A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research

Adaptive image steganography based on optimal embedding and robust against chi-square attack

A real-life requirement motivated this case study of secure covert communication. Steganography is a technique used to transfer hidden information in an imperceptible manner. We proposed a novel approach of substitution technique of image steganography. The proposed method is flexible on size of secret message and allows us to embed a large amount of secret messages as well as maintaining good visual quality of stego-image. Using this method, message bits are embedded into uncertain and higher LSB layers, resulting in increased imperceptible and robustness of stego-image. Results show that the proposed algorithm provides large embedding capacity without losing the imperceptibility of the stego-image. The algorithm is also robust against Chi-square attack

Software watermarking using fixed size encoding and random dummy method insertion

The rise of software piracy has become rampant and a major concern among software developers. One of the techniques that can be used to discourage piracy is watermarking, by embedding developer’s watermark into software which can later be extracted to prove ownership. During the last few years, different algorithms were produced and developed to hide the watermark inside software. This paper enhances dummy method insertion technique in embedding and recognizing the watermark in Java class files. The enhancement includes the use of fixed size encoding scheme and random dummy method insertion. The proposed fixed size encoding scheme used hash function that can produce a fixed size watermark bit sequences. Random dummy method insertion selects a dummy method from a collection of dummy methods randomly. Finally, this study analyzes the enhancement of dummy method insertion technique using two different measures, namely data-rate and resilience of the watermarking algorithm. In terms of data rate, the results show that encoded watermark for proposed encoding scheme is always fixed even though size of watermark character is increased. In terms of resilience, experimental results show no similarity between class files and thus survived from collusion attack compared to previous method

A Practical Approach in Digitizing Analogue Audio Assets

Today, Broadcast companies are migrating from aTape-based workflow to that File-Based .This not only affectsgrowing number of channels and new ways of broadcasting, butalso it changes the way we store and retrieve our preciouscontents. Such historical essences are transferred to digital onlyonce and used numerous of times, so the method we use fortransferring these materials is so important. Technicalparameters of digitizing from Capturing and scanning to Storagemedia and formats are as important as non-technical conceptslike metadata and Cataloguing. In this paper, a practical methodof transferring Analog materials to digital formats and media ispresented. Suitable formats, backup, metadata and qualitycontrol methods are introduced. A very best practice of digitizingproject at IRIB huge archive will be introduced as a practicalexample

Analysis of statistical properties of chaos based image encryption by different mappings

Chaos mappings attracted a lot of research in recent years because of good properties of chaos maps in terms of continuous broad band power spectrums, sensitivity to initial conditions and similarity to random behavior. Many different scheme and algorithms have proposed in image encryption by chaos maps as well as different mappings. Not only can the structure of algorithm effect on statistical properties of image encryption, but also different chaos maps can, because of different dynamical properties. PWLCM has attracted a lot of research on recent years regarding good dynamical properties. In following sections, the statistical properties of Yoon algorithm [7] will analysis with PWLCM to conclude better statistical properties and better key space

New Method to Optimize Initial Point Values of Spatial Fuzzy c-means Algorithm

Fuzzy based segmentation algorithms are known to be performing well on medical images. Spatial fuzzy C-means (SFCM) is broadly used for medical image segmentation but it suffers from optimum selection of seed point initialization which is done either manually or randomly. In this paper, an enhanced SFCM algorithm is proposed by optimizing the SFCM initial point values. In this method in order to increasing the algorithm speed first the approximate initial values are determined by calculating the histogram of the original image. Then by utilizing the GWO algorithm the optimum initial values could be achieved. Finally By using the achieved initial values, the proposed method shows the significant improvement in segmentation results. Also the proposed method performs faster than previous algorithm i.e. SFCM and has better convergence. Moreover, it has noticeably improved the clustering effect

An ellipse framework for precise iris edge detection

Iris segmentation is the principal task of Iris based biometric identification systems. Always the low contrast between pupil and iris always affects on accuracy of detecting boundary between them. In order to increasing using a difference function and a factor matrix. We also enhance the technique to detect the pupil and iris as ellipse instead of circle. Experiments show that the proposed technique can segment the iris region and pupil region precisely. Based on our result, 99.34% of eyes have been segmented accurately in 1.24s averagely

Detecting SIM box fraud by using support vector machine and artificial neural network

Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud. The classification uses nine selected features of data extracted from Customer Database Record. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training

Classification of SIM box fraud detection using support vector machine and artificial neural network

SIM box fraud is classified as one of the dominant types of fraud instead of subscription and superimposed types of fraud. This fraud activity has been increasing dramatically each year due to the new modern technologies and the global superhighways of communication, resulting the decreasing of the revenue and quality of service in telecommunication providers especially in Africa and Asia. This paper outlines the Artificial Neural Network (ANN) and Support Vector Machine (SVM) to detect Global System for Mobile communication (GSM) gateway bypass in SIM Box fraud. The suitable features of data obtained from the extraction process of Customer Database Record (CDR) are used for classification in the development of ANN and SVM models. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy

Diffusion analysis of EFN-MDS structure

In general, block ciphers consist of one top-level structural model into which the round function F is plugged into. The study focuses on Extended-Feistel-Network (EFN) that is a generalization of a Feistel Network (FN). This structure is employed in several ciphers that were developed for Advanced Encryption Standard such as CAST-256, MARS and RC6. The problem with EFN is that it requires many rounds when the number of sub-blocks used in EFN is large. This paper proposed a new structural model that can overcome this problem by incorporating EFN with a linear transformation based on Maximum Distance Separable (MDS) codes. The diffusion analysis shows that EFN-MDS requires at most half the number of rounds to achieve completeness property as compared to EFN structure. Therefore the proposed structure is suitable for designing ciphers with scalable block sizes and ciphers with large block sizes