Cloud Based Malware Detection Technique

Sagar Shaw, Manish Kumar Gupta, Sanjay Chakraborty

Abstract: Security is one of the major concerns in cloud computing now-a-days. Malicious code deployment is the main cause of threat in today’s cloud paradigm. Antivirus software unable to detect many modern malware threats which causes serious impacts in basic cloud operations. This paper counsels a new model for malware detection on cloud architecture. This model enables identification of malicious and unwanted software by amalgamation of multiple detection engines. This paper follows DNA sequence detection process, symbolic detection process, and behavioural detection process to detect various threats. The proposed approach (PMDM) can be deployed on a VMM which remains fully transparent to guest VM and to cloud users. However, PMDM prevents the malicious code running in one VM (infected VM) to spread into another noninfected VM with help of hosted VMM. After detecting malicious code by PMDM technique, it warns the other guest VMs about it. In this paper, a prototype of PMDM is partially implemented on one popular open-source cloud architecture—Eucalyptus.