fault injection
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Field programmable gate arrays (FPGAs) are used in large numbers in data centers around the world. They are used for cloud computing and computer networking. The most common type of FPGA used in data centers are re-programmable SRAM-based FPGAs. These devices offer potential performance and power consumption savings. A single device also carries a small susceptibility to radiation-induced soft errors, which can lead to unexpected behavior. This article examines the impact of terrestrial radiation on FPGAs in data centers. Results from artificial fault injection and accelerated radiation testing on several data-center-like FPGA applications are compared. A new fault injection scheme provides results that are more similar to radiation testing. Silent data corruption (SDC) is the most commonly observed failure mode followed by FPGA unavailable and host unresponsive. A hypothetical deployment of 100,000 FPGAs in Denver, Colorado, will experience upsets in configuration memory every half-hour on average and SDC failures every 0.5–11 days on average.

Fault injection attack (FIA) has become a serious threat to the confidentiality and fault tolerance of integrated circuits (ICs). Circuit designers need an effective method to evaluate the countermeasures of the IC designs against the FIAs at the design stage. To address the need, this article, based on FPGA emulation, proposes an in-circuit early evaluation framework, in which FIAs are emulated with parameterized fault models. To mimic FIAs, an efficient scan approach is proposed to inject faults at any time at any circuit nodes, while both the time and area overhead of fault injection are reduced. After the circuit design under test (CUT) is submitted to the framework, the scan chains insertion, fault generation, and fault injection are executed automatically, and the evaluation result of the CUT is generated, making the evaluation a transparent process to the designers. Based on the framework, the confidentiality and fault-tolerance evaluations are demonstrated with an information-based evaluation approach. Experiment results on a set of ISCAS89 benchmark circuits show that on average, our approach reduces the area overhead by 41.08% compared with the full scan approach and by over 20.00% compared with existing approaches. The confidentiality evaluation experiments on AES-128 and DES-56 and the fault-tolerance evaluation experiments on two CNN circuits, a RISC-V core, a Cordic core, and the float point arithmetic units show the effectiveness of the proposed framework.

Spiking Neural Networks (SNN) are fast emerging as an alternative option to Deep Neural Networks (DNN). They are computationally more powerful and provide higher energy-efficiency than DNNs. While exciting at first glance, SNNs contain security-sensitive assets (e.g., neuron threshold voltage) and vulnerabilities (e.g., sensitivity of classification accuracy to neuron threshold voltage change) that can be exploited by the adversaries. We explore global fault injection attacks using external power supply and laser-induced local power glitches on SNN designed using common analog neurons to corrupt critical training parameters such as spike amplitude and neuron’s membrane threshold potential. We also analyze the impact of power-based attacks on the SNN for digit classification task and observe a worst-case classification accuracy degradation of −85.65%. We explore the impact of various design parameters of SNN (e.g., learning rate, spike trace decay constant, and number of neurons) and identify design choices for robust implementation of SNN. We recover classification accuracy degradation by 30–47% for a subset of power-based attacks by modifying SNN training parameters such as learning rate, trace decay constant, and neurons per layer. We also propose hardware-level defenses, e.g., a robust current driver design that is immune to power-oriented attacks, improved circuit sizing of neuron components to reduce/recover the adversarial accuracy degradation at the cost of negligible area, and 25% power overhead. We also propose a dummy neuron-based detection of voltage fault injection at ∼1% power and area overhead each.

Abstract Cryptographic ICs on edge devices for internet-of-things (IoT) applications are exposed to an adversary and threatened by malicious side channel analysis. On-chip analog monitoring by sensor circuits embedded inside the chips is one of the possible countermeasures against such attacks. An on-chip monitor circuit consisting of a successive approximation register (SAR) analog-to-digital converter (ADC) and an input buffer acquires a wideband signal, which enables to detects an irregular noise due to an active fault injection and a passive side channel leakage analysis. In this paper, several countermeasures against security attacks utilizing wideband on-chip monitors are reviewed. Each technique is implemented on a prototype chip, and the measurement results prove they can effectively detect and diagnose the security attacks.

AbstractThis paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework using the Application-Level Fault Injection (ALFI) to investigate how the faults at the application level affect the scalability resilience and behaviour of cloud-based software services. Previous studies on scalability analysis of cloud-based software services provide a baseline of the scalability behaviour of such services, allowing to conduct in-depth scalability investigation of these services. Experimental analysis on the EC2 cloud using a real-world cloud-based software service is used to demonstrate the framework, considering delay latency of software faults with two varied settings and two demand scenarios. The experimental approach is explained in detail. Here we simulate delay latency injection with two different times, 800 and 1600 ms, and compare the results with the baseline data. The results show that the proposed approach allows a fair assessment of the fault scenario’s impact on the cloud software service’s scalability resilience. We explain the use of the methodology to determine the impact of injected faults on the scalability behaviour and resilience of cloud-based software services.

Fault attacks are traditionally considered under a threat model that assumes the device under test is in the possession of the attacker. We propose a variation on this model. In our model, the attacker integrates a fault injection circuit into a malicious field-replaceable unit, or FRU, which is later placed by the victim in close proximity to their own device. Examples of devices which incorporate FRUs include interface cards in routers, touch screens and sensor assemblies in mobile phones, ink cartridges in printers, batteries in health sensors, and so on. FRUs are often installed by after-market repair technicians without properly verifying their authenticity, and previous works have shown they can be used as vectors for various attacks on the privacy and integrity of smart devices. We design and implement a low-cost fault injection circuit suitable for placement inside a malicious FRU, and show how it can be used to practically extract secrets from a privileged system process through a combined hardware-software approach, even if the attacker software application only has user-level permissions. Our prototype produces highly effective and repeatable attacks, despite its cost being several orders of magnitude less than that of commonly used fault injection analysis lab setups. This threat model allows fault attacks to be carried out remotely, even if the device under test is in the hands of the victim. Considered together with recent advances in software-only fault attacks, we argue that resistance to fault attacks should be built into additional classes of devices.

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.

Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to radiation-induced single-event effects (SEEs) that can degrade the dependability of DL applications. In this article, we propose Reconfigurable ConvNet (RECON), a reconfigurable acceleration framework for dependable, high-performance semantic segmentation for space applications. In RECON, we propose both selective and adaptive approaches to enable efficient SEE mitigation. In our selective approach, control-flow parts are selectively protected by triple-modular redundancy to minimize SEE-induced hangs, and in our adaptive approach, partial reconfiguration is used to adapt the mitigation of dataflow parts in response to a dynamic radiation environment. Combined, both approaches enable RECON to maximize system performability subject to mission availability constraints. We perform fault injection and neutron irradiation to observe the susceptibility of RECON and use dependability modeling to evaluate RECON in various orbital case studies to demonstrate a 1.5–3.0× performability improvement in both performance and energy efficiency compared to static approaches.

The continuous scaling of electronic components has led to the development of high-performance microprocessors which are even suitable for safety-critical applications where radiation-induced errors, such as single event effects (SEEs), are one of the most important reliability issues. This work focuses on the development of a fault injection environment capable of analyzing the impact of errors on the functionality of an ARM Cortex-A9 microprocessor embedded within a Zynq-7000 AP-SoC, considering different fault models affecting both the system memory and register resources of the embedded processor. We developed a novel Python-based fault injection platform for the emulation of radiation-induced faults within the AP-SoC hardware resources during the execution of software applications. The fault injection approach is not intrusive, and it does not require modifying the software application under evaluation. The experimental analyses have been performed on a subset of the MiBench benchmark software suite. Fault injection results demonstrate the capability of the developed method and the possibility of evaluating various sets of fault models.