On the security of embedded systems against side-channel attacks

Side-Channel Analysis (SCA) represents a serious threat to the security of millions of smart devices that form part of the so-called Internet of Things (IoT). On the other hand, perform the "right- fitting" cryptographic code for the IoT is a highly challenging task due to the reduced resource constraints of must of the IoT devices and the variety of cryptographic algorithms on disposal. An important criterion to assess the suitability of a light-weight cipher implementation, with respect to the SCA point of view, is the amount of energy leakage available to an adversary. In this thesis, the efficiency of a selected function that is commonly used in AES implementations in the perspective of Correlation Power Analysis (CPA) attacks are analyzed, leading to focus on the very common situation where the exact time of the sensitive processing is drowned in a large number of leakage points. In the particular case of statistical attacks, much of the existing literature essentially develop the theory under the assumption that the exact sensitive time is known and cannot be directly applied when the latter assumption is relaxed, being such a particular aspect for the simple Differential Power Analysis (DPA) in contrast with the CPA. To deal with this issue, an improvement that makes the statistical attack a real alternative compared with the simple DPA has been proposed. For the power consumption model (Hamming Weight model), and by rewriting the simple DPA attacks in terms of correlation coefficients between Boolean functions. Exhibiting properties of S-boxes relied on CPA attacks and showing that these properties are opposite to the non-linearity criterion and to the propagation criterion assumed for the former DPA. In order to achieve this goal, the study has been illustrated by various attack experiments performed on several copies implementations of the light-weight AES chipper in a well-known micro-controller educative platform within an 8-bit processor architecture deployed on a 350 nanometers CMOS technology. The Side-channel attacks presented in this work have been set in ideal conditions to capture the full complexity of an attack performed in real-world conditions, showing that certain implementation aspects can influence the leakage levels. On the other side, practical improvements are proposed for specific contexts by exploring the relationship between the non-linearity of the studied selection function and the measured leakages, with the only pretension to bridge the gap between the theory and the practice. The results point to new enlightenment on the resilience of basic operations executed by common light-weight ciphers implementations against CPA attacks

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

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Barrel Shifter Physical Unclonable Function Based Encryption

Physical Unclonable Functions (PUFs) are circuits designed to extract physical randomness from the underlying circuit. This randomness depends on the manufacturing process. It differs for each device enabling chip-level authentication and key generation applications. We present a protocol utilizing a PUF for secure data transmission. Parties each have a PUF used for encryption and decryption; this is facilitated by constraining the PUF to be commutative. This framework is evaluated with a primitive permutation network - a barrel shifter. Physical randomness is derived from the delay of different shift paths. Barrel shifter (BS) PUF captures the delay of different shift paths. This delay is entangled with message bits before they are sent across an insecure channel. BS-PUF is implemented using transmission gates; their characteristics ensure same-chip reproducibility, a necessary property of PUFs. Post-layout simulations of a common centroid layout 8-level barrel shifter in 0.13 {\mu}m technology assess uniqueness, stability and randomness properties. BS-PUFs pass all selected NIST statistical randomness tests. Stability similar to Ring Oscillator (RO) PUFs under environment variation is shown. Logistic regression of 100,000 plaintext-ciphertext pairs (PCPs) failed to successfully model BS- PUF behavior