Abstract-Cloud Computing strives to be dynamic as a service oriented architecture (SoA). The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and maintain resilience in the cloud, it not only has to have the ability to identify the known threats but also to new challenges that target the infrastructure of a cloud. In this paper, we introduce and discuss a detection method of malwares from the VM memory snapshot analysis and the corresponding VM snapshots are classified into attacked and non-attacked VM snapshots. As snapshots are always taken to be a backup in the backup servers, this approach could reduce the overhead of the backup server with a self-healing capability of the VMs in the local cloud infrastructure itself without any compromised VM in the backup server. A machine learning approach is projected here to classify the attacked and non attacked snapshots. The features of the snapshots are gathered from the API calls of VM instances. Our proposed scheme has a high detection accuracy of about 93% while having the capability to classify and detect different types of malwares with respect to the VM snapshots. Finally the paper exhibits an algorithm using snapshots to detect and thus to selfheal. The self-healing approach with machine learning algorithms can determine new threats with some prior knowledge of its functionality.
Reversible data hiding is a technique which enables images to be authenticated and then restored to their original form by removing the digital watermark and replacing the image data that had been overwritten. An efficient reversible lossless data hiding algorithm can recover the original image without any distortion and its Peak Signal to Noise Ratio (PSNR) lower bound is higher than that of all existing reversible data hiding algorithms. This article proposes a dynamic method to determine the most suitable peak-valley pairs instead of greedy way to maximize the embedding capacity of the object. Thus the embedding capacity and the image quality can be improved by this new approach. The application of data hiding includes hidden communication in military applications and watermarking of copyright protection, and so forth.
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