Correlation Power Analysis with Companding Methods

AbstractCompanding methods have been profoundly applied in signal processing for quantization. And various companding schemes have been proposed to improve the PAPR (Peak to Average Power Ratio) of OFDM systems. In this paper, based on the exploration of the features of μ-law functions, we propose Correlation Power Analysis (CPA) with μ-law companding methods. μ-law expanding function is used to preprocess the power traces collected during AES encryption on ASIC and FPGA respectively. Experiments show that it reduces the number of power traces to recover all the key bytes as much as 25.8% than the conventional CPA

Synchronization of spatiotemporal semiconductor lasers and its application in color image encryption

Optical chaos is a topic of current research characterized by high-dimensional nonlinearity which is attributed to the delay-induced dynamics, high bandwidth and easy modular implementation of optical feedback. In light of these facts, which adds enough confusion and diffusion properties for secure communications, we explore the synchronization phenomena in spatiotemporal semiconductor laser systems. The novel system is used in a two-phase colored image encryption process. The high-dimensional chaotic attractor generated by the system produces a completely randomized chaotic time series, which is ideal in the secure encoding of messages. The scheme thus illustrated is a two-phase encryption method, which provides sufficiently high confusion and diffusion properties of chaotic cryptosystem employed with unique data sets of processed chaotic sequences. In this novel method of cryptography, the chaotic phase masks are represented as images using the chaotic sequences as the elements of the image. The scheme drastically permutes the positions of the picture elements. The next additional layer of security further alters the statistical information of the original image to a great extent along the three-color planes. The intermediate results during encryption demonstrate the infeasibility for an unauthorized user to decipher the cipher image. Exhaustive statistical tests conducted validate that the scheme is robust against noise and resistant to common attacks due to the double shield of encryption and the infinite dimensionality of the relevant system of partial differential equations.Comment: 20 pages, 11 figures; Article in press, Optics Communications (2011

Recent progress in discriminating between coal cutting and rock cutting with adaptive signal processing techniques

"The report documents the current status of the U.S. Bureau of Mines ongoing investigation of the use of adaptive signal discrimination (ASD) systems to distinguish between cutting coal and cutting mine rock. Cutting tool forces and vibrations were measured in the laboratory using both conical bits and roller cutters in a linear cutting apparatus for several material samples and two cutting directions. A number of ASD systems consisting of one or more signal classifiers were trained and tested to study how data window size, type of signal feature, and combining (polling) of classifier results influence system performance. The results show that ASD system recognition rates can be improved by increasing data window size, removing air cutting portions from the signal data, overlapping data windows, and combining (fusing) information at various levels of ASD system operation." - NIOSHTIC-2NIOSH no. 10012169199

Automatic Test Framework Anomaly Detection in Home Routers

In a modern world most people have a home network and multiple devices behind it. These devices include simple IoT, that require external protection not to join a botnet. This protection can be granted by a security router with a feature of determining the usual network traffic of a device and alerting its unusual behaviour. This work is dedicated to creating a testbed to verify such router's work. The test bed includes tools to capture IoT traffic, edit and replay it. Created tool supports UDP, TCP, partially ICMP and is extendable to other protocols. UDP and TCP protocols are replayed using OS sockets at transport network layer. The methods described have proved to work on a real setup

PowerSpy: Location Tracking using Mobile Device Power Analysis

Modern mobile platforms like Android enable applications to read aggregate power usage on the phone. This information is considered harmless and reading it requires no user permission or notification. We show that by simply reading the phone's aggregate power consumption over a period of a few minutes an application can learn information about the user's location. Aggregate phone power consumption data is extremely noisy due to the multitude of components and applications that simultaneously consume power. Nevertheless, by using machine learning algorithms we are able to successfully infer the phone's location. We discuss several ways in which this privacy leak can be remedied.Comment: Usenix Security 201

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