Ashu Sharma

Combating malware is very important for digital world security. To prevent these digital systems from the advanced malware, viz. The metamorphic malware variants with the same malicious behavior (of same family), can obfuscate themselves to look different from each other. This variation in structure leads to a huge signature database for traditional signature matching techniques to detect them. In order to effective and efficient detection of malware in large amounts of data, my research goes around these advanced malware and their detection techniques.

Projects:

  • Effective detection of android malicious apps by group-wise classification
  • PeerClear: Peer-to-Peer Bot-net Detection

  • Automatic malware detection using memory forensics
  • Cloud Based IoT Honeypot for MQTT Protocol

  • A Hybrid Approach to Detect Linux Malware

  • An Approach to Classify Malware into families

  • Validation of Attack Paths Model

Publications:

  • 2018, Cloud Based IoT Honeypot for MQTT Protocol, 14th International Conference on Information Systems Security (ICISS 18) (in communication)
  • 2018, A Hybrid Approach to Detect Linux Malware, 14th International Conference on Information Systems Security (ICISS 18) (in communication)
  • 2018, PeerClear: Peer-to-Peer Bot-net Detection, Eighth International Conference on Security, Privacy, and Applied Cryptography Engineering (SPACE 2018) (in communication)
  • 2018, Machine Learning in Cyber Security, Wiley Interdisciplinary Reviews (WIREs), WILEY (in communication)
  • 2018, A Survey on the Detection of Android Malicious Apps, International Conference on Computer, Communication and Computational Sciences (IC4S- 2018) (in communication)
  • 2018, A Survey on the Detection of Windows Desktops Malware, Proceeding in Springer, Advances in Intelligent Systems and Computing (in press)
  • 2018, Evolution of Malware and its Detection Techniques, Proceeding in Springer, Lecture Notes in Networks and Systems (in press)
  • 2018, Group-wise Classification to Improve the Detection Accuracy of Android Malicious Apps, International Journal of Network Security (in press)
  • 2017, An investigation of the classifiers to detect android malicious apps, International Congress on Information and Communication Technology, Springer science
  • 2016, An effective approach for classification of advanced malwares with high accuracy, International Journal of Security and Its Applications
  • 2016, Grouping the executables to detect malwares with high accuracy, International Conference on Information Security and Privacy (ICISP), Procedia Computer Science
  • 2016, Improving the detection accuracy of unknown malware by partitioning the executables in groups, 9th ICACCT, Springer science
  • 2014, Evolution and detection of polymorphic and metamorphic malwares: A survey, IJCA 2012 Artificial Hygiene for Computer Systems, IJCA

 

Year of Joining IIT
2018
Pass out University & Year
Ph.D. (Malware Analysis), BITS PILANI, 2018.
M.Tech (Information Security), ABV-IIITM, Gwalior, 2011.
B.Tech (CSE), Uttar Pradesh Technical University (UPTU), 2009.
ashu.abviiitm@gmail.com