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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A Mobile Ad-hoc Network (MANET) is a constantly self-configuring, infrastructure-less network of mobile devices where each device is wireless, moves without restraint and be a router to put across traffic unassociated to its own use. Every device must be prepared to constantly sustain the information obligatory for routing the traffic. And this is the main challenge in building a MANET. Such networks may be self operating or linked to a larger internet and may have one or multiple different transceivers between nodes resulting in a highly dynamic and autonomous topology. The first focus is on MANET attacks followed by detection of the malicious node from MANET via Polynomial-Reduction Algorithm. Although scientists have assessed many algorithms for the detection and rectification of the malicious nodes in the MANETs, the problem still persists. Due to the unprecedented growth in technology, the unidentified vulnerabilities are also intensifying. Therefore, it is very crucial to come up with some ground-breaking ideas to prevent the MANET. In this paper we are used NS2 simulator to implementing malicious nodes in MANET.

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Recently, the biometric-based identifications are widely adopted for personnel identification. However, the unimodal recognition systems currently suffer from noisy data, biometric sensor data quality, spoofing attacks, unacceptable error rates and lack of distinctiveness of the biometric trait. These issues can be undertaken via multi-modal biometrics authentication system. This paper proposes a multi-modal framework to capture human skeletal features and facial features using imaging techniques. This modelling technique captures human joints with 3D depth data, to improve the system efficiency. This Biometric technique is used to recognize the known/unknown image from the trained images within short span of time. The proposed research has subdivided into three-folds. First, preprocessing the image, using the MinMax method which reduces the noise in data and enhance the image quality. Second, image features are extracted using Genetic method in combination with cuckoo search algorithm which is considered to be best optimization technique for image analysis. Third, the human recognition is attained via the Artificial Neural Network (ANN) which relies on back-propagation algorithm. The performance of the proposed method is evaluated based on the measure of different metric named as, true positive and negative rate, false positive and rejection rate, accuracy. The experimental results show that this new framework dramatically improves the system performance.