• Prachi Gadhire
• Dr. Farhat Jummani

Malware, or harmful software as it is sometimes called, is always changing to keep up with the expansion of our internet presence. Malware detection receives a lot of attention in the present cybersecurity environment since it is such a serious threat. Numerous machine learning techniques have been used in automatic malware detection during the past year. Recently, deep learning has been used with better results. Deep learning models perform noticeably better when analysing lengthy series of system calls. This work uses the shallow deep learning-based feature extraction method word2vec to represent any given malware based on its opcodes. Also chosen for the classification task is the Gradient Boosting malware classification method. K-fold cross-validation is used to validate the model performance without skipping a validation

MALWARE, DEEPLEARNING

"DEEP LEARNING INMALWARE TYPES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.c93-c97, July-2022, Available :http://www.jetir.org/papers/JETIR2207211.pdf

"DEEP LEARNING INMALWARE TYPES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppc93-c97, July-2022, Available at : http://www.jetir.org/papers/JETIR2207211.pdf