The rapid development of the transfer of data via internet made it easier to send the correct information and quicker to the destination. Today's massive demand of web applications needs the information to be communicated in a secure manner. The transmission of information in a public communication system isn't secure because of interception and improper manipulation by an eavesdropper. So for this problem, the solution is Steganography, which means the art of hiding information in ways that avert the revealing of hiding messages. Secure video steganography is a difficult task of sending the embedded data to the receiver without being detected. This paper presents a steganographic model which is based on random pattern based sequential bit encoding. The proposed model has been designed to enhance the security level. The video embedding creates the higher level of security for the embedded data. Here the encrypted secret image is embedded into the blocks of a cover video file which is selected using autocorrelation function which measures the similarity between the two images. The embedding is done on the basis of random selection of the pixels in the blocks of cover video. The performance parameters that are used to obtain results are taken in the form of chi-square histogram difference measurements. The proposed model increases the security by using random pattern embedding.

In this present digital world, digital pictures and videos are the main sources of information. However, these carriers of information can be easily tampered by using softwares such as Adobe photoshop, GIMP etc. Thus, the issue of verification of authenticity and integrity of digital images becomes necessary. Copy Move Forgery is a popular type of forgery that is commonly used for the manipulation of digital images. In this, a region of digital image is copied and then pasted to another location with in the same image with intension to make an object disappear from an image by covering it with small block copied from another part of the same image. There are several post processing operations that are applied by manipulators to obstruct the forgery detection techniques. Thus, for aforementioned problem, we in this paper proposed a method which is a combination of SIFT and SURF algorithms. In this firstly image is split in to sub-parts by DWT method and then SIFT and SURF are applied to actual components of image one by one. After this, features extracted by both methods are matched to locate the forged part in the image. The experiment shows that the proposed method is more efficient and provides better results than applying SIFT and SURF alone.

Due to an aggressive development and covert transmission of the computer users over the web, the steganography is acquiring its vogue day by day. It is the method to encode the covert data in a transmission medium in a fashion that the actuality of data must remain hidden. To protect the integrity of the data over the internet becomes necessary for the high sensitivity communications or the data transfers. The steganography carries a number of options for the embedding of the secret data into the cover data. In this manuscript, we have proposed the hybrid representation for the embedding of the data, which utilizes the random pattern embedding along with the cryptography for the higher steganography security levels. The proposed model has been designed to enhance the security level by utilizing the sequential data encoding and decoding in the video data. The video embedding creates the higher level of security for the embedded data. The experimental results have been obtained in the form of the embedding capacity, mean squared error (MSE) and Bits per Pixel (BPP). The proposed model has been found to be efficient enough for the video steganography.

Image watermarking is the process of the hiding the one image into other image for the copyright protection. The process of watermarking must be done in this way that the pixels of the original image must remain in its original HD form. A lot of work has been done in this context in previous years but some techniques have their own applications, drawbacks as well as advantages. So, this paper will utilize three techniques i.e. Discreet Wavelet Transform (DWT), Neural Network (NN) and RSA encryption for image watermarking. In the end the performance of the proposed technique will be measured on the basis of PSNR, MSE, BCR, BER and NCC in MATLAB R2010a environment.

Vehicular Ad hoc Network involves the movement of vehicles and the communication between them for their security. VANETs have many application areas. One of main applications of VANETs is to improve the driving safety. In various safety related applications, vehicular nodes constantly communicate with roadside equipments. Road Side Units (RSUs) can sense the real time information about road conditions, animals straying and road blocks and passes all this hazardous related information to the vehicles approaching in its range. These alert messages enable the driver to take timely decisions in preventing from accidents or delays in information delivery. In this paper, Enhanced Direction based Hazard Routing Protocol and Ad Hoc On-demand Distance Vector protocols are used to avoid prevent vehicles from collision and to increase the coverage range of VANETs. These issues are resolved by bypass routing and a synchronized clock maintained with the RSUs respectively. To solve the issues and make the system more reliable we propose the roadside wireless sensor nodes along with vehicular nodes in the network. The RSUs are fixed at some distances and communicate with wireless sensors attached at vehicular nodes.

With the outgrowth of cloud computing, a large amount of private information is stored over cloud servers, which is in encrypted format. But searching over encrypted data is very difficult. Earlier search schemes were based on Boolean search through keywords. But don’t consider relevance of files. After that ranked search comes into its role, which uses searchable symmetric encryption (SSE). To achieve more practical and efficient design method was further modified to “Order preserving symmetric encryption” (OPSE), which uses primitives and indexed metadata files used in ranked SSE. In this proposed work further enhancements are done to reduce storage space for encrypted metadata using Porter Stemming method. Improvements in retrieval time are also done by using Boyer Moore’s searching algorithm.

The VANETs carry many security concerns. One of the popular and dangerous attacks can be launched in the form of Sybil or Prankster attack, where an attacker inserts a fake position within in the cluster. The inserted fake node information can be utilized by the hackers in the case of selfish driver, traffic jams, selective collisions and other similar hazardous situations. To avoid such things the VANETs must be protected against such attacks. In this paper, a novel solution has been proposed to overcome the Sybil and prankster attacks on the VANETs. The new solution is capable of detecting the fake information injections by verifying the VANET node behaviour in the cluster. The behaviour of the node includes the direction, speed, pattern, etc. In case a node is found malicious, the whole cluster is reported against that node, and node is ordered to stop by the central control system. The proposed model has been developed using the random waypoint model. The random way point model is much closer to the real time VANETs. The random waypoint model has been compared against the reference point group model. The experimental results have shown the effectiveness of the proposed model.

The Outlier detection is very active area of research in data mining where outlier is a mismatched data in dataset with respect to the other available data. In existing approaches the outlier detection done only on numeric dataset. For outlier detection if we use clustering method , then they mainly focus on those elements as outliers which are lying outside the clusters but it may possible that some of the unknown elements with any possible reasons became the part of the cluster so we have to concentrate on that also. The Proposed method uses hybrid approach to reduce the number of outliers. The number of outlier can only reduce by improving the cluster formulation method. The proposed method uses two data mining techniques for cluster formulation i.e. weighted k-means and neural network where weighted k-means is the clustering technique that can apply on text and date data set as well as numeric data set. Weighted k-means assign the weights to each element in dataset. The output of weighted k-means becomes the input for neural network where the neural network is the classification and clustering technique of data mining. Training is provided to the neural network and according to that neurons performed the testing. The neural network test the cluster formulated by weighted k-means to ensure that the clusters formulated by weighted k-means are group accordingly. There is lots of outlier detection methods present in data mining. The proposed method use Integrating Semantic Knowledge (SOF) for outlier detection. This method detects the semantic outlier where the semantic outlier is a data point that behaves differently with other data points in the same class or cluster. The main motive of this research work is to reduce the number of outliers by improving the cluster formulation methods so that outlier rate reduces and also to decrease the mean square error and improve the accuracy. The simulation result clearly shows that proposed method works pretty well as it significantly reduces the outlier.

Many organizations provide computing resources or services to the client like hardware, software, storage etc. Storage is one of important service that is popular and commonly used today and it provides storage space online to store the data. Service providers give a storage space to consumers on the basis of renting criteria with a trust of high and powerful security. Consumer and organizations uses these storages to secure own personal data. There are many reasons come across that causes to move data from one place to another. Some of them are: reconstruction of storage, backup, new technologies, security etc. So, data migration, data transfer between different cloud storages, often requires. Achieving mechanism of Data migration between clouds is very difficult because of its different architecture to store data. For example Window Azure cloud storage creates a primary key name ID automatically whenever a new table is created. Architecture of cloud storage is depending upon its implementation done by corresponding cloud vendors (like Microsoft). 
The main vision of this research work is to provide an intermediate service to move the data between both homogeneous and heterogeneous cloud storages. This intermediate service is a two way strategy that built in a SOA technology called WCF that helps the clients, organizations to move data between same and different cloud storages (Window Azure and Go Daddy cloud storages mainly used in this research work).

The procedure of partitioning an image into various segments to reform an image into somewhat that is more significant and easier to analyze, defined as image segmentation. In real world applications, noisy images exits and there could be some measurement errors too. These factors affect the quality of segmentation, which is of major concern in medical fields where decisions about patients’ treatment are based on information extracted from radiological images. Several algorithms and techniques have developed for image segmentation and have their own advantages and disadvantages. Random walker method is a supervised segmentation method and it requires that it should be more efficient in producing effective segmentation results in case of medical images which are complex images. In the present paper, we are going to incorporate the advantages of fuzzy logic with a random walker to make resulting segmentation better in texture and quality. For this, we will use fuzzy rules to approximate boundaries in images which will improve segmentation results.