VANETs enable wireless communication among vehicles and vehicle to infrastructure. Its main objective is to render safety, comfort and convenience on the road. VANET is different from ad-hoc networks due to its unique characteristics. However, because of lack of infrastructure and centralized administration, it becomes vulnerable to misbehaviors. This greatly threatens ing such a useful network must provide adequate security measures for secure communication. The proposed algorithm DMN-Detection of Malicious Nodes in VANETs improves DMV Algorithm in terms of effective selection of verifiers for detection of malicious nodes and hence improves the network performance.

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Image encryption using chaotic maps has been established a great way. The study shows that a number of functional architecture has already been proposed that utilize the process of diffusion and confusion. However, permutation and diffusion are considered as two separate stages, both requiring image-scanning to obtain pixel values. If these two stages are mutual, the duplicate scanning effort can be minimized and the encryption can be accelerated. This paper presents a technique which replaces the traditional preprocessing complex system and utilizes the basic operations like confusion, diffusion which provide same or better encryption using cascading of 3D standard and 3D cat map. We generate diffusion template using 3D standard map and rotate image by using vertically and horizontally red and green plane of the input image. We then shuffle the red, green, and blue plane by using 3D Cat map and standard map. Finally the Image is encrypted by performing XOR operation on the shuffled image and diffusion template. Theoretical analyses and computer simulations on the basis of Key space Analysis, statistical analysis, histogram analysis, Information entropy analysis, Correlation Analysis and Differential Analysis confirm that the new algorithm that minimizes the possibility of brute force attack for decryption and very fast for practical image encryptio

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This paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinatorial optimization problems. Over the years, combinatorial optimization problems are gaining awareness of the researchers both in scientific as well as industrial world. This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems. Basically we have divided the metaheuristic into three broad categories namely trajectory methods, population based methods and hybrid methods. Trajectory methods are those that deal with a single solution. These include simulated annealing, tabu search, variable neighborhood search and greedy randomized adaptive search procedure. Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization. Hybrid methods deal with the hybridization of single point search methods and population based methods. These are further categorized into five different types. Finally we conclude the paper by giving some issues which are needed to develop a well performed metaheuristic algorithm.