• Dr.Deena Babu Mandru
• S.Rahul
• T.Thakshith Kumar
• S.Varun Kuar
• V.G.Suraj Kumar Yadav[

The evolving landscape of computer technology has led to a significant shift towards virtual environments, accompanied by a rise in the threat posed by malware. Traditional methods of malware detection are struggling to keep pace with the increasing sophistication of malware variants, which often employ advanced packing and obfuscation techniques to evade detection. In response to this challenge, there is a growing interest in exploring new techniques for effectively combating malware. One promising approach is the utilization of deep learning methods, which offer the potential to detect complex and new malware variants more effectively than traditional machine learning methods. This abstract presents an overview of the current challenges in malware detection and highlights the potential of deep learning methods, particularly ensemble learning approaches, for enhancing cybersecurity measures.

Malware, Cyber Security, Ensemble learning

"Automated Malware Detection using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f77-f86, May-2024, Available :http://www.jetir.org/papers/JETIR2405509.pdf

"Automated Malware Detection using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf77-f86, May-2024, Available at : http://www.jetir.org/papers/JETIR2405509.pdf