour, G. S. Gaba, R. Alroobaea, M. Masud, ‘An empirical study on vulnerability assessment and penetration detection for highly sensitive networks’, J. Intell. Syst., 30(1), pp. 592–603, 2021.
J. Li, J. Chen, M. Huang, M. Zhou, W. Xie, Z. Zeng, S. Chen, Z. Zhang, ‘An Integration Testing Framework and Evaluation Metric for Vulnerability Mining Methods’, China Commun., 15(2), pp. 190–208, 2018.
A. Abdul Rasheed, ‘Vulnerability detection towards protecting intrusion by Social Network Analysis approach,’ 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, pp. 1219–1224, 2021.
K. Chhillar, S. Shrivastava, ‘Vulnerability Scanning and Management of University Computer Network,’ 2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), Jaipur, India, pp. 01–06, 2021.
H. Hanif, M. Nasir, M. Ab Razak, A. Firdaus, N. B. ANuar, ‘The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches’, J. Netw. Comput. Appl., 179(9), pp. 1–24, 2021.
G. Lin, S. Wen, Q. L. Han, J. Zhang, Y. Xiang, ‘Software Vulnerability Detection Using Deep Neural Networks: A Survey’, P. IEEE, 108(10), pp. 1825–1848, 2020.
Y. Xiao, Z. Xu, W. Zhang, C. Yu, L. Liu, W. Zou, Z. Yuan, Y. Liu, A. Piao, W. Huo, ‘VIVA: Binary Level Vulnerability Identification via Partial Signature’, 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 213–224, Honolulu, HI, USA, 2021.
V. A. Minaev, I. D. Korolev, A. V. Mazin, S. A. Konovalenko, ‘Model of vulnerability identification in unstable network interactions with automated system’, Radio Ind. (Russia), 28(2), pp. 48–57, 2018.
P. Nancy, S. Muthurajkumar, S. Ganapathy, S. V. N. Santhosh Kumar, M. Selvi, K. Arputharaj, ‘Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks’, IET Commun., 14(5), pp. 888–895, 2020.
J. Ren, Z. Zheng, Q. Liu, Z. Wei, H. Yan, ‘A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning’, Secur. Commun. Netw., 2019, pp. 1–13, 2019.
V. Kovtun, I. Izonin and M. Greguš, ‘Model of Information System Communication in Aggressive Cyberspace: Reliability, Functional Safety, Economics’, IEEE Access, 10, pp. 31494–31502, 2022.
M. Alqarni, A. Azim, ‘Software source code vulnerability detection using advanced deep convolutional neural network’, CASCON ’21: Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, pp. 226–231, 2021.
S. Chakraborty, R. Krishna, Y. Ding, B. Ray, ‘Deep Learning Based Vulnerability Detection: Are We There Yet?’, IEEE T. Software Eng., 48(9), pp. 3280–3296, 2022.
K. Munonye, M. Peter, ‘Machine learning approach to vulnerability detection in OAuth 2.0 authentication and authorization flow’, Int. J. Inf. Secur., 21(2), pp. 223–237, 2022.
M. Begum, M. Arock, ‘Efficient Detection Of SQL Injection Attack(SQLIA) Using Pattern-based Neural Network Model’, 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 343–347, 2021.
H. C. Chen, A. Nshimiyimana, C. Damarjati, P. H. Chang, ‘Detection and Prevention of Cross-site Scripting Attack with Combined Approaches’, 2021 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1–4, Jeju, Korea, 2021.
B. Biswas, A. Mukhopadhyay, ‘G-RAM Framework for Software Risk Assessment and Mitigation Strategies in Organizations’, J. Enterp. Inf. Manag., 31(2), pp. 276–299, 2018.
N. Bi, J. Tan, J. H. Lai, C. Suen, ‘High-Dimensional Supervised Feature Selection via Optimized Kernel Mutual Information’, Expert Syst. Appl., 108(OCT.), pp. 81–95, 2018.
H. Vranken, H. Alizadeh, ‘Detection of DGA-Generated Domain Names with TF-IDF’, Electronics, 11(3), pp. 1–28, 2022.
L. Nguyen, H. H. Chung, K. V. Tuliao, T. M. Y. Lin, ‘Using XGBoost and Skip-Gram Model to Predict Online Review Popularity’, SAGE Open, 10(4), pp. 215824402098331, 2020.
J. Zhao, ‘Efficiency of Corporate Debt Financing Based on Machine Learning and Convolutional Neural Network’, Microprocess. Microsy., 83(no.1), pp. 1–5, 2021.
V. Kumar, R. S. Singh, Y. Dua Y, ‘Morphologically dilated convolutional neural network for hyperspectral image classification’, Signal Process. Image, 101, pp. 116549–, 2022.
S. O. Michael, S. S. Juan, E. Mit, ‘Preliminary Evaluation of Convolutional Neural Network Acoustic Model for Iban Language Using NVIDIA NeMo’, J. Telecomm. Inform. Technol., pp. 43–53, 2022.
I. Shahin, N. Hindawi, A. B. Nassif, A. Alhudhaif, K. Polat, ‘Novel dual-channel long short-term memory compressed capsule networks for emotion recognition’, Expert Syst. Appl., vol. 188, pp. 1–52, 2022.
K. S. Kumar, B. R. Gomathi, ‘Forecasting Photovoltaic Power Output Using Long Short-Term Memory and Neural Network Models’, Int. J. Eng. Appl., 10(2), pp. 116–125, 2022.
K. Abhishek, G. Hamarneh, ‘Matthews Correlation Coefficient Loss For Deep Convolutional Networks: Application To Skin Lesion Segmentation’, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 225–229, Nice, France, 2021.
L. B. Zhang, F. Peng, L. Qin, M. Long, ‘Face spoofing detection based on color texture Markov feature and support vector machine recursive feature elimination’, J. Vis. Commun. Image R., 51(feb.), pp. 56–69, 2018.
D. Alis, C. Alis, M. Yergin, C. Topel, O. Asmakutlu, O. Bagcilar, Y. D. Senli, A. Ustundag, V. Salt, S. N. Dogan, M. Velioglu, H. H. Selcuk, B. Kara, C. Ozer, I. Oksuz, O. Kizilkilic, E. Karaarslan, ‘A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT’, Sci. Rep., 12(1), pp. 1–9, 2022.
A. Sehgal, N. Kehtarnavaz, ‘A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection’, IEEE Access, 6, pp. 9017–9026, 2018.
P. Ramaraj, ‘A Neural Network in Convolution with Constant Error Carousel Based Long Short Term Memory for Better Face Recognition’, Turk. J. Comput. Math. Educ., 12(2), pp. 2042–2052, 2021.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Cyber Security and Mobility
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
