IJBTT

Elephant Herd Optimization with Weighted Extreme Learning Machine based PDF Malware Detection and Classification Model

Elephant Herd Optimization with Weighted Extreme Learning Machine based PDF Malware Detection and Classification Model
	© 2022 by IJETT Journal
	Volume-70 Issue-8
	Year of Publication : 2022
	Authors : P. Pandi Chandran, Hema Rajini. N, M. Jeyakarthic
	DOI : 10.14445/22315381/IJETT-V70I8P222

How to Cite?

P. Pandi Chandran, Hema Rajini. N, M. Jeyakarthic, "Elephant Herd Optimization with Weighted Extreme Learning Machine based PDF Malware Detection and Classification Model," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 216-223, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P222

Abstract
Portable Document Format (PDF) is widely utilized for document exchange and distribution because of its high portability and universal usage. Benign users and attackers leverage the format's adaptable and flexible nature to utilize different vulnerabilities, overcome security limitations, and then convert the PDF format into one of the foremost malicious code spread vectors. Investigation of the content in the malicious PDF for extracting major features plays a vital role in the automated identification of new attacks. This study develops an Elephant Herd Optimization with Weighted Extreme Learning Machine (EHO-WELM) based PDF malware detection and classification model. The presented EHO-WELM model mainly aims to determine the existence of PDF malware. For attaining this, the EHO-WELM model pre-processes in two ways: categorical encoding and null value removal. In addition, the pre-processed data are passed into the WELM model to identify and classify PDF Malware. For determining the weight values of the WELM model, the EHO algorithm is applied to improve the classifier efficacy, showing the work's novelty. The simulation analysis of the EHO-WELM model on the benchmark dataset implied superior outcomes over the existing approaches.

Keywords
Malware detection, Weighted extreme learning machine, PDF Malware, Elephant herd optimization, Parameter tuning.

Reference
[1] Gibert, D., Mateu, C. and Planes, J, “ the Rise of Machine Learning for Detection and Classification of Malware: Research Developments, Trends and Challenges,” Journal of Network and Computer Applications, vol.153, P.102526, 2020.
[2] Komatwar, R. and Kokare, M, “ A Survey on Malware Detection and Classification,” Journal of Applied Security Research, vol.16, no.3, pp.390-420, 2021.
[3] Wang, J., Xue, Y., Liu, Y. and Tan, T.H, “ Jsdc: A Hybrid Approach for Javascript Malware Detection and Classification,” in Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, pp.109-120, 2015.
[4] Vinayakumar, R., Soman, K.P. and Poornachandran, P, “ Deep Android Malware Detection and Classification,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.1677-1683, 2017.
[5] Singh, J. and Singh, J, “ A Survey on Machine Learning-Based Malware Detection in Executable Files,” Journal of Systems Architecture, vol.112, pp.101861, 2021.
[6] Nishant Jakhar, Rainu Nandal, Kamaldeep, "Design of A Rule-Based Decisive Model for Optimizing the Load Balancing in A Smart Grid Environment," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 97-103, 2022.
[7] Miguel Fernández, Avid Roman-Gonzalez, ”A Multi-Objective Approach to Modelling the Integrated Resource Selection and Operation Sequences Problem in Production System,” International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 51-56, 2022.
[8] T V Divya, Barnali Gupta Banik, “An Integrated Cycle GAN and PEGASUS to Generate Synthetic Data for Detection of Fake News,” International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 57-70, 2022.
[9] Wang, C., Zhang, L., Zhao, K., Ding, X. and Wang, X, “ Advandmal: Adversarial Training for Android Malware Detection and Family Classification,” Symmetry, vol.13, no.6, pp.1081, 2021.
[10] Sethi, K., Chaudhary, S.K., Tripathy, B.K. and Bera, P, “ A Novel Malware Analysis Framework for Malware Detection and Classification Using Machine Learning Approach,” in Proceedings of the 19th International Conference on Distributed Computing and Networking, pp. 1-4, 2018.
[11] Gao, H., Cheng, S. and Zhang, W, “ Gdroid: Android Malware Detection and Classification with Graph Convolutional Network,” Computers & Security, vol.106, P.102264, 2021.
[12] Roseline, S.A., Geetha, S., Kadry, S. and Nam, Y, “Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm,” IEEE Access, vol.8, pp.206303-206324, 2020.
[13] Reddy, V., Kolli, N. and Balakrishnan, N, “ Malware Detection and Classification Using Community Detection and Social Network Analysis,” Journal of Computer Virology and Hacking Techniques, vol.17, no.4, pp.333-346, 2021.
[14] Zong, W., Huang, G.B. and Chen, Y, “ Weighted Extreme Learning Machine for Imbalance Learning,” Neurocomputing, vol., 101, pp.229-242, 2013.
[15] Yu, H., Yang, X., Zheng, S. and Sun, C, “ Active Learning From Imbalanced Data: A Solution of Online Weighted Extreme Learning Machine,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no.4, pp.1088-1103, 2018.
[16] Wang, G.G., Deb, S. and Coelho, L.D.S, “ Elephant Herding Optimization,” in 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 1-5, 2015.
[17] Https://Www.Unb.Ca/Cic/Datasets/Pdfmal-2022.Html
[18] Https://Github.Com/Srndic/Mimicus/Tree/Master/Data
[19] Damaševiˇcius, R.; Venˇckauskas, A.; Toldinas, J.; Grigaliunas, Š, " Ensemble-Based ¯ Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection. Electronics,” vol.10, pp.485, 2021. Https:// Doi.Org/10.3390/Electronics10040485
[20] Modalavalasa Hari Krishna, Dr.Makkena Madhavi Latha "Intelligent Parameter Tuning Using Segmented Recursive Reinforcement Learning Algorithm" International Journal of Engineering Trends and Technology 68.6(2020):1-8.