• Somnath Chouwdhury
• Yashwardhan Paniya
• Akanksha Suryavanshi
• Aditya lal Sinha
• Priya Ujawe

Modern antivirus software is effective at detecting known threats, but can be evaded by malware not present in their databases or any zero day attack.(zero-day attacks — malicious files targeting vulnerabilities that are previously undisclosed.). An exponential growth in digital content consumption, digital transactions too have grown and today India sees almost twice the number of digital payments in comparison to China. This increase in digitalization has created an opportunity for hackers to damage the data and one of the most common attacks used are malware. Malware are also included in the OWASP top 10 cyber threats. In order to create a more flexible, cost effective and reliable antivirus product, we need to develop alternatives that complement traditional signature-based detection. An obvious solution to this can be classifier algorithms in machine learning. We are proposing a antivirus based on supervised machine learning classifier algorithms.

Antivirus, Machine Learning, Artificial Intelligence, Cyber Security, Malware

"Malware Detection and Elimination using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.b698-b701, December-2021, Available :http://www.jetir.org/papers/JETIR2112182.pdf

"Malware Detection and Elimination using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppb698-b701, December-2021, Available at : http://www.jetir.org/papers/JETIR2112182.pdf