On Borrowed Time -- Preventing Static Power Side-Channel Analysis

In recent years, static power side-channel analysis attacks have emerged as a serious threat to cryptographic implementations, overcoming state-of-the-art countermeasures against side-channel attacks. The continued down-scaling of semiconductor process technology, which results in an increase of the relative weight of static power in the total power budget of circuits, will only improve the viability of static power side-channel analysis attacks. Yet, despite the threat posed, limited work has been invested into mitigating this class of attack. In this work we address this gap. We observe that static power side-channel analysis relies on stopping the target circuit's clock over a prolonged period, during which the circuit holds secret information in its registers. We propose Borrowed Time, a countermeasure that hinders an attacker's ability to leverage such clock control. Borrowed Time detects a stopped clock and triggers a reset that wipes any registers containing sensitive intermediates, whose leakages would otherwise be exploitable. We demonstrate the effectiveness of our countermeasure by performing practical Correlation Power Analysis attacks under optimal conditions against an AES implementation on an FPGA target with and without our countermeasure in place. In the unprotected case, we can recover the entire secret key using traces from 1,500 encryptions. Under the same conditions, the protected implementation successfully prevents key recovery even with traces from 1,000,000 encryptions

Autonomous Decision-Making Schemes for Real-World Applications in Supply Chains and Online Systems

Designing hand-engineered solutions for decision-making in complex environments is a challenging task. This dissertation investigates the possibility of having autonomous decision-makers in several real-world problems, e.g., in dynamic matching, marketing, and transportation. Achieving high-quality performance in these systems is strongly tied to the actions that a controller performs in different situations. This problem is further complicated by the fact that every single action might have long-term consequences, so ignoring them might cause unpredicted outcomes. My primary focus is to approach these problems with long-term objectives in mind, instead of only focusing on myopic ones. By borrowing techniques from optimal control and reinforcement learning, I design modeling infrastructures for each specific problem. Currently, the mainstream of reinforcement learning research uses games and robotics simulators for verification of the performance of an algorithm. In contrast, my main endeavor in this dissertation is to bridge the gap between the developed methods and their real-world applications, which are studied less often. For instance, for dynamic matching, I propose a simple matching rule with optimality guarantees; for customer journey, I use reinforcement learning to design an online algorithm based on temporal difference learning; and, for transportation, I showed that it is possible to train a solver with the capability of solving a wide variety of vehicle routing problems using reinforcement learning. Finally, I conclude this dissertation by introducing a new paradigm, which I call corrective reinforcement learning. This paradigm addressed one major challenge in applying policies found by RL, that is, they might significantly differ from real systems. I propose a mechanism that resolves this issue by finding improved controllers which are close to the status quo. I believe that the models proposed in this dissertation will contribute to the discovery of methods that can outperform current systems, which are primarily controlled by humans

ENERGY EFFICIENT WIRED NETWORKING

This research proposes a new dynamic energy management framework for a backbone Internet Protocol over Dense Wavelength Division Multiplexing (IP over DWDM) network. Maintaining the logical IP-layer topology is a key constraint of our architecture whilst saving energy by infrastructure sleeping and virtual router migration. The traffic demand in a Tier 2/3 network typically has a regular diurnal pattern based on people‟s activities, which is high in working hours and much lighter during hours associated with sleep. When the traffic demand is light, virtual router instances can be consolidated to a smaller set of physical platforms and the unneeded physical platforms can be put to sleep to save energy. As the traffic demand increases the sleeping physical platforms can be re-awoken in order to host virtual router instances and so maintain quality of service. Since the IP-layer topology remains unchanged throughout virtual router migration in our framework, there is no network disruption or discontinuities when the physical platforms enter or leave hibernation. However, this migration places extra demands on the optical layer as additional connections are needed to preserve the logical IP-layer topology whilst forwarding traffic to the new virtual router location. Consequently, dynamic optical connection management is needed for the new framework. Two important issues are considered in the framework, i.e. when to trigger the virtual router migration and where to move virtual router instances to? For the first issue, a reactive mechanism is used to trigger the virtual router migration by monitoring the network state. Then, a new evolutionary-based algorithm called VRM_MOEA is proposed for solving the destination physical platform selection problem, which chooses the appropriate location of virtual router instances as traffic demand varies. A novel hybrid simulation platform is developed to measure the performance of new framework, which is able to capture the functionality of the optical layer, the IP layer data-path and the IP/optical control plane. Simulation results show that the performance of network energy saving depends on many factors, such as network topology, quiet and busy thresholds, and traffic load; however, savings of around 30% are possible with typical medium-sized network topologies