The uncertainty of Side-Channel Analysis: A way to leverage from heuristics

Performing a comprehensive side-channel analysis evaluation of small embedded devices is a process known for its variability and complexity. In real-world experimental setups, the results are largely influenced by a huge amount of parameters that are not easily adjusted without trial and error and are heavily relying on the experience of professional security analysts. In this paper, we advocate the use of an existing statistical methodology called Six Sigma (6{\sigma}) for side-channel analysis optimization for this purpose. This well-known methodology is commonly used in other industrial fields, such as production and quality engineering, to reduce the variability of industrial processes. We propose a customized Six Sigma methodology, which enables even a less-experienced security analysis to select optimal values for the different variables that are critical for the side-channel analysis procedure. Moreover, we show how our methodology helps in improving different phases in the side-channel analysis process.Comment: 30 pages, 8 figure

Auto-tune POIs: Estimation of distribution algorithms for efficient side-channel analysis

Due to the constant increase and versatility of IoT devices that should keep sensitive information private, Side-Channel Analysis (SCA) attacks on embedded devices are gaining visibility in the industrial field. The integration and validation of countermeasures against SCA can be an expensive and cumbersome process, especially for the less experienced ones, and current certification procedures require to attack the devices under test using multiple SCA techniques and attack vectors, often implying a high degree of complexity. The goal of this paper is to ease one of the most crucial and tedious steps of profiling attacks i.e. the points of interest (POI) selection and hence assist the SCA evaluation process. To this end, we introduce the usage of Estimation of Distribution Algorithms (EDAs) in the SCA field in order to automatically tune the point of interest selection. We showcase our approach on several experimental use cases, including attacks on unprotected and protected AES implementations over distinct copies of the same device, dismissing in this way the portability issue

Toward Practical Autoencoder-based Side-Channel Analysis Evaluations

This paper introduces a practical evaluation procedure based on autoencoders for profiled side-channel analysis evaluations. An autoencoder is a learning model able to pre-process leakage traces improving in this way the guessing entropy. Nevertheless, this learning model\u27s design should aim to code the leakage distribution to avoid relevant information being removed. For this reason, we propose an autoencoder built upon dilated convolutions. When using these learning models, the evaluation produces new assets, e.g., new versions of the dataset and new models based on learning algorithms. Our procedure comprises meaningful metrics and visualization techniques, namely signal-to-noise ratio and weight visualization, to evaluate those assets\u27 effectiveness. After applying our procedure and our new autoencoder architecture to the ASCAD random key database, our results outperform state-of-the-art

Towards Human Dependency Elimination: AI Approach to SCA Robustness Assessment

Evaluating the side-channel resistance of a device in practice is a problematic and arduous process. Current certification schemes require to attack the device under test with an ever-growing number of techniques to validate its security. In addition, the success or failure of these techniques strongly depends on the individual implementing them, due to the fallible and human intrinsic nature of several steps of this path. To alleviate this problem, we propose a battery of automated attacks as a side-channel analysis robustness assessment of an embedded device. To prove our approach, we conduct realistic experiments on two different devices, creating a new dataset (AES_RA) as a part of our contribution. Furthermore, we propose a novel way of performing these attacks using Principal Component Analysis, which also serves as an alternative way of selecting optimal principal components automatically. In addition, we perform a detailed analysis of automated attacks against masked AES implementations, comparing our method with the state-of-the-art approaches and proposing two novel initialization techniques to overcome its limitations in this scenario. We support our claims with experiments on AES_RA and a public dataset (ASCAD), showing how our, although fully automated, approach can straightforwardly provide state-of-the-art results

Reliable and High QoS Wireless Communications over Harsh Environments, Journal of Telecommunications and Information Technology, 2013, nr 1

One of the most challenging research fields in which research community has taken a very active role is focused on trying to bring the features of wireless networks into line with the traditional wired solutions. Given the noisy and lossy nature of the wireless medium, it is more difficult to provide a comparable Quality of Service (QoS) and Reliability over wireless networks. This lack of reliability avoids the use of wireless solution in scenarios under harsh environment and mission-critical applications. In this paper we propose an inter-node collaborative schema with the aim of improving the achievable QoS level for multicast streaming, through the use of Network Coding and the algebra it is based on. We also present an implementation of the described algorithm on the OPNET discrete event simulation tool. Experimental results highlighting the performance achieved by the proposed algorithm and its improved efficiency as compared to other solutions are described

Toward a New Generation of Bio-Scaffolds for Neural Tissue Engineering: Challenges and Perspectives

Neural tissue engineering presents a compelling technological breakthrough in restoring brain function, holding immense promise. However, the quest to develop implantable scaffolds for neural culture that fulfill all necessary criteria poses a remarkable challenge for material science. These materials must possess a host of desirable characteristics, including support for cellular survival, proliferation, and neuronal migration and the minimization of inflammatory responses. Moreover, they should facilitate electrochemical cell communication, display mechanical properties akin to the brain, emulate the intricate architecture of the extracellular matrix, and ideally allow the controlled release of substances. This comprehensive review delves into the primary requisites, limitations, and prospective avenues for scaffold design in brain tissue engineering. By offering a panoramic overview, our work aims to serve as an essential resource, guiding the creation of materials endowed with bio-mimetic properties, ultimately revolutionizing the treatment of neurological disorders by developing brain-implantable scaffolds