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Abstract: Malware continues to be a serious threat starting from home users to large enterprises. This makes it a hot research topic. Detection of malware is done using static and dynamic analysis of malware signatures and behaviour patterns. These are proven to be ineffective and time consuming while detecting unknown malware. In order to identify the new malware many machine learning algorithms are created. Feature engineering is a key step for building these algorithms. This takes too much time. By using deep learning techniques this step can be completely avoided. Recent research reported that many of them used biased dataset, which is completely ineffective in real-time situations. Hence this drives to create a new algorithm/architecture to detect malware using deep learning.

Programming : Python

1. PPT
2. Code
3. Report
4. Documents
5. Explanation Video
6. Base Research Papers

1.https://www.ijstr.org/final-print/jan2020/Detection-Of-Malware-Using-Deep-Learning-Techniques.pdf

2.https://www.ijstr.org/final-print/jan2020/Detection-Of-Malware-Using-Deep-Learning-Techniques.pdf

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