Cryptography has been widely used as a mean to secure message communication. A cryptosystem is made up of a publicly available algorithm and a secretly kept key. The algorithm is responsible for transforming the original message into something unintelligible. The result of losing the key or cracked algorithm can be catastrophic, where all secret communications will be known to adversaries. One way to find the key is by brute-force attacks which try every possible combination of keys. The only way to prevent this is by having the key of sufficiently large enough such that finding the right key cannot be made in a reasonable time frame. However, large key size imposes extra computational works which result in larger energy consumption and thus more heat dissipation to the environment. Therefore, the selection of key size does not only depends on the required security level, but also factors such as the ability of the processor and the available memory resources. The advent of multi-core technology promises some improvements in the utilization of computational resources. Many reports support the idea that multi-core technology brought a significant improvement over the single core technology. In this study, we investigate this hypothesis on the RSA cryptosystem in relation to the key size. Earlier studies reported multi-core efficiency in normal applications, but the question arises if multi-core architecture remains superior to a single core architecture when dealing with applications involving large integers. From our experimentation, we observe that the higher the number of cores, the better the performance of the encryption and decryption processes. The quad-core technology can smoothly handle operations involving 8192 bits key.
Steganography is the science of concealing a secret message by embedding it into innocent carriers such as text, audio, images, etc. It plays a crucial role in a broad range of security applications such as securing message exchange, user authentication and copyrighting. One of the most effortless and widely-used techniques is the age-old Least Significant Bits (LSB) algorithm, which can be implemented in both transformative and spatial domains. The advantage of this technique is that it can be used with any form of digital media. However, operating pixels on a sequential basis leaves the algorithm susceptible to many steganalysis techniques. Consequently, it is easy for the attacker to recognize the inclusion of a secret message within the media and thus to proceed with the extraction. Therefore, it is necessary to provide an extra layer of security to protect the data. In this study, we propose a random selection of pixels that hold a secret message based on an integer solution of the elliptic curve equation. In addition, we have embedded noise bits into the unused pixels to make the steganalysis process more difficult. The attacker not only needs to guess which pixels (out of all pixels in the image) have been selected to carry the secret, but also must arrange them in the correct order. The results show that the proposed algorithm achieves a significant security improvement in comparison to standard LSB when it comes to defending against bruteforce attacks with a subordinate effect of image quality.
An efficient approach to secure information is critically needed at present. Cryptography remains the best approach to achieve security. On this basis, the National Institute of Standards and Technology (NIST) selected Rijndael, which is a symmetric block cipher, as the advanced encryption standard (AES). The MixColumns transformation of this cipher is the most important function within the linear unit and the major source of diffusion. Dynamic MixColumns transformation can be used to enhance the AES security. In this study, a method to enhance the AES security is developed on the basis of two methods. The first method is an extension of a previous study entitled “A novel Approach for Enhancing Security of Advance Encryption Standard using Private XOR Table and 3D chaotic regarding to Software quality Factor.” In the current study, the fixed XOR operation in AES rounds is replaced with a dual dynamic XOR table by using a 3D chaotic map. The dual dynamic XOR table is based on 4 bits; one is used for even rounds, and the other is used for odd rounds. The second method is dynamic MixColumns transformation, where the maximum distance separable (MDS) matrix of the MixColumns transformation, which is fixed and public in every round, is changed with a dynamic MDS matrix, which is private, by using a 3D chaotic map. A 3D chaotic map is used to generate secret keys. These replacements enhance the AES security, particularly the resistance against attacks. Diehard and NIST tests, entropy, correlation coefficient, and histogram are used for security analysis of the proposed method. C++ is used to implement the proposed and original algorithms. MATLAB and LINX are used for the security analysis. Results show that the proposed method is better than the original AES.
Energy consumption is one of the most critical issues in wireless sensor network (WSN). For a sensor device, transmission of data is considered as the most energy consuming task, and it mostly depends on the size of the data. Fortunately, data compression can be used to minimise the transmitted data size and thus extend sensor's lifetime. In this paper, we propose a new lossless compression algorithm that can handle small data communication in WSNs. Using compression ratio, memory usage, number of instructions and execution speed as a comparison parameters, the proposed algorithm is measured against a set of existing algorithms. Two different datasets have been used for this purpose; namely, self-generated dataset and real sensor dataset from Harvard Sensor Library. As a result, the proposed algorithm not only outclasses other existing algorithms but most importantly produces positive compression ratio throughout the whole test where most existing algorithms experience an expansion in data size when dealing with very small data.
Energy consumption is one of the most critical issues in wireless sensor network (WSN). For a sensor device, transmission of data is considered as the most energy consuming task, and it mostly depends on the size of the data. Fortunately, data compression can be used to minimise the transmitted data size and thus extend sensor's lifetime. In this paper, we propose a new lossless compression algorithm that can handle small data communication in WSNs. Using compression ratio, memory usage, number of instructions and execution speed as a comparison parameters, the proposed algorithm is measured against a set of existing algorithms. Two different datasets have been used for this purpose; namely, self-generated dataset and real sensor dataset from Harvard Sensor Library. As a result, the proposed algorithm not only outclasses other existing algorithms but most importantly produces positive compression ratio throughout the whole test where most existing algorithms experience an expansion in data size when dealing with very small data.
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