Improving Non-Profiled Side-Channel Attacks using Autoencoder based Preprocessing

In recent years, deep learning-based side-channel attacks have established their position as mainstream. However, most deep learning techniques for cryptanalysis mainly focused on classifying side-channel information in a profiled scenario where attackers can obtain a label of training data. In this paper, we introduce a novel approach with deep learning for improving side-channel attacks, especially in a non-profiling scenario. We also propose a new principle of training that trains an autoencoder through the noise from real data using noise-reduced labels. It notably diminishes the noise in measurements by modifying the autoencoder framework to the signal preprocessing. We present convincing comparisons on our custom dataset, captured from ChipWhisperer-Lite board, that demonstrate our approach outperforms conventional preprocessing methods such as principal component analysis and linear discriminant analysis. Furthermore, we apply the proposed methodology to realign de-synchronized traces that applied hiding countermeasures, and we experimentally validate the performance of the proposal. Finally, we experimentally show that we can improve the performance of higher-order side-channel attacks by using the proposed technique with domain knowledge for masking countermeasures

Shining Light on the Shadow: Full-round Practical Distinguisher for Lightweight Block Cipher Shadow

Shadow is a lightweight block cipher proposed at IEEE IoT journal 2021. Shadow’s main design principle is adopting a variant 4- branch Feistel structure in order to provide a fast diffusion rate. We define such a structure as Shadow structure and prove that it is al- most identical to the Generalized Feistel Network, which invalidates the design principle. Moreover, we give a structural distinguisher that can distinguish Shadow structure from random permutation with only two plaintext/ciphertext pairs. By exploiting the key schedule, the distin- guisher can be extended to key recovery attack with only one plain- text/ciphertext pair. Furthermore, by considering Shadow’s round func- tion, only certain forms of monomials can appear in the ciphertext, re- sulting in an integral distinguisher of four plaintext/ciphertext pairs. Even more, the algebraic degree does not increase more than 12 for Shadow-32 and 20 for Shadow-64 regardless of rounds used. Our results show that Shadow is highly vulnerable to algebraic attacks, and that algebraic attacks should be carefully considered when designing ciphers with AND, rotation, and XOR operations

Pulmonary Toxicity and Proteomic Analysis in Bronchoalveolar Lavage Fluids and Lungs of Rats Exposed to Copper Oxide Nanoparticles

Copper oxide nanoparticles (CuO NPs) were intratracheally instilled into lungs at concentrations of 0, 0.15, and 1.5 mg/kg bodyweight to 7-week-old Sprague–Dawley rats. The cytotoxicity, immunotoxicity, and oxidative stress were evaluated, followed by proteomic analysis of bronchoalveolar lavage fluid (BALF) and lungs of rats. The CuO NPs-exposed groups revealed dose-dependent increases in total cells, polymorphonuclear leukocytes, lactate dyhydrogenase, and total protein levels in BALF. Inflammatory cytokines, including macrophage inflammatory protein-2 and tumor necrosis factor-α, were increased in the CuO NPs-treated groups. The expression levels of catalase, glutathione peroxidase-1, and peroxiredoxin-2 were downregulated, whereas that of superoxide dismutase-2 was upregulated in the CuO NPs-exposed groups. Five heat shock proteins were downregulated in rats exposed to high concentrations of CuO NPs. In proteomic analysis, 17 proteins were upregulated or downregulated, and 6 proteins were validated via Western blot analysis. Significant upregulation of 3-hydroxy-3-methylglutaryl-CoA synthase and fidgetin-like 1 and downregulation of annexin II, HSP 47 and proteasome α1 occurred in the CuO NPs exposed groups. Taken together, this study provides additional insight into pulmonary cytotoxicity and immunotoxicity as well as oxidative stress in rats exposed to CuO NPs. Proteomic analysis revealed potential toxicological biomarkers of CuO NPs, which also reveals the toxicity mechanisms of CuO NPs

Low-Noise Multimodal Reconfigurable Sensor Readout Circuit for Voltage/Current/Resistive/Capacitive Microsensors

This paper presents a low-noise reconfigurable sensor readout circuit with a multimodal sensing chain for voltage/current/resistive/capacitive microsensors such that it can interface with a voltage, current, resistive, or capacitive microsensor, and can be reconfigured for a specific sensor application. The multimodal sensor readout circuit consists of a reconfigurable amplifier, programmable gain amplifier (PGA), low-pass filter (LPF), and analog-to-digital converter (ADC). A chopper stabilization technique was implemented in a multi-path operational amplifier to mitigate 1/f noise and offsets. The 1/f noise and offsets were up-converted by a chopper circuit and caused an output ripple. An AC-coupled ripple rejection loop (RRL) was implemented to reduce the output ripple caused by the chopper. When the amplifier was operated in the discrete-time mode, for example, the capacitive-sensing mode, a correlated double sampling (CDS) scheme reduced the low-frequency noise. The readout circuit was designed to use the 0.18-µm complementary metal-oxide-semiconductor (CMOS) process with an active area of 9.61 mm2. The total power consumption was 2.552 mW with a 1.8-V supply voltage. The measured input referred noise in the voltage-sensing mode was 5.25 µVrms from 1 Hz to 200 Hz