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Malware Detection and Pattern Recognition using Deep Learning
Submitted by Vidyasagar Nallapati (@dumbyoda) on Thursday, 27 April 2017
Section: Crisp talk Technical level: Intermediate
Malware is a serious and evolving threat to security across corporates and governments, the research on malware detection using data mining, pattern recognition and machine learning methods had been there for a long time. Signature-based solutions, Heuristic techniques, Sandbox solutions have been in use and several frameworks are built around them however they are built on shallow learning architectures and which are somewhat unsatisfying for malware detection problems. In this presentation and paper, I would be discussing on how Deep learning algorithms can be applied for pattern recognition and prediction of malware. The models are greedy layerwise training operation for unsupervised feature learning, followed by supervised parameter fine-tuning and optimising for best accuracy.
- Malware Detection Analytics
- Various Machine Learning Algorithms that are applied in Malware detection
- Architecture of how Deep Learning can be applied to Malware detection
- Practical implementations of few actual scenarios
- Future and References
Vidyasagar Nallapati is a software architect from Bangalore. He loves all the things mathematics, statistics, machine learning, developer tools, automation and anything that will make systems better and easier, so he has found that in programming, architecting, building and running large scale distributed services. Presently he works at Dell EMC Data Engineering and Data Sciences team as an architect. Prior to this has am extensive practical experience in building rock-solid data pipelines in several startups, he has done his B.Tech from IIT BHU and has studied analytics at IIM Bangalore.
When Vidyasagar is not writing code, he can be found traveling, playing music and indulging in coffee.