Journal of Computing and Security
Volume:2 Issue: 1, Winter 2015

Authenticated encryption (AE) schemes provide both privacy and integrity of data. CAESAR is a competition to design and analysis of the AE schemes. An AE scheme has two components: a mode of operation and a primitive. In this paper JHAE, a novel authenticated encryption mode, is presented based on the JH (SHA-3 finalist) hash mode. JHAE is an on-line and single-pass dedicated AE mode based on permutation that supports optional associated data (AD). It is proved that this mode, based on ideal permutation, achieves privacy and integrity up to O(2n/2) queries where the length of the used permutation is 2n. To decrypt, JHAE does not require the inverse of its underlying permutation and therefore saves area space. JHAE has been used by Artemia, one of the CAESAR’s first round candidates.

Data hiding in halftone images is a challenging task since these images are very susceptible to alterations. Current steganographic methods for these types of images either embed the data after the image is completely halftoned or modify the halftoning process for embedding purposes. In this paper we present a third type of steganography which performs the embedding by altering the grayscale image prior to its conversion to the halftone form. The goal of the proposed algorithm is to minimize the amount of alterations that are caused in the grayscale image. We compared our algorithm with a secure steganographic method. Also an effective steganalytic attack is applied to show the security of the proposed algorithm and the secure capacity of the proposed method was calculated to be 2% of bi-level image size while the secure capacity of its counterpart was calculated to be under 1%. It is shown that the proposed algorithm in terms of visual quality and security, as compared to the existing algorithms, produces superior results.

In this article, our aim is to quantify the impact of three reference architectural styles, i.e. Aspect-peer-to-peer, Aggregator-escalator-peer, and Chain-of-configurators on the maintainability attribute in self-healing systems. To do so, we used metrics of coupling and cohesion. To show the effectiveness of our method, we applied it to the Wireless Body Area Network (WBAN) as a case study. Our findings showed the Chain-of-configurators architectural style, in comparison with two other styles, had the least (best) coupling and the most (best) cohesion. In fact, it was the best style from the maintainability point of view.

The moduli set2n−1 −1,2n −1,2n,2n −1 has been recently proposed in literature for class of 4n-bit dynamic range in residue number system. Due to only utilizing modulus in the form of 2k − 1 besides modulo 2n, this moduli set enjoys the efficient Arithmetic Unit (AU) in its architecture. Not only does the efficiency of a RNS system depend on the residue arithmetic unit but it also is limited to the residue to binary converter. In this paper, a new two level residue-to-binary converter architecture based on Mixed Radix Conversion (MRC) is presented for the aforementioned moduli set. The proposed converter includes two levels of design based on MRC properties. Firstly, the 3-moduli subset2n−1 −1,2n −1,2n −1 is properly organized and as it does not calculate several values, it results in some cost modifications. Eventually, a two-moduli set2n−1 −1
2n −1
(2n −1),2n is formed to compute the binary of RNS counterpart. The proposed architecture is shown to be more efficient both in terms of hardware cost and conversion delay in comparison with the related state-of-the-art works.

Face recognition systems suffer from different spoofing attacks like photograph-based and video-based spoofing attacks. This paper presents a new method to empower the face recognition systems against video-based spoofing by employing efficient scene texture analyzing. To this end, the scene of input and reference images are divided into same non-overlapped blocks and the texture pattern of each block is extracted by local binary pattern (LBP) operator. To reduce the sensitivity of LBP to noise and also to increase the reliability of the proposed method, first input image transformed to YCbCr color space and then similarity of texture pattern in Y, Cb and Cr channels are extracted independently. The majority of similarity of three channels is used as the final similarity of each block. The ratio of same blocks in the input image and reference image is used as a measure for detecting video-based spoofing attacks. The performance of the proposed algorithm is evaluated using several scenarios. The obtained results show the effectiveness of the proposed algorithm against video-based spoofing attacks in real environments.

Author identification is an attempt to demonstrate the characteristics of the author of a piece of language information so that in the end, it would be possibletosignificantlydistinguishthedifferencebetweenvarioustextswritten by different people. The rapid development of Internet communication has caused Internet tools with anonymous identity, such as emails and weblogs, to become popular communication methods for the perpetrators of illegal acts and has raised some security concerns. Persian language is of interest to a great number of different individuals and organizations for various reasons such as political, social, artistic, cultural and religious issues. In this paper, a number of intelligent writeprint methods which help automatic identification of a Persian writer based on his/her writing style are studied and compared. For this purpose, after collecting two different databases, five feature types including lexical, syntactic, semantic and application-specific features, were used for extracting stylometric characteristics. In this study KNN, Delta, Neural Networks ,Decision Treeand Linear Discriminate Analysis classi fication methods were applied to these databases. The results and their comparison showed that Linear Discriminate Analysis and KNN methods ranked first and second, respectively, in terms of accuracy among the studied methods.