Ransomeware classification using fuzzy neural network algorithm

https://doi.org/10.53730/ijhs.v6nS2.8026

Authors

  • M. Manoj Research Scholar, Sri Ramakrishna College of Arts &Science for Women, Coimbatore, India
  • Rani V G Associate Professor, Department of Computer Science Sri Ramakrishna College of Arts & Science for Women, Coimbatore, Coimbatore India

Keywords:

malware, classification, neural network, ransomware

Abstract

Traditional malware classification relies on known malware types and significantly large datasets labelled manually which limits its ability to recognize new malware classes. For unknown malware types or new variants of existing malware containing only a few samples each class, common classification methods often fail to work well due to severe over fitting. In this paper, we propose a new neural network structure called fuzzy neural network based algorithm for ransom ware classification. Malware classification with fuzzy neural network classifier yields 98% accuracy which means greater performance than classification with neural network.

Downloads

Download data is not yet available.

References

Mitsuhashi R, Shinagawa T. High-accuracy malware classification with a malware-optimized deep learning model. arXiv preprint arXiv:2004.05258. 2020 Apr 10.

Asrafi N, Lo DC, Parizi RM, Shi Y, Chen YW. Comparing performance of malware classification on automated stacking. InProceedings of the 2020 ACM Southeast Conference 2020 Apr 2 (pp. 307-308).

Kim DW, Shin GY, Han MM. Analysis of feature importance and interpretation for malware classification. Computers, Materials & Continua. 2020 Jan 1;65(3):1891-904.

Tang Z, Wang P, Wang J. ConvProtoNet: Deep prototype induction towards better class representation for few-shot malware classification. Applied Sciences. 2020 Jan;10(8):2847.

Nisa M, Shah JH, Kanwal S, Raza M, Khan MA, Damaševičius R, Blažauskas T. Hybrid malware classification method using segmentation-based fractal texture analysis and deep convolution neural network features. Applied Sciences. 2020 Jan;10(14):4966.

Yuan B, Wang J, Liu D, Guo W, Wu P, Bao X. Byte-level malware classification based on markov images and deep learning. Computers & Security. 2020 May 1;92:101740.

Narayanan BN, Davuluru VS. Ensemble malware classification system using deep neural networks. Electronics. 2020 May;9(5):721.

Vu DL, Nguyen TK, Nguyen TV, Nguyen TN, Massacci F, Phung PH. HIT4Mal: Hybrid image transformation for malware classification. Transactions on Emerging Telecommunications Technologies. 2020 Nov;31(11):e3789.

Hosseini S, Nezhad AE, Seilani H. Android malware classification using convolutional neural network and LSTM. Journal of Computer Virology and Hacking Techniques. 2021 Apr 29:1-2.

Dahl GE, Stokes JW, Deng L, Yu D. Large-scale malware classification using random projections and neural networks. In2013 IEEE International Conference on Acoustics, Speech and Signal Processing 2013 May 26 (pp. 3422-3426). IEEE.

Ronen R, Radu M, Feuerstein C, Yom-Tov E, Ahmadi M. Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135. 2018 Feb 22.

Nari S, Ghorbani AA. Automated malware classification based on network behavior. In2013 International Conference on Computing, Networking and Communications (ICNC) 2013 Jan 28 (pp. 642-647). IEEE.

Kalash M, Rochan M, Mohammed N, Bruce ND, Wang Y, Iqbal F. Malware classification with deep convolutional neural networks. In2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) 2018 Feb 26 (pp. 1-5). IEEE.

Anderson B, Storlie C, Lane T. Improving malware classification: bridging the static/dynamic gap. InProceedings of the 5th ACM workshop on Security and artificial intelligence 2012 Oct 19 (pp. 3-14).

Milosevic N, Dehghantanha A, Choo KK. Machine learning aided Android malware classification. Computers & Electrical Engineering. 2017 Jul 1;61:266-74.

Published

28-05-2022

How to Cite

Manoj, M., & Rani, V. G. (2022). Ransomeware classification using fuzzy neural network algorithm. International Journal of Health Sciences, 6(S2), 11268–11278. https://doi.org/10.53730/ijhs.v6nS2.8026

Issue

Section

Peer Review Articles