Selective deep convolutional neural network for low cost distorted image classification

Neural networks trained using images with a certain type of distortion should be better at classifying test images with the same type of distortion than generally-trained neural networks, given other factors being equal. Based on this observation, an ensemble of convolutional neural networks (CNNs) trained with different types and degrees of distortions is used. However, instead of simply classifying test images of unknown distortion types with the entire ensemble of CNNs, an extra tiny CNN is specifically trained to distinguish between the different types and degrees of distortions. Then, only the dedicated CNN for that specific type and degree of distortion, as determined by the tiny CNN, is activated and used to classify a possibly distorted test image. This proposed architecture, referred to as a \textit{selective deep convolutional neural network (DCNN)}, is implemented and found to result in high accuracy with low hardware costs. Detailed simulations with realistic image distortion scenarios using three popular datasets show that memory, MAC operations, and energy savings of up to 93.68%, 93.61%, and 91.92%, respectively, can be achieved with almost no reduction in image classification accuracy. The proposed selective DCNN scores up to 2.18x higher than the state-of-the-art DCNN model when evaluated using NetScore, a comprehensive metric that considers both CNN performance and hardware cost. In addition, it is shown that even higher hardware cost reduction can be achieved when selective DCNN is combined with previously proposed model compression techniques. Finally, experiments conducted with extended types and degrees of image distortion show that selective DCNN is highly scalable.11Ysciescopu

A New Exponentiation Algorithm Resistant to Combined Side Channel Attack

Abstract Since two different types of side channel attacks based on passive information leakage and active fault injection are independently considered as implementation threats on cryptographic modules, most countermeasures have been separately developed according to each attack type. But then, Amiel et al. proposed a combined side channel attack in which an attacker combines these two methods to recover the secret key in an RSA implementation. In this paper, we show that the BNP (Boscher, Naciri, and Prouff) algorithm for RSA, which is an SPA/FA-resistant exponentiation method, is also vulnerable to the combined attack. In addition, we propose a new exponentiation algorithm resistant to power analysis and fault attack as well as the combined attack. The proposed secure exponentiation algorithm can be employed to strengthen the security of CRT-RSA

Implementation of Disassembler on Microcontroller Using Side-Channel Power Consumption Leakage

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

An Improved and Efficient Countermeasure against Power Analysis Attacks

Recently new types of differential power analysis attacks (DPA) against elliptic curve cryptosystems (ECC) and RSA systems have been introduced. Most existing countermeasures against classical DPA attacks are vulnerable to these new DPA attacks which include refined power analysis attacks (RPA), zero-value point attacks (ZPA), and doubling attacks. The new attacks are different from classical DPA in that RPA uses a special point with a zero-value coordinate, while ZPA uses auxiliary registers to locate a zero value. So, Mamiya et al proposed a new countermeasure against RPA, ZPA, classical DPA and SPA attacks using a basic random initial point. His countermeasure works well when applied to ECC, but it has some disadvantages when applied to general exponentiation algorithms (such as RSA and ElGamal) due to an inverse computation. This paper presents an efficient and improved countermeasure against the above new DPA attacks by using a random blinding concept on the message different from Mamiya's countermeasure and show that our proposed countermeasure is secure against SPA based Yen's power analysis which can break Coron's simple SPA countermeasure as well as Mamiya's one. The computational cost of the proposed scheme is very low when compared to the previous methods which rely on Coron's simple SPA countermeasure. Moreover this scheme is a generalized countermeasure which can be applied to ECC as well as RSA system

An Improved and Efficient Countermeasure against Power Analysis Attacks

Abstract. Recently new types of differential power analysis attacks (DPA) against elliptic curve cryptosystems (ECC) and RSA systems have been introduced. Most existing countermeasures against classical DPA attacks are vulnerable to these new DPA attacks which include refined power analysis attacks (RPA), zero-value point attacks (ZPA), and doubling attacks. The new attacks are different from classical DPA in that RPA uses a special point with a zero-value coordinate, while ZPA uses auxiliary registers to locate a zero value. So, Mamiya et al proposed a new countermeasure against RPA, ZPA, classical DPA and SPA attacks using a basic random initial point. His countermeasure works well when applied to ECC, but it has some disadvantages when applied to general exponentiation algorithms (such as RSA and ElGamal) due to an inverse computation. This paper presents an efficient and improved countermeasure against the above new DPA attacks by using a random blinding concept on the message different from Mamiya’s countermeasure and show that our proposed countermeasure is secure against SPA based Yen’s power analysis which can break Coron’s simple SPA countermeasure as well as Mamiya’s one. The computational cost of the proposed scheme is very low when compared to the previous methods which rely on Coron’s simple SPA countermeasure. Moreover this scheme is a generalized countermeasure which can be applied to ECC as well as RSA system