• Naresh jain
• Nipun kadam
• Sahil Rane
• Adinath paikrao
• Priti Mishra

The project "Malware Analysis and Detection Using Machine Learning Algorithm" aims to enhance cyber-security measures by accurately identifying malicious software through advanced machine learning techniques. Developed using Python, the project employs the Flask web framework for backend operations and utilizes HTML, CSS, and JavaScript for a responsive and interactive frontend interface. Two machine learning models are central to this project: the Extra Tree Classifier and Logistic Regression. The Extra Tree Classifier model demonstrates superior performance, achieving a training accuracy of 97.42% and a testing accuracy of 97.23%. In comparison, the Logistic Regression model achieves a training accuracy of 94.84% and a testing accuracy of 93.67%. Both models are trained and validated using the TUNADROMD dataset, which comprises 4465 instances and 242 attributes, with the target classification attribute distinguishing between malware and good ware. For the analysis, a subset of 23 attributes was selected based on their relevance and impact on the classification task. This strategic selection aims to optimize model performance while reducing computational complexity. The project's results indicate that the Extra Tree Classifier is highly effective in distinguishing between malicious and benign software, offering a reliable tool for malware detection in real- world applications. Overall, this project demonstrates the efficacy of machine learning algorithms in cybersecurity, providing a robust solution for malware detection that can be integrated into various digital security infrastructures.

Malware analysis, Malware detection, Logistic Regression, TUNADROMD dataset, Extra Tree Classifier, Machine learning algorithm

"Malware Detection Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e827-e830, March-2025, Available :http://www.jetir.org/papers/JETIR2503510.pdf

"Malware Detection Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe827-e830, March-2025, Available at : http://www.jetir.org/papers/JETIR2503510.pdf