This paper presents an ultra-secure router-to-router key exchange system. The key exchange process can be initiated by either router at will and can be carried out as often as required. We compare the efficacy of the proposed approach with contemporary quantum key distribution (QKD) systems and show that quantum-level security is attainable without resorting to single photon generators and other attendant instrumentation associated with QKD. Furthermore, the proposed system addresses the extremely limited geographical reach of commercially available QKD systems and other environmental restrictions they must operate in. The proposed system carries out all processing in electronics and is not vulnerable to the man in the middle attack. The medium of transfer can, of course, be optical fibers as is common in telecommunication.

Tor is the low-latency anonymity tool and one of the prevalent used open source anonymity tools for anonymizing TCP traffic on the Internet used by around 500,000 people every day. Tor protects user’s privacy against surveillance and censorship by making it extremely difficult for an observer to correlate visited websites in the Internet with the real physical-world identity. Tor accomplished that by ensuring adequate protection of Tor traffic against traffic analysis and feature extraction techniques. Further, Tor ensures anti-website fingerprinting by implementing different defences like TLS encryption, padding, and packet relaying. However, in this paper, an analysis has been performed against Tor from a local observer in order to bypass Tor protections; the method consists of a feature extraction from a local network dataset. Analysis shows that it’s still possible for a local observer to fingerprint top monitored sites on Alexa and Tor traffic can be classified amongst other HTTPS traffic in the network despite the use of Tor’s protections. In the experiment, several supervised machine-learning algorithms have been employed. The attack assumes a local observer sitting on a local network fingerprinting top 100 sites on Alexa; results gave an improvement amongst previous results by achieving an accuracy of 99.64% and 0.01% false positive.

Steganography is the art of amalgamating the secret message into another public message which may be text, audio or video file in a way that no one can know or imperceptible the existence of message. So, the secret message can send in a secret and obscure way using steganography techniques. In this paper, we use the audio steganography where the secret message conceal in audio file. We use audio rather than image because the human auditory system (HAS) is more sensitive than human visual system (HVS). We propose an audio steganography algorithm, for embedding text, audio or image based on Lifting Wavelet Transform (LWT) transform with modification of Least Significant Bit (LSB) technique and three random keys where these key is used to increase the robustness of the LSB technique and without it no one can know the sort of secret message type, the length of the secret message and the initial position of the embedded secret message in LSB. The performance of our algorithm is calculated using SNR and we compare the values of our proposed method with some known algorithms.

Cloud computing enables the users to outsource and access data economically from the distributed cloud server. Once the data leaves from data owner premises, there is no privacy guarantee from the untrusted parties in cloud storage system. Providing data privacy for the outsourced sensitive data is a challenging task in cloud computing. In this paper we have proposed a Trusted Third party Query Process(TTQP) method to provide data privacy for graph structured outsourced data. This method utilize the encrypted graph frequent features search index list to search the matched query graph features in graph data base. The proposed system has analyzed in terms of different size of data graphs, index storage, query feature size and query execution time. The performance analysis of our proposed system shows, this method is more secure than the existing privacy preserving encrypted Query Graph (PPQG).

RC4 is one of the most widely used stream cipher due to its simplicity, speed and efficiency. In this paper we have presented a chronological survey of RC4 stream cipher demonstrating its weaknesses followed by the various RC4 enhancements from the literature. From the recently observed cryptanalytic attempts on RC4 it is established that innovative research efforts are required to develop secure RC4 algorithm, which can remove the weaknesses of RC4, such as biased bytes, key collisions, and key recovery attacks specifically on WEP and WPA. These flaws in RC4 are offering open challenge for developers. Hence our chronological survey corroborates the fact that even though researchers are working on RC4 stream cipher since last two decades, it still offers a plethora of research issues related to statistical weaknesses in either state or keystream.

In recent years, secure information sharing has become a top requirement for many applications such as banking and military. Secret Sharing is an effective method to improve security of data. Secret Sharing helps to avoid storing data at a single point through dividing and distributing “shares” of secrets and recovering it later with no loss of original quality. This paper proposes a new Secret Sharing scheme for secure transmission of color images. The key features of this scheme are better visual quality of the recovered image with no pixel expansion, eliminating half toning of color images, eliminating the need for code book to decrypt images since reconstruction is done through XOR ing of all images and non-requirement of regeneration of shares for addition or deletion of users leading to less computational complexity. Besides these advantages, this scheme also helps to renew shares periodically and is highly beneficial in applications where data has to be stored securely in a database.

Limited and constrained energy resources of wireless sensor network should be used wisely to prolong sensor nodes lifetime. To achieve high energy ef?ciency and to increase wireless sensor network lifetime, sensor nodes are grouped together to form clusters. Organizing wireless sensor networks into clusters enables the ef?cient utilization of limited energy resources of the deployed sensor nodes. However, the problems of unbalanced energy consumption exist in intra and inter cluster communication, and it is tightly bound to the role and the location of a sensor nodes and cluster heads in the network. Also, clustering mechanism results in an unequal load distribution in the network. This paper presents an analytical and conceptual model of Energy-ef?cient edge-based network partitioning scheme proposed for wireless sensor networks. Also, it analyzes different network design space proposed for wireless sensor networks and evaluates their performance. From the experimental results it is observed that, with proper network organization mechanism, sensor network resources are utilized effectively to elevate network lifetime.

This paper proposes an optimal region growing threshold for the segmentation of magnetic resonance images (MRIs). The proposed algorithm combines local search procedure with thresholding region growing to achieve better generic seeds and optimal thresholds for region growing method. A procedure is used to detect the best possible seeds from a set of data distributed all over the image as a high accumulator of the histogram. The output seeds are fed to the local search algorithm to extract the best seeds around initial seeds. Optimal thresholds are used to overcome the limitations of region growing algorithm and to select the pixels sequentially in a random walk starting at the seed point. The proposed algorithm works automatically without any predefined parameters. The proposed algorithm is applied to the challenging application “gray matter/white matter” segmentation datasets. The experimental results compared with other segmentation techniques show that the proposed algorithm produces more accurate and stable results.