Cited By
View all- Aroon NLiu VKane LLi YTesfamicael AMcKague M(2024)An Architecture of Enhanced Profiling Assurance for IoT NetworksElectronics10.3390/electronics1314283213:14(2832)Online publication date: 18-Jul-2024
Research on Multi-granularity Intrusion Detection Algorithm Based onSequential Three-Way Decision
Pages 1154 - 1160
Abstract
Intrusion detection is one of the significant research directions in the field of network information security, which has received widespread attention in academia and industry. How to distinguish the nature of network behavior is the important content of intrusion detection research. In view of this, the paper proposes Multi-Granularity intrusion detection algorithm based on Sequential Three-Way Decision (S3WD-MG). Firstly, the feature sets of different granularity are obtained through auto-encoding; Secondly, the relevant identification information in the features extracted by the auto-encoder network will increase with training time, thus forming a multi-granularity feature set; Finally, based on the sequential three-way decision theory and the decision threshold, the most appropriate decision is made for network behavior. The experimental consequence on the NSL-KDD test set shows that, S3WD-MG has higher detection rate and stronger robustness than other model in intrusion detection.
References
[1]
YANG Y, ZHENG K, WU C, et al. Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network[J]. Sensors, 2019, 19(11): 2528.
[2]
(LI Y Z, WANG R S, ZHANG N G, et al. Intrusion detection technology based on semi-supervised fuzzy cluster[J]. 2010, 24(04): 381--385.)
[3]
ZEGEYE W K, DEAN R A, MOAZZAMI F. Multi-layer hidden markov model based intrusion detection system[J]. Machine Learning and Knowledge Extraction, 2019, 1(1): 265--286.
[4]
LU M X, DU G Z, JI Z X. Network intrusion detection based on deep transfer learning[J]. Application Research of Computers, 2020, 37(09):2811--2814.
[5]
VIJAYANAND R, DEVARAJ D, KANNAPIRAN B. Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection[J]. Computers & Security, 2018, 77: 304--314.
[6]
WANG S, JIN Z G. IDS classification algorithm based on fuzzy SVM model[J]. Application Research of Computers, 2020, 37(02):501--504.
[7]
ABUROMMAN A, REAZ M B I. A survey of intrusion detection systems based on ensemble and hybrid classifiers[J]. Computers & Security, 2017, 65: 135--152.
[8]
PAPAMARTZIVANOS D, MÁRMOL F G, KAMBOURAKIS G. Introducing deep learning self-adaptive misuse network intrusion detection systems[J]. IEEE Access, 2019, 7: 13546--13560.
[9]
BENMESSAHEL I, XIE K, CHELLAL M, et al. A new evolutionary neural networks based on intrusion detection systems using locust swarm optimization[J]. Evolutionary Intelligence, 2019, 12(2): 131--146.
[10]
YAO Y. Three-way decisions with probabilistic rough sets[J]. Information sciences, 2010, 180(3): 341--353.
[11]
ZHANG L B, LI H X, ZHOU X Z, et al. Multi-granularity cost-sensitive three-way decision for face recognition[J]. Journal of Shandong University: Natural Science, 2014, 49(8): 48--57.
[12]
YAO Y. An outline of a theory of three-way decisions[C]//International Conference on Rough Sets and Current Trends in Computing. Springer, Berlin, Heidelberg, 2012: 1--17.
[13]
ZHANG G Q, LIU Q, JI L H. Multi-granularity sentiment classification method based on sequential three-way decisions[J]. Computer Science, 2018, 45(12): 153--159.
[14]
YAO Y. Granular computing and sequential three-way decisions[C]//International Conference on Rough Sets and Knowledge Technology. Springer, Berlin, Heidelberg, 2013: 16--27.
[15]
WU Q, TAN S. A two-stage framework for cross-domain sentiment classification[J]. Expert Systems with Applications, 2011, 38(11): 14269--14275.
[16]
LI H, ZHOU X, HUANG B, et al. Cost-sensitive three-way decision: a sequential strategy[C]//International Conference on Rough Sets and Knowledge Technology. Springer, Berlin, Heidelberg, 2013: 325--337.
[17]
ZHANG L, LI H, ZHOU X, et al. Sequential three-way decision based on multi-granular autoencoder features[J]. Information Sciences, 2020, 507: 630--643.
[18]
BENGIO Y, COURVILLE A, VINCENT P. Representation learning: A review and new perspectives[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8): 1798--1828.
[19]
LIU D, LIANG D. Generalized three-way decisions and special three-way decisions[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(2015): 502--510.
[20]
GHANEM W A H M, Jantan A, Ghaleb S A A, et al. An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons[J]. IEEE Access, 2020, 8: 130452--130475.
[21]
SHONE N, NGOC T N, PHAI V D, et al. A deep learning approach to network intrusion detection[J]. IEEE transactions on emerging topics in computational intelligence, 2018, 2(1): 41--50.
[22]
LI Y, ZHANG S, LI Y, et al. Research on intrusion detection algorithm based on deep learning and semi-supervised clustering[J]. International Journal of Cyber Research and Education (IJCRE), 2020, 2(2): 38--60.
[23]
GAO N, GAO L, HE Y, et al. A lightweight intrusion detection model based on autoencoder network with feature reduction [J]. Chinese Journal of Acta Electronica Sinica, 2017, 45(3): 7301--739.
[24]
GAO X, SHAN C, HU C, et al. An adaptive ensemble machine learning model for intrusion detection[J]. IEEE Access, 2019, 7: 82512--82521.
Index Terms
- Research on Multi-granularity Intrusion Detection Algorithm Based onSequential Three-Way Decision
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October 2021
1723 pages
ISBN:9781450384322
DOI:10.1145/3501409
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Association for Computing Machinery
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Published: 31 December 2021
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EITCE 2021
EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering
October 22 - 24, 2021
Xiamen, China
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EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
Overall Acceptance Rate 508 of 972 submissions, 52%
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View all- Aroon NLiu VKane LLi YTesfamicael AMcKague M(2024)An Architecture of Enhanced Profiling Assurance for IoT NetworksElectronics10.3390/electronics1314283213:14(2832)Online publication date: 18-Jul-2024
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