A Survey on Malware Detection with Deep Learning

Şahin M., Bahtiyar Ş.

13th International Conference on Security of Information and Networks, SIN 2020, Virtual, Online, Turkey, 4 - 06 November 2020 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1145/3433174.3433609
  • City: Virtual, Online
  • Country: Turkey
  • Keywords: Classification, Deep Learning, Detection, Malware
  • Istanbul Technical University Affiliated: Yes


© 2020 ACM.Rapid development of Internet and technology has emerged a bunch of evolving malware and attack strategies. Therefore researchers focused on machine learning and deep learning methods to detect malware (viruses, bots, ransomware, trojans). In order to protect users from this treats many companies have been developing new algorithms and products. However, malware types have been increasing dramatically. Anti-malware producers have been detecting with millions of new malware types each year. So in order to stop that increase, there is an urgent need to develop new intelligent methods on malware detection. In this work, we have overviewed current intelligent machine learning and deep learning methods to solve malware detection. In this sense, we will present malware feature extraction and classification methods. Also, we will discuss more issues and challenges on that problem. Finally, we will share our foresight on malware detection methods.