Enhancing Electromagnetic Side-Channel Analysis in an Operational Environment

Side-channel attacks exploit the unintentional emissions from cryptographic devices to determine the secret encryption key. This research identifies methods to make attacks demonstrated in an academic environment more operationally relevant. Algebraic cryptanalysis is used to reconcile redundant information extracted from side-channel attacks on the AES key schedule. A novel thresholding technique is used to select key byte guesses for a satisfiability solver resulting in a 97.5% success rate despite failing for 100% of attacks using standard methods. Two techniques are developed to compensate for differences in emissions from training and test devices dramatically improving the effectiveness of cross device template attacks. Mean and variance normalization improves same part number attack success rates from 65.1% to 100%, and increases the number of locations an attack can be performed by 226%. When normalization is combined with a novel technique to identify and filter signals in collected traces not related to the encryption operation, the number of traces required to perform a successful attack is reduced by 85.8% on average. Finally, software-defined radios are shown to be an effective low-cost method for collecting side-channel emissions in real-time, eliminating the need to modify or profile the target encryption device to gain precise timing information

Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest Classifiers

Radio Frequency (RF) emissions from electronic devices expose security vulnerabilities that can be used by an attacker to extract otherwise unobtainable information. Two realms of study were investigated here, including the exploitation of 1) unintentional RF emissions in the field of Side Channel Analysis (SCA), and 2) intentional RF emissions from physical devices in the field of RF-Distinct Native Attribute (RF-DNA) fingerprinting. Statistical analysis on the linear model fit to measured SCA data in Linear Regression Attacks (LRA) improved performance, achieving 98% success rate for AES key-byte identification from unintentional emissions. However, the presence of non-Gaussian noise required the use of a non-parametric classifier to further improve key guessing attacks. RndF based profiling attacks were successful in very high dimensional data sets, correctly guessing all 16 bytes of the AES key with a 50,000 variable dataset. With variable reduction, Random Forest still outperformed Template Attack for this data set, requiring fewer traces and achieving higher success rates with lower misclassification rate. Finally, the use of a RndF classifier is examined for intentional RF emissions from ZigBee devices to enhance security using RF-DNA fingerprinting. RndF outperformed parametric MDA/ML and non-parametric GRLVQI classifiers, providing up to GS =18.0 dB improvement (reduction in required SNR). Network penetration, measured using rogue ZigBee devices, show that the RndF method improved rogue rejection in noisier environments - gains of up to GS =18.0 dB are realized over previous methods

A multi-threading software countermeasure to mitigate side channel analysis in the time domain

This research is the first of its kind to investigate the utilisation of a multi-threading software-based countermeasure to mitigate Side Channel Analysis (SCA) attacks, with a particular focus on the AES-128 cryptographic algorithm. This investigation is novel, as there has not been a software-based countermeasure relying on multi-threading to our knowledge. The research has been tested on the Atmel microcontrollers, as well as a more fully featured system in the form of the popular Raspberry Pi that utilises the ARM7 processor. The main contributions of this research is the introduction of a multi-threading software based countermeasure used to mitigate SCA attacks on both an embedded device and a Raspberry Pi. These threads are comprised of various mathematical operations which are utilised to generate electromagnetic (EM) noise resulting in the obfuscation of the execution of the AES-128 algorithm. A novel EM noise generator known as the FRIES noise generator is implemented to obfuscate data captured in the EM field. FRIES comprises of hiding the execution of AES-128 algorithm within the EM noise generated by the 512 Secure Hash Algorithm (SHA) from the libcrypto++ and OpenSSL libraries. In order to evaluate the proposed countermeasure, a novel attack methodology was developed where the entire secret AES-128 encryption key was recovered from a Raspberry Pi, which has not been achieved before. The FRIES noise generator was pitted against this new attack vector and other known noise generators. The results exhibited that the FRIES noise generator withstood this attack whilst other existing techniques still leaked out secret information. The visual location of the AES-128 encryption algorithm in the EM spectrum and key recovery was prevented. These results demonstrated that the proposed multi-threading software based countermeasure was able to be resistant to existing and new forms of attacks, thus verifying that a multi-threading software based countermeasure can serve to mitigate SCA attacks

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers