Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.

Blockchain technology has already changed industry and commercial enterprises remarkably. It is the underlying mechanism of a very well-known cryptocurrencies such as Bitcoin and Ethereum, and many other business applications. Therefore, its security draws the researchers’ attention more and more recently. One of Blockchain vulnerabilities is caused by weak randomness in ECDSA. A random number is not secure, cryptographically, which leads to a leakage in private key and even the user’s fund theft. As well the spam transaction attack may exploit the ECDSA weak randomness. This problem in security has been well known in cryptocurrencies community such as Bitcoin and fixed by applying RFC 6979 update in 2013. However, the problem is not entirely solved. The elliptic curve digital signature algorithm (ECDSA) was the first successful algorithm based on elliptic curve. This algorithm security depends on complexity of elliptic curve discrete logarithm problem (ECDLP). This algorithm applied in blockchain mechanism as a result of its low computational cost and short key. In this paper, we analyze the ECDSA weakness in blockchain and enhance its scheme by generating the signature with two secret keys. Using two secret keys will reduce the risk probability of revealing the secret key by knowing two messages. Therefore, the improved scheme can improve the security of the ECDSA.

Abstract:By employing the characteristics of the basic structure (keystream), Stream ciphers designers attempt to create algorithms that have advanced features from security and speed point of view. They take into consideration the state-of-the-art scientific and technical developments to design more advanced algorithm versions. This research proposes the design of a new efficient and secure stream cipher, named BloStream which proves to be more secure than conventional stream ciphers that commonly implement Exclusive-OR (XOR) for mixing. The proposed algorithm encompasses two major components. The first part involves the Pseudo Random Number Generator (PRNG), exhausting Rabbit algorithm. And the second part involves a nonlinear invertible round function (combiner), depending on Rijndael-like function algorithm, to perform the encryption/decryption processes. This new construction strengthens the weak XOR combiner. The proposed cipher is not only a random number generator but also a self-synchronizing stream cipher in such a way that the cipher text influences its internal functioning. The proposed algorithm utilizes 16-bytes secret key to encrypt the plaintext which is a multiple of 16-bytes up to 2 64 bytes length. The evaluation of BloStream performance, in terms of implementation aspects and security properties as well as the statistical test for keystream and comparison with similar systems revealed that, BloStream was more efficient, faster, and securer than the conventional stream ciphers.Key words: Stream ciphers, rabbit cipher, Rijndael-like function, combiner algorithm, PRNG. IntroductionStream ciphers are an important class of symmetric encryption algorithms. They encrypt individual characters or binary digits of a plaintext message one at a time, using an encryption transformation which varies with time. They are also more appropriate, and in some cases mandatory (e.g. in some telecommunications applications), when buffering is limited or when characters must be individually Ring the plaintext with a random key. The drawback of the Vernam cipher is that the keystream must possess a true random sequence, shared by the sender and the receiver, and it can only be used once [1]-[3]. A combiner is the heart of a stream cipher, which generally employs an 'additive' combiner such as XOR. Additive combiners have absolutely no strength at all; this means that, if an opponent somehow comes up International Journal of Applied Physics and Mathematics 153Volume 5, Number 3, July 2015 An alternate approach to the design of a secure stream ciphers is to seek combining functions which can resist attack; such functions would act to conceal the pseudo-random sequence from analysis. Such cryptographic combining functions could be utilized to substitute the Vernam XOR combiner provided that they have an inverse. An improved combiner is intended to enhance the sophistication of cryptanalysis, making it more time consuming and expensive than simple combiners [6]. Dynamic substitution is a way to build a cryptographic comb...

With the development of high-speed network technologies, there has been a recent rise in the transfer of significant amounts of sensitive data across the Internet and other open channels. The data will be encrypted using the same key for both Triple Data Encryption Standard (TDES) and Advanced Encryption Standard (AES), with block cipher modes called cipher Block Chaining (CBC) and Electronic CodeBook (ECB). Block ciphers are often used for secure data storage in fixed hard drives, portable devices, and safe network data transport. Therefore, to assess the security of the encryption method, it is necessary to become familiar with and evaluate the algorithms of cryptographic systems. Block cipher users need to be sure that the ciphers they employ are secure against various attacks.        A Fully Connected Neural Network (FCNN) model was initially used to assess how well the models were classified. Then, all models, including encoder models, were assessed using True Positive (TP) measures for successful classification of the discovered encoder and False Positive (FP) measures for imprecise categorization. The accuracy value, retrieval, loss, precision value, and F1 score were then calculated using a confusion matrix to assess the model's efficacy (abbreviated as F1). ECB results with an accuracy of 85% and CBC results with an accuracy of 88% were produced, and the parameters of the FCNN model were tweaked to provide better results. These results helped to identify the encryption algorithm more precisely and evaluate it.

Implementation of TSFS (Transposition, Substitution, Folding, and Shifting) algorithm as an encryption algorithm in database security had limitations in character set and the number of keys used. The proposed cryptosystem is based on making some enhancements on the phases of TSFS encryption algorithm by computing the determinant of the keys matrices which affects the implementation of the algorithm phases. These changes showed high security to the database against different types of security attacks by achieving both goals of confusion and diffusion.