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A multi-objective sequential three-way decision approach for real-time malware detection

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Abstract

In order to solve the problem that traditional two-way decision based malicious code detection methods fail to consider the influence of decision making under the condition of insufficient information when facing complex and massive data in dynamic environment, this paper proposes a malicious code detection model based on sequential three-way decision. This model introduces sequential three-way decision into the domain of malicious code to avoid the risk of possible error detection due to insufficient information. In order to improve the overall performance of the detection model and eliminate the subjectivity of threshold selection, this paper designs a multi-objective sequential three-way decision model based on the above model, while considering the decision efficiency and decision accuracy of the model. In addition, the multi-objective optimization algorithm is used to solve the model. The simulation results show that the model not only guarantees the detection performance, but also improves the decision efficiency effectively. The real dynamic detection environment is better fitted.

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The data that support the findings of this study are openly available in high-profile competition website kaggle.com and KM Laboratory at https://sites.google.com/site/nckuikm/home.

References

  1. She C, Ma Y, Wang J, Jia L (2017) An improved malicious code intrusion detection method based on target tree for space information network. Int J Distrib Sens Netw 13(12):1550147717747847

    Article  Google Scholar 

  2. Zhang Y, Liu Z, Jiang Y (2020) The classification and detection of malware using soft relevance evaluation. IEEE Trans Reliab 71(1):309–320

  3. Zhu X, Li Q, Chen Z, Zhang G, Shan P (2020) Research on security detection technology for internet of things terminal based on Firmware Code genes. IEEE Access 8:150226–150241

  4. Wong MY, Lie D (2016) IntelliDroid: a targeted input generator for the dynamic analysis of android malware. NDSS 16(2016). https://doi.org/10.14722/ndss.2016.23118

  5. Liu Y, Tantithamthavorn C, Li L, Liu Y (2022) Deep Learn Android Malware Defenses: A systematic literature review[J]. ACM Comput Surv 55(8):1–36

  6. Sethuraman SC (2023) A comprehensive survey on deep learning based malware detection techniques. Comput Sci Rev 47:100529

    Article  MATH  Google Scholar 

  7. Jha S, Prashar D, Long HV, Taniar D (2020) Recurrent neural network for detecting malware. Comput Secur 99:102037

    Article  Google Scholar 

  8. Cui Z, Zhao Y, Cao Y, Cai X, Zhang W, Chen J (2021) Malicious code detection under 5G HetNets based on a multi-objective RBM Model. IEEE Network 35(2):82–87

    Article  Google Scholar 

  9. Yuan B, Wang J, Wu P, Qing X (2022) IoT Malware classification based on Lightweight Convolutional neural networks. IEEE Internet Things J 9(5):3770–3783

    Article  Google Scholar 

  10. Nauman M, Azam N, Yao J (2016) A three-way decision making approach to malware analysis using probabilistic rough sets. Inf Sci 374:193–209

    Article  Google Scholar 

  11. Yang X, Li Y, Li T (2023) A review of sequential three-way decision and multi-granularity learning. Int J Approximate Reasoning 152:414–433

    Article  MathSciNet  MATH  Google Scholar 

  12. Yao Y (2008) Probabilistic rough set approximations. Int J Approximate Reasoning 49(2):255–271

    Article  MATH  Google Scholar 

  13. Zhang Q, Pang G, Wang G (2020) A novel sequential three-way decisions model based on penalty function. Knowl Based Syst 192:105350

    Article  Google Scholar 

  14. Yang X, Li T, Fujita H, Liu D, Yao Y (2017) A unified model of sequential three-way decisions and multilevel incremental processing. Knowl Based Syst 134:172–188

    Article  Google Scholar 

  15. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification deep convolutional neural networks[J]. Adv Neural Inf Proces Syst 25

  16. Wang L, Qian X, Zhang Y, Shen J, Cao X (2020) Enhancing sketch-based image Retrieval by CNN semantic re-ranking. IEEE Trans Cybernetics 50(7):3330–3342

    Article  Google Scholar 

  17. Cui Z, Du L, Wang P, Cai X, Zhang W (2019) Malicious code detection based on CNNs and multi-objective algorithm. J Parallel Distrib Comput 129:50–58

    Article  Google Scholar 

  18. Yu Y, Cao Z, Liu Z, Geng W, Yu J, Zhang W (2022) A Two-Stream CNN with simultaneous detection and segmentation for robotic grasping. IEEE Trans Syst Man Cybernetics: Syst 52(2):1167–1181

    Article  Google Scholar 

  19. Yao Y (2011) The superiority of three-way decisions in probabilistic rough set models. Inf Sci 181(6):1080–1096

    Article  MathSciNet  MATH  Google Scholar 

  20. Yao Y (2015) Rough sets and three-way decisions[C]. In Rough Sets and Knowledge Technology: 10th International Conference, RSKT 2015, Held as Part of the International Joint Conference on Rough Sets, IJCRS 2015, Tianjin, China, November 20-23, 2015, Proceedings 10. Springer International Publishing, pp 62–73

  21. Cai X, Geng S, Wu D, Cai J, Chen J (2021) A multicloud-model-based many-objective intelligent algorithm for efficient task scheduling in internet of things. IEEE Internet Things J 8(12):9645–9653

    Article  Google Scholar 

  22. Yao Y (2013) Granular Computing and Sequential three-way decisions. Rough sets and Knowledge Technology. Springer, Berlin, pp 16–27

    Chapter  Google Scholar 

  23. Li B, Li J, Tang K, Yao X (2015) Many-objective evolutionary algorithms: A survey[J]. ACM Comput Surv (CSUR) 48(1):1–35

  24. Zhang Q, Zhi X, Dai Y, Wang G (2022) Democratic three-way decisions based on voting mechanism. Int J Mach Learn Cybernet 13(1):99–114

    Article  Google Scholar 

  25. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  26. Zhang Q, Xia D, Wang G (2017) Three-way Decis model two types Classif errors. Inform Sci 420:431–453

    Article  MathSciNet  Google Scholar 

  27. Zhang Q, Huang Z, Wang G (2021) A novel sequential three-way decision model with autonomous error correction. Knowl Based Syst 212:106526

    Article  Google Scholar 

  28. Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report, 103

  29. Zhang Q, Li H (2007) MOEA/D: a Multiobjective Evolutionary Algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  30. Cui Z, Xue F, Cai X, Cao Y, Wang Gg, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Industr Inf 14(7):3187–3196

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61806138); the Central Financial Guidance for Local Science and Technology Development Fund (Grant No. YDZJSX2021A038); and the Key R&D Program of Shanxi Province (International Cooperation), Grant No. 201903D421048.

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Authors and Affiliations

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China

    Zhuoxuan Lan, Binquan Zhang, Jie Wen & Zhihua Cui

  2. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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  1. Zhuoxuan Lan
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  2. Binquan Zhang
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  3. Jie Wen
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  4. Zhihua Cui
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Correspondence to Zhihua Cui.

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Lan, Z., Zhang, B., Wen, J. et al. A multi-objective sequential three-way decision approach for real-time malware detection. Appl Intell 53, 28865–28878 (2023). https://doi.org/10.1007/s10489-023-05049-7

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