• PARSHVA CHETAN DOSHI
• DARSH BHAVESH PATEL
• SOLANKI OMKAR PRAVIN
• VISHAL PARAG PADIA

In recent years, malware has grown to be one of the biggest risks to computer security. Using signature-based techniques, which are useless against fresh and previously undiscovered infection, is the conventional method of identifying malware. Techniques for machine learning (ML) have become a promising replacement for conventional approaches. The capacity of machine learning algorithms to detect previously undiscovered malware, even when it has not yet been recognised by signature-based techniques, has attracted considerable attention. The calibre of the characteristics that are collected from the malware samples determines how well ML-based malware detection systems perform. A crucial stage in the creation of ML-based malware detection systems is feature engineering. The model should be trained using informative, discriminative characteristics Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for example, are deep learning-based techniques that have been used to detect malware recently. These methods have demonstrated encouraging outcomes in identifying malware with high accuracy and low false positive rates. The application of machine learning techniques for malware detection is, all things considered, a promising strategy that has the potential to dramatically increase the efficiency of malware detection systems. Yet, the creation of efficient ML-based malware detection systems necessitates careful feature engineering, the application of suitable machine learning techniques, and the availability of substantial, high-quality dataset

Machine Learning, Malware Detection, Cyber Security, Computer Safety, CNNs, RNNs.

"Malware Analysis & Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.c110-c118, June-2023, Available :http://www.jetir.org/papers/JETIR2306213.pdf

"Malware Analysis & Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppc110-c118, June-2023, Available at : http://www.jetir.org/papers/JETIR2306213.pdf