Malware Detection Using Machine Learning Techniques

Abstract

Due to the increase in cyber-attacks and the dynamic nature of technology and malware, there is a need to develop a working model capable of detecting malicious files based on certain features. The project used the drebin-215-dataset-5560malware-9476-benign.csv dataset, it is the collection of a diverse dataset of both malware and benign samples that include different types of malware. Feature extraction techniques are used to capture relevant attributes from samples, including file system activities, network traffic, and more. Subsequently, a number of machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression and Convolutional Neural Networks, they are trained and evaluated on the extracted features to classify the samples as malicious or benign. The evaluation process involves assessing the performance of each algorithm in terms of accuracy, precision, recall and F1 score. In addition, the models are tested for their ability to generalize to unseen data and resist overfitting. A comparative analysis is performed to identify the most effective malware detection algorithm based on the characteristics of the dataset. The results of this project provide insight into the effectiveness of various machine learning techniques for malware detection and contribute to the development of more robust and proactive cyber security solutions. By leveraging machine learning, organizations can improve their ability to detect and mitigate malware threats in real-time, thereby strengthening the overall security posture of their systems and networks

Introduction

The Internet and technology have become deeply integrated into daily life, but with this advancement comes increased cybersecurity risks such as malware attacks. Malware includes viruses, worms, ransomware, spyware, trojans, and more, each with distinct behaviors and threats to systems. Traditional malware detection methods, including static and dynamic analyses, face challenges against evolving threats like zero-day and polymorphic malware. Therefore, machine learning techniques are being explored to improve malware detection, especially for Windows executable files, which dominate the global operating system market.

Malware types range from viruses that require user interaction to spread, to stealthy rootkits and ransomware that encrypt data for ransom. Malware analysis can be static (code inspection), dynamic (behavioral analysis in a virtual environment), or hybrid (combining both).

Detection methods include signature-based detection, behavior detection (heuristics), feature detection, blocklisting, allowlisting, and honeypots (decoy systems to trap malware). Various tools like Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), sandboxing, and cloud-based solutions enhance malware detection.

Machine learning algorithms such as Decision Trees, Support Vector Machines (SVM with linear, polynomial, and RBF kernels), K-Nearest Neighbors (K-NN), and Convolutional Neural Networks (CNN) are applied to analyze malware more effectively by identifying patterns and behaviors beyond traditional methods.

Conclusion

This research shows that for malware detection, machine learning techniques plays an important rolebehavioural and structural attributes of malicious software, the study demonstrates that by using machine learning algorithms we can achieve high accuracy and reliability. The Random Forest model achieved the highest accuracy (99%) which clearly indicates the suitability for malware detection tasks.Also, the project highlightsthe importance of feature extraction and data pre-processing for model training and evaluation. This project encourages the need for adaptive and scalable detection mechanisms to combat the evolving malware threats. The insights gained from this study contribute to the development of more robust cybersecurity frameworks capable of real-time threat detection and mitigation.

References

[1] https://www.ijert.org/research/an-emerging-malware-analysis-techniques-and-tools-a-comparative-analysis-IJERTV10IS040071.pdf [2] https://www.researchgate.net/publication/224089748_Malware_detection_using_machine_learning [3] https://www.mdpi.com/1099-4300/23/8/1009#:~:text=Dynamic%20analysis%20technology%20generally%20analyzes%20the%20characteristics,characteristics%20of%20application%20software%20by%20executing%20programs [4] https://ijaseit.insightsociety.org/index.php/ijaseit/article/view/6827/pdf_846 [5] https://www.mdpi.com/2073-8994/14/11/2304 [6] https://doi.org/10.1002/cpe.5422 [7] https://arxiv.org/pdf/1606.06897 [8] https://www.ijraset.com/research-paper/malware-detection-using-ml [9] https://www.ijraset.com/best-journal/malware-detection-using-machine-learning [10] https://www.ijraset.com/research-paper/a-static-approach-for-malware-analysis-a-guide-to-analysis-tools-and-techniques [11] https://www.mdpi.com/2073-8994/14/11/2304#B1-symmetry-14-02304 [12] https://www.tutorialspoint.com/machine_learning/machine_learning_performance_metrics.htm [13] https://spotintelligence.com/2023/03/01/adam-optimizer [14] https://www.geeksforgeeks.org/ [15] Anderson, H. S., Filar, B., & Kharkar, A. (2018). Evading Machine Learning Malware Detection. Black Hat USA. [16] Kolter, J. Z., & Maloof, M. A. (2006). Learning to detect malicious executables in the wild. Journal of Machine Learning Research, 7, 2721–2744. [17] Shafiq, M. Z., Tabish, S. M., Farooq, M., & Mirza, H. (2009). PE-Miner: Mining structural information to detect malicious executables in real time. RAID. [18] Eskandari, M., Hashemi, S., & Leckie, C. (2020). A fast KNN-based approach for malware detection. Computers & Security, 94, 101877. [19] Rieck, K., Trinius, P., Willems, C., & Holz, T. (2008). Automatic analysis of malware behavior using machine learning. Journal in Computer Virology, 7(4), 1-15. [20] Saxe, J., & Berlin, K. (2015). Deep neural network-based malware detection using two-dimensional binary program features. 10th International Conference on Malicious and Unwanted Software.

Copyright

Copyright © 2025 Shahnawaz Abedin, Syed Ali Mehdi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.