Design and analysis of lightweight block ciphers have become more popular due to the fact that the future use of block ciphers in ubiquitous devices is generally assumed to be extensive. In this respect, several lightweight block ciphers are designed, of which Present and Hight are two recently proposed ones by Bogdanov et al. and Hong et al. respectively. In this paper, we propose new attacks on Present and Hight. Firstly, we present the first related-key cryptanalysis of 128bit keyed Present by introducing 17-round related-key rectangle attack with time complexity approximately 2 104 memory accesses. Moreover, we further analyze the resistance of Hight against impossible differential attacks by mounting new 26-round impossible differential and 31-round related-key impossible differential attacks where the former requires time complexity of 2 119.53 reduced round Hight evaluations and the latter is slightly better than exhaustive search.

Abstract. In this paper we present a new statistical cryptanalytic technique that we call improbable differential cryptanalysis which uses a differential that is less probable when the correct key is used. We provide data complexity estimates for this kind of attacks and we also show a method to expand impossible differentials to improbable differentials. By using this expansion method, we cryptanalyze 13, 14, and 15-round CLEFIA for the key sizes of length 128, 192, and 256 bits, respectively. These are the best cryptanalytic results on CLEFIA up to this date.

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Cyber attacks constitute a significant threat to organizations with implications ranging from economic, reputational, and legal consequences. As cybercriminals' techniques get sophisticated, information security professionals face a more significant challenge to protecting information systems. In today's interconnected realm of computer systems, each attack vector has a network dimension. The present study investigates network intrusion attempts with anomaly-based machine learning models to provide better protection than the conventional misuse-based models. Two models, namely an ensemble learning model and a convolutional neural network model, were built and implemented on a data set gathered from a real-life, institutional production environment. To demonstrate the models' reliability and validity, they were applied to the UNSW-NB15 benchmarking data set. The type of attack was limited to probing attacks to keep the scope of the study manageable. The findings revealed high accuracy rates, the CNN model being slightly more accurate.INDEX TERMS Anomaly-based, misuse-based, intrusion detection systems, probing attacks.

Grassi et al. [Gra+16] introduced subspace trail cryptanalysis as a generalization of invariant subspaces and used it to give the first five round distinguisher for Aes. While it is a generic method, up to now it was only applied to the Aes and Prince. One problem for a broad adoption of the attack is a missing generic analysis algorithm. In this work we provide efficient and generic algorithms that allow to compute the provably best subspace trails for any substitution permutation cipher.