15 天前 - … Machine learning models can adapt to complex malware tactics and identify new
forms of malware that may not be detected by traditional methods but the big issue most of …

18 天前 - … power of artificial intelligence (AI) and big data analytics to enhance security and
privacy in … learning-based approach for malware detection and classification. By representing …

19 天前 - … of malware detection are discussed. Overall, this research enhances our understanding
of distributed machine learning algorithms in big data … models for malware detection. …

19 天前 - … the biggest obstacles in malware detection. In this research, a detection approach to
detect malware … pre-existing malware within the dataset and newly emerging malware. The …

22 天前 - … The MobileNet fine-tuning model has proven to be highly effective in classifying
malware families on the Microsoft Big 2015 and Malimg datasets. The results show very good …

27 天前 - … Malware that is polymorphic continuously alters its signature characteristics …
malware detection techniques. We applied various machine learning algorithms to detect malware …

33 天前 - … had proven to be effective in the prediction of malware [2… learning and deep learning
in the identification of malware, One … on few-shot learning put a big question mark on the …

33 天前 - … with large data, machine learning techniques have been … Two cyber security
challenges, Microsoft malware big data … the same topics of malware detection and metalearning. …

36 天前 - … This article proposes a dynamic security architecture design based on micro services
and deep learning. Through the method proposed in this article, 100% of known malware …

38 天前 - … malware bytes successfully. Our approach combines the strengths of deep learning
and traditional machine learning … malware classes and Microsoft Big 2015 dataset with nine …