Profiling side-channel attacks on cryptographic algorithms

Traditionally, attacks on cryptographic algorithms looked for mathematical weaknesses in the underlying structure of a cipher. Side-channel attacks, however, look to extract secret key information based on the leakage from the device on which the cipher is implemented, be it smart-card, microprocessor, dedicated hardware or personal computer. Attacks based on the power consumption, electromagnetic emanations and execution time have all been practically demonstrated on a range of devices to reveal partial secret-key information from which the full key can be reconstructed. The focus of this thesis is power analysis, more specifically a class of attacks known as profiling attacks. These attacks assume a potential attacker has access to, or can control, an identical device to that which is under attack, which allows him to profile the power consumption of operations or data flow during encryption. This assumes a stronger adversary than traditional non-profiling attacks such as differential or correlation power analysis, however the ability to model a device allows templates to be used post-profiling to extract key information from many different target devices using the power consumption of very few encryptions. This allows an adversary to overcome protocols intended to prevent secret key recovery by restricting the number of available traces. In this thesis a detailed investigation of template attacks is conducted, along with how the selection of various attack parameters practically affect the efficiency of the secret key recovery, as well as examining the underlying assumption of profiling attacks in that the power consumption of one device can be used to extract secret keys from another. Trace only attacks, where the corresponding plaintext or ciphertext data is unavailable, are then investigated against both symmetric and asymmetric algorithms with the goal of key recovery from a single trace. This allows an adversary to bypass many of the currently proposed countermeasures, particularly in the asymmetric domain. An investigation into machine-learning methods for side-channel analysis as an alternative to template or stochastic methods is also conducted, with support vector machines, logistic regression and neural networks investigated from a side-channel viewpoint. Both binary and multi-class classification attack scenarios are examined in order to explore the relative strengths of each algorithm. Finally these machine-learning based alternatives are empirically compared with template attacks, with their respective merits examined with regards to attack efficiency.

Molecular mechanisms underpinning IFN-gamma and TNF-alpha synergy in intestinal epithelial cells

Cytokine-driven signalling shapes immune homeostasis and guides inflammatory responses mainly through induction of specific gene expression programmes both within and outside the immune cell compartment. These transcriptional outputs are often amplified via cytokine synergy, which sets a stimulatory threshold that safeguards from exacerbated inflammation and immunopathology. In this study, we investigated the molecular mechanisms underpinning synergy between two pivotal Th1 cytokines, IFN-γ and TNF-α, in human intestinal epithelial cells. These two proinflammatory mediators induce a unique state of signalling and transcriptional synergy implicated in processes such as antiviral and antitumour immunity, intestinal barrier and pancreatic β-cell dysfunction. Since its discovery more than 30 years ago, this biological phenomenon remains, however, only partially defined. Here, using a functional genomics approach including RNAi perturbation screens and small-molecule inhibitors, we identified two new regulators of IFN-γ/TNF-α-induced chemokine and antiviral gene and protein expression, a Bcl-2 protein BCL-G and a histone demethylase UTX. We also discovered that IFN-γ/TNF-α synergise to trigger a coordinated shutdown of major receptor tyrosine kinases expression in colon cancer cells. Together, these findings extend our current understanding of how IFN-γ/TNF-α synergy elicits qualitatively and quantitatively distinct outputs in the intestinal epithelium. Given the well-documented role of this synergistic state in immunopathology of various disorders, our results may help to inform the identification of high quality and biologically relevant druggable targets for diseases characterised by an IFN-γ/TNF-α high immune signature