This paper presents a practical differential fault analysis method for the faulty Advanced Encryption Standard (AES) with a reduced round by means of a semi-invasive fault injection. To verify our proposal, we implement the AES software on the ATmega128 microcontroller as recommended in the standard document FIPS 197. We reduce the number of rounds using a laser beam injection in the experiment. To deduce the initial round key, we perform an exhaustive search for possible key bytes associated with faulty ciphertexts. Based on the simulation result, our proposal extracts the AES 128-bit secret key in less than 10 hours with 10 pairs of plaintext and faulty ciphertext.

With the development of 5G and network technology, the usage of IoT devices has become popular. Because most of these IoT devices can be controlled by an adversary away from the administrator, several security issues such as firmware dumping can arise. Firmware dumping is the cornerstone or goal of many types of hardware hacking. Therefore, many IoT device manufacturers adopt some protection mechanisms such as the restriction of hardware debuggers. However, several recent studies have shown that the operating instructions of an IoT device can be recovered through the profiling-based side-channel analysis. The Side-Channel-Based Disassembler (SCBD) refers to software that recovers instructions of the device only from the side-channel signal. The SCBD is powerful enough to defeat many firmware protection mechanisms. In this paper, we show how an adversary can build an instruction (opcode)-level disassembler using the power consumption signal of commercial microcontrollers (MCUs) such as the 8-bit ATxmega128 and 32-bit STM32F0. To implement the SCBD, we elaborately constructed the instruction template considering the pipeline of the target MCUs through instruction sequence analysis. Furthermore, we preprocessed the side-channel signals using the Continuous Wavelet Transform (CWT) for noise reduction and Kullback-Leibler Divergence (KLD) for instruction feature extraction. Our experimental results show that the machine-learning-based instruction disassembling models can recover the operating instructions with an accuracy of about 91.9% and 98.6% for the ATxmega128 and STM32F0, respectively. Furthermore, we achieved an accuracy of 77% and 96.5% in a cross-board validation.

Because two types of side-channel attacks, namely passive information leakages and active fault injections, are considered separate implementation threats to cryptographic modules, most countermeasures against these attacks have been independently developed. However, Amiel et al. demonstrated that a fault injection combined with a simple power analysis (SPA) can break such a classical Rivest, Shamir, and Adelman (RSA) system implementation. In this paper, we show that this combined attack (CA) can be applied to the Boscher, Naciri, and Prouff algorithm, which is an SPA/fault attack (FA)-resistant exponentiation method for RSA implementation. Furthermore, this paper proposes a novel exponentiation algorithm resistant to power analysis and an FA as well as to the CA. The proposed exponentiation algorithm can be employed for secure Chinese remainder theorem-RSA implementation. In addition, the paper presents some experimental results of an SPA under the assumption of a successful fault injection.

Object detection systems are used in various fields such as autonomous vehicles and facial recognition. In particular, object detection using deep learning networks enables real-time processing in low-performance edge devices and can maintain high detection rates. However, edge devices that operate far from administrators are vulnerable to various physical attacks by malicious adversaries. In this paper, we implement a function for detecting traffic signs by using You Only Look Once (YOLO) as well as Faster-RCNN, which can be adopted by edge devices of autonomous vehicles. Then, assuming the role of a malicious attacker, we executed adversarial patch attacks with Adv-Patch and Dpatch. Trying to cause misdetection of traffic stop signs by using Adv-Patch and Dpatch, we confirmed the attacks can succeed with a high probability. To defeat these attacks, we propose an image reconstruction method using an autoencoder and the Structural Similarity Index Measure (SSIM). We confirm that the proposed method can sufficiently defend against an attack, attaining a mean Average Precision (mAP) of 91.46% even when two adversarial attacks are launched.