Investigating Machine Learning Techniques for Gesture Recognition with Low-Cost Capacitive Sensing Arrays

Machine learning has proven to be an effective tool for forming models to make predictions based on sample data. Supervised learning, a subset of machine learning, can be used to map input data to output labels based on pre-existing paired data. Datasets for machine learning can be created from many different sources and vary in complexity, with popular datasets including the MNIST handwritten dataset and CIFAR10 image dataset. The focus of this thesis is to test and validate multiple machine learning models for accurately classifying gestures performed on a low-cost capacitive sensing array. Multiple neural networks are trained using gesture datasets obtained from the capacitance board. In this paper, I train and compare different machine learning models on recognizing gesture datasets. Learning hyperparameters are also adjusted for results. Two datasets are used for the training: one containing simple gestures and another containing more complicated gestures. Accuracy and loss for the models are calculated and compared to determine which models excel at recognizing performed gestures

When Frodo Flips: End-to-End Key Recovery on FrodoKEM via Rowhammer

In this work, we recover the private key material of the FrodoKEM key exchange mechanism as submitted to the NIST Post Quantum Cryptography (PQC) standardization process. The new mechanism that allows for this is a Rowhammer-assisted \emph{poisoning} of the FrodoKEM Key Generation (KeyGen) process. The Rowhammer side-channel is a hardware-based security exploit that allows flipping bits in DRAM by “hammering” rows of memory adjacent to some target-victim memory location by repeated memory accesses. Using Rowhammer, we induce the FrodoKEM software to output a higher-error Public Key (PK), $(\mathbf{A}, \mathbf{B} = \mathbf{A}\mathbf{S}+\mathbf{\widetilde{E}}),$ where the error $\widetilde{\mathbf{E}}$ is modified by Rowhammer. Then, we perform a decryption failure attack, using a variety of publicly-accessible supercomputing resources running on the order of only 200,000 core-hours. We delicately attenuate the decryption failure rate to ensure that the adversary\u27s attack succeeds practically, but so honest users cannot easily detect the manipulation. Achieving this public key poisoning requires an extreme engineering effort, as FrodoKEM\u27s KeyGen runs on the order of 8 milliseconds. (Prior Rowhammer-assisted attacks against cryptography require as long as 8 hours of persistent access.) In order to handle this real-world timing condition, we require a wide variety of prior and brand new, low-level engineering techniques, including e.g. memory massaging algorithms -- i.e. Feng Shui -- and a precisely-targeted performance degradation attack on the extendable output function SHAKE. We explore the applicability of our techniques to other lattice-based KEMs in the NIST PQC Round 3 candidate-pool, e.g. Kyber, Saber, etc, as well as the difficulties that arise in the various settings. To conclude, we discuss various simple countermeasures to protect implementations against this, and similar, attacks