Acta Scientific Computer Sciences

Research Article Volume 4 Issue 12

Research Methodology on A Machine Learning Framework and Algorithms for Automatic Detection of Malware

M Atheequllah Khan1,Imtiyaz Khan2, Pankaj Kawadkar3, M Upendra Kumar4* and D Shravani5

1,2Assistant Professor, CS and AI Dept, MJCET, OU, India
3Associate professor and HOD (CSE/IT/MCA),SSSUTMS Sehore, Madhya Pradesh, India
4Professor and Associate, Head CS and AI Dept, MJCET, OU, India
5Associate Professor, ADCE Stanley College of Engineering and Technology for Women, OU, India

*Corresponding Author: M Upendra Kumar, Professor and Associate, Head CS and AI Dept, MJCET, OU, India.

Received: November 20, 2022; Published: November 29, 2022


Cyberspace is ever expanding with inclusion of diversified networks and systems. With the emerging technologies such as Internet of Things (IoT) and distributed computing, there is seamless integration of heterogeneous applications with interoperability. This has brought unprecedented use cases and applications in various domains. Unfortunately, there is every growing threat to cyberspace due to different kinds of malicious programs termed as malware. Since adversaries are developing various kinds of malware, its detection has become a challenging task. Of late, machine learning (ML) techniques are widely used to solve problems in real world applications. Plenty of supervised learning methods came into existence. The objective of this paper is to explore and evaluate different ML models with empirical study. In this paper, we proposed a ML framework for analysing performance of different prediction models. An algorithm known as Machine Learning based Automatic Malware Detection (ML-AMD) is proposed. This algorithm is used to realize the framework with supervised learning. This empirical study has resulted in knowledge about ML models such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Multilayer Perceptron (MLP) and Gradient Boosting (GB). Random Forest model has exhibited highest accuracy with 97.96%. The research outcomes in this paper help in triggering further investigations towards automatic detection of malware.

Keywords: Malware Detection; Machine Learning; Decision Tree; Logistic Regression; Random Forest; Multilayer Perceptron and Gradient Boosting


  1. Gibert Daniel., et al. “HYDRA: A multimodal deep learning framework for malware classification”. Computers and Security 95 (2020): 1-47.
  2. Li Yi., et al. “A Machine Learning Framework for Domain Generation Algorithm (DGA)-Based Malware Detection”. IEEE Access (2019): 1-18.
  3. Pei Xinjun., et al. “AMalNet: A deep learning framework based on graph convolutional networks for malware detection”. Computers and Security 93 (2020): 1-21.
  4. Karbab ElMouatez Billah., et al. “MalDozer: Automatic framework for android malware detection using deep learning”. Digital Investigation 24 (2018): S48-S59.
  5. Karbab ElMouatez Billah and Debbabi Mourad. “MalDy: Portable, data-driven malware detection using natural language processing and machine learning techniques on behavioral analysis reports”. Digital Investigation 28 (2019): S77-S87.
  6. Huanyu Wu. “A Systematical Study for Deep Learning Based Android Malware Detection”. Proceedings of the 2020 9th International Conference on Software and Computer Applications (2020): 1-6.
  7. Ebenezer Jangam., et al. “Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking”. Applied Intelligence (2021): 1-17.
  8. Mahindru Arvind and Sangal AL. “MLDroid—framework for Android malware detection using machine learning techniques”. Neural Computing and Applications (2020): 1-58.
  9. Sara Hosseinzadeh Kassania., et al. “Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach”. Biocybernetics and Biomedical Engineering (2021): 1-13.
  10. Chin T., et al. “A Machine Learning Framework for Studying Domain Generation Algorithm (DGA)-Based Malware”. Security and Privacy in Communication Networks (2018): 433-448.
  11. Chen Xiao., et al. “Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection”. IEEE Transactions on Information Forensics and Security (2019): 1-15.
  12. Masum Mohammad and Shahriar Hossain. “IEEE 2019 IEEE International Conference on Big Data (Big Data) - Los Angeles, CA, USA (2019.12.9-2019.12.12)”. 2019 IEEE International Conference on Big Data (Big Data) - Droid-NNet: Deep Learning Neural Network for Android Malware Detection (2019): 5789-5793.
  13. Xiao Fei., et al. “Malware Detection Based on Deep Learning of Behavior Graphs”. Mathematical Problems in Engineering (2019): 1-10.
  14. Nighat Usman., et al. “Intelligent Dynamic Malware Detection using Machine Learning in IP Reputation for Forensics Data Analytics”. Future Generation Computer Systems (2021): 1-18.
  15. Singh Jagsir., et al. “A survey on machine learning-based malware detection in executable files”. Journal of Systems Architecture (2020): 1-24.
  16. Nan Zhang., et al. “Deep learning feature exploration for Android malware detection”. Applied Soft Computing (2021): 1-7.
  17. Alzaylaee Mohammed K., et al. “DL-Droid: Deep Learning Based Android Malware Detection Using Real Devices”. Computers and Security (2019): 1-28.
  18. S Akarsh., et al. “IEEE 2019 5th International Conference on Advanced Computing and Communication Systems (ICACCS) - Coimbatore, India (2019.3.15-2019.3.16)”. 2019 5th International Conference on Advanced Computing and Communication Systems (ICACCS) - Deep Learning Framework and Visualization for Malware Classification (2019): 1059-1063.
  19. Mirabelle Dib., et al. “A Multi-Dimensional Deep Learning Framework for IoT Malware Classification and Family Attribution”. IEEE Transactions on Network and Service Management (2021): 1-12.
  20. Kim Tae Guen., et al. “A Multimodal Deep Learning Method for Android Malware Detection using Various Features”. IEEE Transactions on Information Forensics and Security (2018): 1-16.
  21. Pektaş Abdurrahman and Acarman Tankut. “Deep Learning To Detect Android Malware via Opcode Sequences”. Neurocomputing (2019): 1-21.
  22. Mahshid Gohari., et al. “Android Malware Detection and Classification Based on Network Traffic Using Deep Learning”. 2021 7th International Conference on Web Research (ICWR) (2021): 1-7.
  23. Chandrashekar G and Sahin F. “A survey on feature selection methods”. Computers & Electrical Engineering1 (2014): 16-28.
  24. Velswamy Karunakaran., et al. “Exploring a Filter and Wrapper Feature Selection Techniques in Machine Learning” (2021).


Citation: M Upendra Kumar., et al. “Research Methodology on A Machine Learning Framework and Algorithms for Automatic Detection of Malware". Acta Scientific Computer Sciences 4.12 (2022): 62-66.


Copyright: © 2022 M Upendra Kumar., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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