In present-day communications systems, cryptographic algorithms (ciphers) provide confidentiality and integrity of data through secret pieces of information (i.e. shared or private keys) known only to the communicating parties. However, as shown in numerous previous works, measuring the physical properties of hardware during executions of a cipher can reveal information about its current state. When sufficient information leaks through these so-called "side-channels", an adversary can compute the key. In this presentation, we will examine the EM side channel, which originates from electromagnetic radiation leaking from a device.

Performing EM side-channel attacks used to require rather expensive oscilloscopes with high sample rate ADCs. With the advent of inexpensive SDRs such as the RTL-SDR and advances in AI, the bar to perform such attacks has been adequately lowered. We will learn how to use the open-source ElectroMagnetic Mining Array (EMMA) tool to capture leakages emanated by an Arduino Duemilanove during the execution of an AES encryption operation. Next, a standard CEMA attack will be performed. This attack correlates the measured amplitude of a signal with the hamming weight of part of the key in order to determine which key was used during the execution of the cipher. Finally, we will examine applications of neural networks to side-channel analysis. Both traditional deep Convolutional Neural Networks (CNNs) as well as a novel "correlation optimization" (CO) method using shallow neural networks will be discussed.