Analysis of Four Historical Ciphers Against Known Plaintext Frequency Statistical Attack

The need of keeping information securely began thousands of years. The practice to keep the information securely is by scrambling the message into unreadable form namely ciphertext. This process is called encryption. Decryption is the reverse process of encryption. For the past, historical ciphers are used to perform encryption and decryption process. For example, the common historical ciphers are Hill cipher, Playfair cipher, Random Substitution cipher and Vigenère cipher. This research is carried out to examine and to analyse the security level of these four historical ciphers by using known plaintext frequency statistical attack. The result had shown that Playfair cipher and Hill cipher have better security compare with Vigenère cipher and Random Substitution cipher

Neural Aided Statistical Attack for Cryptanalysis

In Crypto’19, Gohr proposed the first deep learning-based key recovery attack on 11-round Speck32/64, which opens the direction of neural aided cryptanalysis. Until now, neural aided cryptanalysis still faces two problems: (1) the attack complexity estimations rely purely on practical experiments. There is no theoretical framework for estimating theoretical complexity. (2) it does not work when there are not enough neutral bits that exist in the prepended differential. To the best of our knowledge, we are the first to solve these two problems. In this paper, we propose a Neural Aided Statistical Attack (NASA) that has the following advantages: (1) NASA supports estimating the theoretical complexity. (2) NASA does not rely on any special properties including neutral bits. (3) NASA is applicable to large-size ciphers. Moreover, we propose three methods for reducing the attack complexity of NASA. One of the methods is based on a newly proposed concept named Informative Bit that reveals an important phenomenon. Four attacks on 9-round or 10-round Speck32/64 are executed to verify the correctness of NASA. To further highlight the advantages of NASA, we have performed a series of experiments. At first, we apply NASA and Gohr’s attack to round reduced DES. Since NASA does not rely on neutral bits, NASA breaks 10-round DES while Gohr’s attack breaks 8-round DES. Then, we compare the time consumption of attacks on 11-round Speck32/64. When the newly proposed three methods are used, the time consumption of NASA is almost the same as that of Gohr’s attack. Besides, NASA is applied to 13-round Speck32/64. At last, we introduce how to analyze the resistance of large-size ciphers with respect to NASA, and apply NASA to 14-round Speck96/96. The code of this paper is available at https://github.com/AI-Lab-Y/NASA. Our work arguably raises a new direction for neural aided cryptanalysis

Improved Neural Aided Statistical Attack for Cryptanalysis

At CRYPTO 2019, Gohr improved attacks on Speck32/64 using deep learning. In 2020, Chen et al. proposed a neural aided statistical attack that is more generic. Chen et’s attack is based on a statistical distinguisher that covers a prepended differential transition and a neural distinguisher. When the probability of the differential transition is pq, its impact on the data complexity is O(p^{-2}q^{-2}. In this paper, we propose an improved neural aided statistical attack based on a new concept named Homogeneous Set. Since partial random ciphertext pairs are filtered with the help of homogeneous sets, the differential transition’s impact on the data complexity is reduced to O(p^{−1}q^{−2}). As a demonstration, the improved neural aided statistical attack is applied to round-reduced Speck. And several better attacks are obtained

Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk (SW) tests, are proposed to detect and suppress spectrum sensing data falsification (SSDF) attacks by malicious users in cooperative spectrum sensing. First, we consider basic and statistical SSDF attacks, where the malicious users attack independently. Then we propose a new SSDF attack, which involves cooperation among malicious users by masking. In practice, the number of malicious users is unknown. Thus, it is necessary to estimate the number of malicious users, which is found using clustering and largest gap method. However, we show using Monte Carlo simulations that, these methods fail to estimate the exact number of malicious users when they cooperate. To overcome this, we propose a modified largest gap method.Comment: Accepted in Proceedings of 79th IEEE Vehicular Technology Conference-Spring (VTC-Spring), May 2014, Seoul, South Kore

A Practical Adaptive Key Recovery Attack on the LGM (GSW-like) Cryptosystem

Under embargo until: 2022-07-15We present an adaptive key recovery attack on the leveled homomorphic encryption scheme suggested by Li, Galbraith and Ma (Provsec 2016), which itself is a modification of the GSW cryptosystem designed to resist key recovery attacks by using a different linear combination of secret keys for each decryption. We were able to efficiently recover the secret key for a realistic choice of parameters using a statistical attack. In particular, this means that the Li, Galbraith and Ma strategy does not prevent adaptive key recovery attacks.acceptedVersio