Abstract
The system of mobile agents is a widely used model for distributed computations and distributed applications. Mobile agents are assuming critical jobs as they are able to ship their state starting with one platform then onto the next, with their information unblemished, and are equipped for performing fittingly in the new platform’s conditions. Albeit mobile agents are used extensively, they can transport with them obscure malign codes leading to the agent-hosting platform’s disruption. To detect any malicious addition to the code being carried, we propose a model named Malicious API Call Identifier (MACI) model which uses the API function call strings that can mirror the functional qualities of a program and can be used to find malicious codes. Certain particular malicious functions (present in API call sequences) are commonly included in these malicious codes which the agent carries. The MACI model uses the n-gram analysis generally used on text/words for NLP tasks to find inherent patterns/coordination among the function calls present in each API call sequence. The model tests four classifiers based on five evaluation metrics, some with ensemble techniques at optimum parameters with tenfold cross-validation. The model applies a suitable feature reduction technique and achieves up to 98.31% accuracy and a 2.89% miss rate to outperform the other state-of-the-art works done in detecting malign APIs.
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References
Singh AK, Gupta I (2020) Online ınformation leaker ıdentification scheme for secure data sharing. Multimed Tools Appl 79(41):31165–31182
Gupta I, Singh AK (2020) SELI: statistical evaluation based leaker ıdentification stochastic scheme for secure data sharing. IET Commun 14(20):3607–3618
Kaur K, Gupta I, Singh AK (2017) A comparative study of the approach provided for preventing the data leakage. Int J Netw Secur Appl (IJNSA) 9(5):21–33
Kaur K, Gupta I, Singh AK (2018) Data leakage prevention: email protection via gateway. J Phys: Conf Ser 933(1). IOP Publishing
Gupta I, Singh AK (2019) A confidentiality preserving data leaker detection model for secure sharing of cloud data using ıntegrated techniques. In: Seventh ınternational conference on smart computing and communication systems (ICSCC), Sarawak, Malaysia. IEEE, pp 1–5
Gupta I, Singh AK (2020) A framework for malicious agent detection in cloud computing environment. Int J Adv Sci Technol (IJAST) 135, 49–62
Gupta I, Gupta R, Singh AK, Buyya R (2020) MLPAM: a machine learning and probabilistic analysis based model for preserving security and privacy in cloud environment. IEEE Syst J
Gupta I, Singh AK (2020) An integrated approach for data leaker detection in cloud environment. J Inf Sci Eng 36(5):993–1005
Gupta I, Singh N, Singh AK (2019) Layer-based privacy and security architecture for cloud data sharing. J Commun Softw Syst (JCOMSS) 15(2):173–185
Ali M, Shiaeles S, Bendiab G, Ghita B (2020) MALGRA: machine learning and N-Gram malware feature extraction and detection system. Electronics 9(11). MDPI
Han W, Xue J, Wang Y, Huang L, Kong X, Mao L (2019) MalDAE: detecting and explaining malware based on correlation and fusion of static and dynamic characteristics. Comput Secur 83, 208–233
Ma X, Guo S, Bai W, Chen J, Xia S, Pan Z (2019) An API semantics-aware malware detection method based on deep learning. Secur Commun Netw 1–9
Hardy W, Chen L, Hou S, Ye Y, Li X (2016) DL4MD: a deep learning framework for intelligent malware detection. In: International conference on data mining DMIN’16
Liu C, Zhang Z, Wang S (2016) An android malware detection approach using bayesian inference. In: IEEE international conference on computer and information technology (CIT), Nadi. IEEE, pp 476–483
Gupta I, Singh AK (2020) GUIM-SMD: guilty user identification model using summation matrix based distribution. IET Inf Secur 14(6):773–782
Gupta I, Singh AK (2019) Dynamic threshold based ınformation leaker ıdentification scheme. Inf Process Lett 147, 69–73
Gupta I, Singh AK (2018) A probabilistic approach for guilty agent detection using bigraph after distribution of sample data. Procedia Comput Sci 125, 662–668
Gupta I, Singh AK (2017) A probability based model for data leakage detection using bigraph. In: 7th ınternational conference on communication and network security (ICCNS). Tokyo, Japan. ACM, pp 1–5
Xu B, Li Y, Yu X (2020) Malware detection based on static and dynamic features analysis. In: Machine learning for cyber security ML4CS 2020. Lecture notes in computer science, vol 12486. Springer, Cham
Zhao Y, Bo B, Feng Y, Xu C, Yu B (2019) A feature extraction method of hybrid gram for malicious behavior based on machine learning. Secur Commun Netw, Hindawi, 2674684:1–2674684:8
Gupta S, Sharma H, Kaur S (2016) Malware characterization using windows API call sequences. In: Carlet C, Hasan M, Saraswat V (eds) Security, privacy, and applied cryptography engineering SPACE 2016. Lecture notes in computer science, vol 10076. Springer, Cham, pp 271–280
Bagga P, Hans R, Sharma V (2017) N-grams based supervised machine learning model for mobile agent platform protection against unknown malicious mobile agents. Int J Interact Multimed Artif Intell 4(6):33–39
Yuxin D, Siyi Z (2019) Malware detection based on deep learning algorithms. Neural Comput Appl 31(2):461–472
Sun Z, Rao Z, Chen J, Xu R, He D, Yang H, Liu J (2019) An opcode sequences analysis method for unknown malware detection. In: ICGDA 2019, Proceedings of the 2nd international conference on geoinformatics and data analysis. ACM, New York, pp 15–19
Huang Y, Ting-Yi C, Sun YS, Chen YC (2019) Learning malware representation based on execution sequences. arXiv:abs/1912.07250
Hu Y, Ali A, Hsieh C, Williams A (2019) Machine learning techniques for classifying malicious API calls and N-Grams in kaggle data-set. SoutheastCon, 1–8
Ahmed H, Traore I, Saad S (2018) Detecting opinion spams and fake news using text classification. Secur Priv 1(1). Wiley
CSDMC2010 Dataset: https://www.azsecure-data.org/other-data.html
APIMDS Dataset: http://ocslab.hksecurity.net/apimds-dataset
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Acharya, A., Prasad, H., Kumar, V., Gupta, I., Singh, A.K. (2022). MACI: Malicious API Call Identifier Model to Secure the Host Platform. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_23
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DOI: https://doi.org/10.1007/978-981-16-6890-6_23
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