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Virtualization is the foundation of cloud computing process and allows more effective utilization of physical computer hardware. Virtualization technologies are used in various tasks for the improvement of operating systems. Virtualization is meant for enabling the entire representation of cloud computing systems on normal speed. Still, the scale of virtualized environments and its complexity is found to be difficult in this process. This paper introduces a security analytics method in virtualized infrastructure for detecting the attacks of cloud computing. As the work relies on big data issues based on the features of network behavior, the detection phase is processed under two major phases: (1) feature extraction and (2) classification. In the feature extraction phase, proposed holoentropy features are extracted along with exponential moving average features. These extracted features are then subjected to the classification process, where the optimized DBN is used to detect the presence of an attack in network. To make the detection more accurate, the weights are optimally tuned by a new hybrid elephant monarch algorithm that helps in DBN learning. At last, the performance of the proposed work is computed over other traditional models in terms of certain measures.

Security is a vital requirement in mobile ad hoc networks to provide secured communication among mobile nodes. Due to different characteristics of MANETS, it creates a number of consequential challenges to its security design. To overcome the challenges, there is a need to build a powerful security solution that achieves both broad protection and desirable network performance. Mobile Ad hoc Network (MANET) has emerged as a new leading edge of technology to provide communication wherever and whenever required. As the wired network needs established infrastructure for communication but the mobile ad hoc network does not need any infrastructure, centralized management and control. Due to movable nature of nodes in Mobilead hoc network difficult routing between nodes are not very easy task. For this purpose many reactive routing protocols have been implemented like AODV, DSR, and DSDV. In the first part of this work, we propose a new algorithm AODV-BTR to improve existing on demand routing protocol and an attempt has been made to compare the performance of proposed algorithm (AODV-BTR) with existing algorithm AODV. Ad hoc networks are susceptible to many types of attacks; due tomovable nature of nodes it is very difficult to provide security at each node. This paper introduces the black hole attack; in this type of attack mischievous node announce that he is having the shortest path to all nodes in the environment by sending fake route reply message. This paper proposes an easiest way to detect Black hole attacks using DLM technique. DML method presents the solution to detect & remove blackhole attack in reactive protocol called AODV-BTR.

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