The Advanced Encryption Standard is one of the most common encryption algorithms. It is highly resistant to mathematical and statistical attacks, however, this security is based on the assumption that an adversary cannot access the algorithm’s internal state during encryption or decryption. Power analysis is a type of side-channel analysis that exploit information leakage through the power consumption of physical realisations of cryptographic systems. Power analysis attacks capture intermediate results during AES execution, which combined with knowledge of the plaintext or the ciphertext can reveal key material. This thesis studies and compares simple power analysis, differential power analysis and template attacks using a cheap consumer oscilloscope against AES-128 implemented on an 8-bit microcontroller. Additionally, the shuffling and masking countermeasures are evaluated in terms of security and performance. The thesis also presents a practical approach to template building and device characterisation. The results show that attacking a naive implementation with differential power analysis requires little effort, both in preparation and computation time. Template attacks require the least amount of measurements but requires significant preparation. Simple power analysis by itself cannot break the key but proves helpful in simplifying the other attacks. It is found that shuffling significantly increases the number of traces required to break the key while masking forces the attacker to use higher-order techniques.

Over the last several years side-channel analysis has emerged as a major threat to securing sensitive information in cryptographic devices. Several side-channels have been discovered and used to break implementations of all major cryptographic algorithms (AES, DES, RSA). This thesis is focused on power analysis attacks. A variety of power analysis methods has been developed to perform these attacks. These methods include simple power analysis (SPA), differential power analysis (DPA), template attacks, etc. This work provides comprehensive survey of mentioned methods and also investigates the application of a machine learning techniques in power analysis. The considered learning techniques are neural networks and support vector machines. The final part of this thesis is dedicated to implemenation of the attack against protected software AES implementation which is used in the DPA Contest.