A Security Analysis of IoT Encryption: Side-channel Cube Attack on Simeck32/64

Simeck, a lightweight block cipher has been proposed to be one of the encryption that can be employed in the Internet of Things (IoT) applications. Therefore, this paper presents the security of the Simeck32/64 block cipher against side-channel cube attack. We exhibit our attack against Simeck32/64 using the Hamming weight leakage assumption to extract linearly independent equations in key bits. We have been able to find 32 linearly independent equations in 32 key variables by only considering the second bit from the LSB of the Hamming weight leakage of the internal state on the fourth round of the cipher. This enables our attack to improve previous attacks on Simeck32/64 within side-channel attack model with better time and data complexity of 2^35 and 2^11.29 respectively.Comment: 12 pages, 6 figures, 4 tables, International Journal of Computer Networks & Communication

Agonistic behavior of captive saltwater crocodile, crocodylus porosus in Kota Tinggi, Johor

Agonistic behavior in Crocodylus porosus is well known in the wild, but the available data regarding this behavior among the captive individuals especially in a farm setting is rather limited. Studying the aggressive behavior of C. porosus in captivity is important because the data obtained may contribute for conservation and the safety for handlers and visitors. Thus, this study focuses on C. porosus in captivity to describe systematically the agonistic behaviour of C. porosus in relation to feeding time, daytime or night and density per pool. This study was carried out for 35 days in two different ponds. The data was analysed using Pearson’s chi-square analysis to see the relationship between categorical factors. The study shows that C. porosus was more aggressive during daylight, feeding time and non-feeding time in breeding enclosure (Pond C, stock density =0.0369 crocodiles/m2) as compared to non-breeding pond (Pond B, stock density =0.3317 crocodiles/m2) where it is only aggressive during the nighttime. Pond C shows the higher domination in the value of aggression in feeding and non-feeding time where it is related to its function as breeding ground. Chi-square analysis shows that there is no significant difference between ponds (p=0.47, χ2= 2.541, df= 3), thus, there is no relationship between categorical factors. The aggressive behaviour of C. porosus is important for the farm management to evaluate the risk in future for the translocation process and conservation of C. porosus generally

LEE: Light‐Weight Energy‐Efficient encryption algorithm for sensor networks

Data confidentiality in wireless sensor networks is mainly achieved by RC5 and Skipjack encryption algorithms. However, both algorithms have their weaknesses, for example RC5 supports variable-bit rotations, which are computationally expensive operations and Skipjack uses a key length of 80-bits, which is subject to brute force attack. In this paper we introduce a light-weight energy- fficient encryption-algorithm (LEE) for tiny embedded devices, such as sensor network nodes. We present experimental results of LEE under real sensor nodes operating in TinyOS. We also discuss the secrecy of our algorithm by presenting a security analysis of various tests and cryptanalytic attacks

Using classifiers to predict linear feedback shift registers

Proceeding of: IEEE 35th International Carnahan Conference on Security Technology. October 16-19, 2001, LondonPreviously (J.C. Hernandez et al., 2000), some new ideas that justify the use of artificial intelligence techniques in cryptanalysis are presented. The main objective of that paper was to show that the theoretical next bit prediction problem can be transformed into a classification problem, and this classification problem could be solved with the aid of some AI algorithms. In particular, they showed how a well-known classifier called c4.5 could predict the next bit generated by a linear feedback shift register (LFSR, a widely used model of pseudorandom number generator) very efficiently and, most importantly, without any previous knowledge over the model used. The authors look for other classifiers, apart from c4.5, that could be useful in the prediction of LFSRs. We conclude that the selection of c4.5 by Hernandez et al. was adequate, because it shows the best accuracy of all the classifiers tested. However, we have found other classifiers that produce interesting results, and we suggest that these algorithms must be taken into account in the future when trying to predict more complex LFSR-based models. Finally, we show some other properties that make the c4.5 algorithm the best choice for this particular cryptanalytic problem.Publicad