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.

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Vehicular Ad-hoc networks (VANETs) are a subset of Mobile Ad-hoc Networks made by vehicles communicating among themselves on roadways. The Routing protocols implemented for MANETs such as Ad-hoc on Demand Distance Vector Routing Protocol (AODV), Dynamic Source Routing (DSR), and Destination Sequence Distance Vector Routing Protocol (DSDV) are not suitable for VANET due to high Mobility. Trusted routing in VANET is a challenging task due to highly dynamic network topology and openness of wireless architecture. To avoid a frequent communication link failure, to reduce the communication overhead and to provide a trusted routing among the vehicular nodes for achieving high packet transmission, we implemented an Optimized Node Selection Routing protocol (ONSRP) of VANET based on Trust. In our proposed work, we implemented an enhanced routing protocol which prevents the network from communication link failure frequently. The testing results stated that the ONSRP routing have a high performance measures than the above mentioned existing routing protocols.

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