Securing communication over public communication channels is one of the challenging issues in the field of cryptography and information security. A stream cipher is presented as an approach for securing exchanged data between different parties through encryption. The core of stream cipher relies on its keystream generator, that is responsible for generating random and secure keystream of encrypting streaming data. Thus, the security of the keystream is measured by its randomness and its resistance to statistical and cryptanalytic attacks. As there is always a trade-off between the security and performance while designing new cryptographic primitives, we introduce a game theory-based stream cipher that performs fast and utilises the unpredictable behaviour of prisoners' dilemma players to generate random sequences that serve as keystreams. To assure further security, the ciphertext is coded by DNA bases that are randomly scrambled at the beginning of every iteration of generating new keystream. The proposed stream cipher's performance and security are thoroughly examined in terms of randomness, performance, and resistance to cryptanalytic attacks. The experiments show that our stream cipher can encrypt around 1230 Mbit/s. Statistically, our proposed stream cipher passed the NIST statistical tests successfully.

Plants leaves recognition is an important scientific field that is concerned of recognizing leaves using image processing techniques. Several methods are presented using different algorithms to achieve the highest possible accuracy. This paper provides an analytical survey of various methods used in image processing for the recognition of plants through their leaves. These methods help in extracting useful information for botanists to utilize the medicinal properties of these leaves, or for any other agricultural and environmental purposes. We also provide insights and a complete review of different techniques used by researchers that consider different features and classifiers. These features and classifiers are studied in term of their capabilities in enhancing the accuracy ratios of the classification methods. Our analysis shows that both of the Support Victor Machines (SVM) and the Convolutional Neural Network (CNN) are positively dominant among other methods in term of accuracy.

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