As side‐channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side‐channel analysis. Howe...

As researches on the quantum computer have progressed immensely, interests in post-quantum cryptography have greatly increased. NTRU is one of the well-known algorithms due to its practical key sizes and fast performance along with the resistance against the quantum adversary. Although NTRU has withstood various algebraic attacks, its side-channel resistance must also be considered for secure implementation. In this paper, we proposed the first single trace attack on NTRU. Previous side-channel attacks on NTRU used numerous power traces, which increase the attack complexity and limit the target algorithm. There are two versions of NTRU implementation published in succession. We demonstrated our attack on both implementations using a single power consumption trace obtained in the decryption phase. Furthermore, we propose a countermeasure to prevent the proposed attacks. Our countermeasure does not degrade in terms of performance.

The security of current key exchange protocols such as Diffie-Hellman key exchange is based on the hardness of number theoretic problems. However, these key exchange protocols are threatened by weak random number generators, advances to CPU power, a new attack from the eavesdropper, and the emergence of a quantum computer. Quantum Key Distribution (QKD) addresses these challenges by using quantum properties to exchange a secret key without the risk of being intercepted. Recent developments on the QKD system resulted in a stable key generation with fewer errors so that the QKD system is rapidly becoming a solid commercial proposition. However, although the security of the QKD system is guaranteed by quantum physics, its careless implementation could make the system vulnerable. In this paper, we proposed the first side-channel attack on plug-and-play QKD system. Through a single electromagnetic trace obtained from the phase modulator on Alice's side, we were able to classify the electromagnetic trace into four classes, which corresponds to the number of bit and basis combination in the BB84 protocol. We concluded that the plug-and-play QKD system is vulnerable to side-channel attack so that the countermeasure must be considered.

Unified point addition for computing elliptic curve point addition and doubling is considered to be resistant to simple power analysis. Recently, new side-channel attacks, such as recovery of secret exponent by triangular trace analysis and horizontal collision correlation analysis, have been successfully applied to elliptic curve methods to investigate their resistance to side-channel attacks. These attacks turn out to be very powerful since they only require leakage of a single power consumption trace. In this paper, using these side-channel attack analyses, we introduce two vulnerabilities of unified point addition on the binary Huff curve. Also, we propose a new unified point addition method for the binary Huff curve. Furthermore, to secure against these vulnerabilities, we apply an equivalence class to the side-channel atomic algorithm using the proposed unified point addition method.

Side-channel analysis is a critical threat to cryptosystems on the Internet of Things and in relation to embedded devices, and appropriate side-channel countermeasure must be required for physical security. A combined countermeasure approach employing first-order masking and desynchronization simultaneously is a general and cost-efficient approach to counteracting side-channel analysis. With the development of side-channel countermeasures, there are plenty of advanced attacks introduced to defeat such countermeasures. At CARDIS 2013, Belgarric et al. first proposed time-frequency analysis, a promising attack regarding the complexity of computation and memory compared to other attacks, such as conventional second-order side-channel analysis after synchronization. Nevertheless, their time-frequency analysis seems to have lower performance than expected against some datasets protected by combined countermeasures. It is therefore required to study the factors that affect the performance of time-frequency analysis. In this paper, we investigate Belgarric et al.’s time-frequency analysis and conduct a mathematical analysis in regard to the preprocessing of frequency information for second-order side-channel analysis. Based on this analysis, we claim that zero-mean preprocessing enhances the performance of time-frequency analysis. We verify that our analysis is valid through experimental results from two datasets, which are different types of first-order masked Advanced Encryption Standard (AES) software implementations. The experimental results show that time-frequency analysis with zero-mean preprocessing seems to have an enhanced or complementary performance compared to the analysis without preprocessing.

Side-channel analysis is one of the vulnerabilities in IoT’s cryptographic systems. To present reliable side-channel analysis results, it is crucial to collect considerable amounts of power consumption traces. Handling large datasets in this context can be highly inefficient regarding data transmission and storage. In various fields, the importance of compression techniques for efficient data storage has increased significantly. Compression techniques for various types of datasets are often designed with consideration for their data characteristics, much like JPEG for images. However, despite its relatively low compression rates, side-channel analysis researchers commonly use Deflate for data compression due to its simplicity and universality. In this paper, we propose a novel side-channel data compression technique using autoencoders, which offers higher compression rates than Deflate while maintaining decompression times and achieving a fast compression time. Furthermore, our model preserves the characteristics of the side-channel throughout the compression and decompression processes. To verify this, we conducted experiments comparing the original data with traces decompressed data using our technique through correlation power analysis. The results confirmed that they exhibit similar correlation coefficients and identical peak positions, demonstrating the preservation of essential data properties.