Cross-Level Network Security Element Fusion Extraction Method Based on Deep Learning
Pages 247 - 254
Abstract
Element extraction of Network Security Situation is the foundation for Network Security Situation awareness. Traditional methods for network security element extraction struggle with efficiently processing large-scale data and establishing effective cross-level element associations, leading to a higher false alarm rate in situation awareness. This paper proposes a cross-level network security element fusion extraction method based on deep learning. Firstly, through a data preprocessing method based on element correlation, we reduce data dimensionality, addressing the challenge of large-scale nonlinear data processing. Secondly, we employ principal component analysis to construct a method for cross-level network security element fusion extraction. Moreover, by integrating convolutional neural networks and long short-term memory networks, we establish a cross-level network security element fusion classification model, achieving precise perception of the Network Security Situation. Experimental results indicate that the proposed cross-level network security element fusion extraction method effectively reduces dataset dimensions, extracts 32-dimensional crucial network security fusion elements, and achieves a detection accuracy rate of up to 99.99% for network attack behaviors, significantly enhancing the capability of network security situation awareness.
References
[1]
S. L. Ullo and G. R. Sinha, “Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications,” Remote Sensing, vol. 13, no. 13, Art. no. 13, Jan. 2021.
[2]
H. Yang, R. Zeng, G. Xu, and L. Zhang, “A network security situation assessment method based on adversarial deep learning,” Applied Soft Computing, vol. 102, p. 107096, Apr. 2021.
[3]
D. Zhao, H. Wang, and Y. Wu, “Situation Element Extraction Based on Fuzzy Rough Set and Combination Classifier,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–18, Aug. 2022.
[4]
Si Cheng, H. Zhang, Y. Wang, and Y. Yang, “Research on Network Security Situational Elements Knowledge Base Model Based on Ontology,” Computer Science, vol. 42, no. 5, pp. 173–177, 2015.
[5]
L. Yang, A. Moubayed, and A. Shami, “MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles,” IEEE Internet Things J., pp. 1–1, 2021.
[6]
A. Ahmim, L. Maglaras, M. A. Ferrag, M. Derdour, and H. Janicke, “A Novel Hierarchical Intrusion Detection System Based on Decision Tree and Rules-Based Models,” in 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), May 2019, pp. 228–233.
[7]
A. Mojtahedi, F. Sorouri, A. N. Souha, A. Molazadeh, and S. S. Mehr, “Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification.” arXiv, Jan. 03, 2022. Accessed: Sep. 14, 2022. [Online]. Available: http://arxiv.org/abs/2201.00584
[8]
T. Wu, H. Fan, H. Zhu, C. You, H. Zhou ., “Intrusion detection system combined enhanced random forest with SMOTE algorithm,” EURASIP Journal on Advances in Signal Processing, vol. 2022, no. 1, p. 39, May 2022.
[9]
T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Computers and Electrical Engineering, vol. 99, p. 107810, Apr. 2022.
[10]
M. Yu, T. Quan, Q. Peng, X. Yu, and L. Liu, “A model-based collaborate filtering algorithm based on stacked AutoEncoder,” Neural Comput & Applic, vol. 34, no. 4, pp. 2503–2511, Feb. 2022.
[11]
Y. Yin, J. Jang-Jaccard, X Wen, A. Singh. J. Zhu, “IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset,” J Big Data, vol. 10, no. 1, p. 15, Feb. 2023.
[12]
S. Marukatat, “Tutorial on PCA and approximate PCA and approximate kernel PCA,” Artif Intell Rev, vol. 56, no. 6, pp. 5445–5477, Jun. 2023.
[13]
L. Yang and A. Shami, “A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles,” in ICC 2022 - IEEE International Conference on Communications, May 2022, pp. 2774–2779.
[14]
F. Laghrissi, S. Douzi, K. Douzi, and B. Hssina, “Intrusion detection systems using long short-term memory (LSTM),” J Big Data, vol. 8, no. 1, p. 65, Dec. 2021.
[15]
J. Zhang, L. Pan, Q.-L. Han, C. Chen, S. Wen ., “Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 377–391, Mar. 2022.
[16]
K. Vamsi Krishna, K. Swathi, B. Basaveswara Rao, “A Novel Framework for NIDS Throuh Fast Knn Classifier on CICIDS 2017 Dataset,” IJRTE, vol. 8, no. 5, pp. 3669–3675, Jan. 2020.
Index Terms
- Cross-Level Network Security Element Fusion Extraction Method Based on Deep Learning
Recommendations
Network Security Situation Awareness Using Exponential and Logarithmic Analysis
IAS '09: Proceedings of the 2009 Fifth International Conference on Information Assurance and Security - Volume 02Network security situation awareness (NSSA) is a hotspot in the network security research field, based on the security situation values, decision makers can be aware of the actual security situation of their networks and then make rational decision to ...
CNSSA: A Comprehensive Network Security Situation Awareness System
TRUSTCOM '11: Proceedings of the 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and CommunicationsWith tremendous attacks in the Internet, there is a high demand for network analysts to know about the situations of network security effectively. Traditional network security tools lack the capability of analyzing and assessing network security ...
Network security situation: From awareness to awareness-control
AbstractNetwork Security Situation Awareness (NSSA) is a security theory which can perceive the network threat from a global perspective. In this paper, we present a Cognitive Awareness-Control Model (CACM) for NSSA. CACM adopts the cross-...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
September 2023
370 pages
ISBN:9798400708800
DOI:10.1145/3617184
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 December 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- The National Natural Science Foundation of China
- The Natural Science Foundation of Jilin Province, China
Conference
ICCSIE 2023
ICCSIE 2023: 8th International Conference on Cyber Security and Information Engineering
September 22 - 24, 2023
Putrajaya, Malaysia
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 23Total Downloads
- Downloads (Last 12 months)23
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Nov 2024
Other Metrics
Citations
Cited By
View allView Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format