Power Side Channels in Security ICs: Hardware Countermeasures

Power side-channel attacks are a very effective cryptanalysis technique that can infer secret keys of security ICs by monitoring the power consumption. Since the emergence of practical attacks in the late 90s, they have been a major threat to many cryptographic-equipped devices including smart cards, encrypted FPGA designs, and mobile phones. Designers and manufacturers of cryptographic devices have in response developed various countermeasures for protection. Attacking methods have also evolved to counteract resistant implementations. This paper reviews foundational power analysis attack techniques and examines a variety of hardware design mitigations. The aim is to highlight exposed vulnerabilities in hardware-based countermeasures for future more secure implementations

Mode-locked dysprosium fiber laser: picosecond pulse generation from 2.97 to 3.30 {\mu}m

Mode-locked fiber laser technology to date has been limited to sub-3 {\mu}m wavelengths, despite significant application-driven demand for compact picosecond and femtosecond pulse sources at longer wavelengths. Erbium- and holmium-doped fluoride fiber lasers incorporating a saturable absorber are emerging as promising pulse sources for 2.7--2.9 {\mu}m, yet it remains a major challenge to extend this coverage. Here, we propose a new approach using dysprosium-doped fiber with frequency shifted feedback (FSF). Using a simple linear cavity with an acousto-optic tunable filter, we generate 33 ps pulses with up to 2.7 nJ energy and 330 nm tunability from 2.97 to 3.30 {\mu}m (3000--3400 cm^-1)---the first mode-locked fiber laser to cover this spectral region and the most broadly tunable pulsed fiber laser to date. Numerical simulations show excellent agreement with experiments and also offer new insights into the underlying dynamics of FSF pulse generation. This highlights the remarkable potential of both dysprosium as a gain material and FSF for versatile pulse generation, opening new opportunities for mid-IR laser development and practical applications outside the laboratory.Comment: Accepted for APL Photonics, 22nd August 201

What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

Phase transitions in single neurons and neural populations: Critical slowing, anesthesia, and sleep cycles

The firing of an action potential by a biological neuron represents a dramatic transition from small-scale linear stochastics (subthreshold voltage fluctuations) to gross-scale nonlinear dynamics (birth of a 1-ms voltage spike). In populations of neurons we see similar, but slower, switch-like there-and-back transitions between low-firing background states and high-firing activated states. These state transitions are controlled by varying levels of input current (single neuron), varying amounts of GABAergic drug (anesthesia), or varying concentrations of neuromodulators and neurotransmitters (natural sleep), and all occur within a milieu of unrelenting biological noise. By tracking the altering responsiveness of the excitable membrane to noisy stimulus, we can infer how close the neuronal system (single unit or entire population) is to switching threshold. We can quantify this “nearness to switching” in terms of the altering eigenvalue structure: the dominant eigenvalue approaches zero, leading to a growth in correlated, low-frequency power, with exaggerated responsiveness to small perturbations, the responses becoming larger and slower as the neural population approaches its critical point–-this is critical slowing. In this chapter we discuss phase-transition predictions for both single-neuron and neural-population models, comparing theory with laboratory and clinical measurement