Analysis of Different Machine Learning Approaches for Malware Detection

The primary objective of this research is to enhance current malware detection methodologies by creating a scalable and reliable version that automatically detects malware by analyzing complex signature-based methods found in both code and records. This method will combine both supervised and unsupervised learning algorithms, building on earlier research that successfully applied multiple device learning techniques. Specifically, category strategies like choice bushes, random forests, and help vector machines, which have been shown to have accuracies ranging from ۸۵% to ۹۵%, could be used in conjunction with more advanced deep learning frameworks like neural networks, which have been shown to have accuracies of over ۹۶% in favorable conditions. Through training these patterns on a comprehensive and diverse dataset of both malicious and benign files, this study seeks to enhance the version's generalization capabilities, thereby enabling it to effectively detect novel, unidentified malware variants. The effectiveness of the suggested model in real-time malware detection scenarios can be ensured by thoroughly assessing its overall performance against installed benchmarks and metrics, which include accuracy, precision, bear in mind, and false tremendous fee. This comprehensive method not only aims to advance the field of cybersecurity but also expands upon the earlier works of others, providing a more proactive and flexible approach to malware detection that is in line with current advancements in cybersecurity and device research.

کلیدواژه ها:

Machine Learning ، Malwares Analysis ، Framework ، Malware Variants ، Malware Classification