Detection of Malware Trojans in Software using Machine Learning

Abstract

As the prevalence of malicious software, particularly trojans, continues to pose a significant threat to the integrity and security of computer systems, the need for effective detection mechanisms becomes paramount. This research presents a comprehensive approach to the detection of malware trojans in software, leveraging advanced techniques from machine learning, behavior analysis, and signature-based methods. The proposed system employs a hybrid model that combines the strengths of static and dynamic analysis to enhance detection accuracy. Static analysis focuses on examining code structures and identifying potential indicators of trojans presence, while dynamic analysis observes the software's behavior during execution to uncover malicious activities that may evade static analysis. Machine learning algorithms play a crucial role in training the detection system to recognize patterns indicative of trojans behavior. The model is trained on a diverse dataset of both benign and malicious software samples, enabling it to adapt and evolve to emerging threats. Feature extraction techniques are applied to capture the essential characteristics of trojans, contributing to the model's ability to generalize effectively. Furthermore, the system incorporates a signature-based approach, utilizing known patterns and signatures of trojans to quickly identify and mitigate known threats. Regular updates of signature databases ensure the system remains current and capable of detecting the latest trojan variants. To evaluate the effectiveness of the proposed approach, extensive testing is conducted on a variety of software samples, including both well-established trojans and newly emerging threats. The results demonstrate the system's robustness and efficiency in detecting trojan activity, with a low false positive rate. In conclusion, the presented research provides a holistic and adaptive solution for the detection of malware trojans in software. By combining static and dynamic analysis with machine learning and signature-based methods, the proposed system offers a versatile defense against the evolving landscape of trojan threats, contributing to the overall cybersecurity resilience of computer systems