Agent based intelligent approach for the malware detection for infected cloud data storage files

The threats on files stored in cloud by malware are increasing in the recent years. Leading to increase in cost in business through many access control policies are provided to protect the data stored in cloud, the malicious users attack the data using malwares. In such a scenario, it is necessary to protect the cloud data using effective methods. Hence, a new intelligent agent to malware detection and prevention model is proposed in this paper to enhance the security of cloud data storage. The main aim of in this work is to detect malware infected files while sending it from server to client and to provide a means or way to transfer the file securely. This work also focuses on improving the energy efficiency when compared with other existing system. By classifying the malwares based on their families, it is easy to identify them as each malware has a signature for each. This will help in finding the malware infected file during transmission across systems and will be highly efficient when compared with the existing systems. The main objective of the work is to detect malware infected files while transmitting the files from server to client and to provide a secure way to transfer files among users. In order to achieve this, the malwares are first classified based on their families and then they are compared with exact matching algorithm and maximum matching algorithm. By using this, in this work the presence of malwares are detected.