Approximate computing techniques that trade accuracy for better computing performance and energy efficiency have been widely used in many computation-intensive applications. As reported in the recent literature, approximate computing systems are prone to stealthy attacks that exploit approximation mechanisms to disguise malicious behaviors in normal operations. The analysis performed in this work indicates that the primary outputs of applications with approximate components are not the best location to detect the presence of attacks. This work proposes an intermediate node evaluation-based attack detection (INEAD) method to distinguish whether the cause of inaccuracy in applications is due to approximation or attacks. The attack detection rate of the proposed method was examined in two applications: 1) an approximate fast Fourier transform (FFT) and 2) an artificial neural network (ANN) using approximate arithmetic modules. The case study of an approximate FFT shows that the INEAD method improves the attack detection rate by 61.6% and reduces the false positive rate by up to 91% over the existing work that detects attacks at the primary output stage. The case study of an approximate ANN shows that the INEAD method outperforms the baseline by 93.6% in terms of attack detection rate.

Approximate computing (AC) techniques have been leveraged to improve computing performance and energy efficiency with minor degradation in accuracy. Recent literature indicates that some AC mechanisms could be exploited by attackers to implement new attack surfaces. To address the emerging attacks in AC systems, we propose to obfuscate the approximate-precise boundary (APB) with entry-blurring and boundary-broadening schemes. The proposed entry-blurring scheme leverages a hidden quality metric, which has a strong correlation with approximation errors, to obscure the entrance of APB and eliminate the explicit transition between approximate and precise modes, thus improving AC systems’ resilience against APB attacks. The proposed boundary-broadening scheme enlarges the transition zone between approximate and precise modes by expanding a single APB threshold to two comparison thresholds, and it further enables a random selection of AC modules in the candidate library. The protection mechanisms provided by our obfuscation method strengthens AC systems’ resilience against APB attacks. Our case studies show that the proposed entry-blurring scheme improves the application quality by up to 168% over the baseline and successfully achieves the desired accuracy. The latency overhead of our method is negligible and the increase on area and power cost can be minimized to 6% and 8%, respectively.

Approximate computing (AC) represents a paradigm shift from conventional precise processing to inexact computation but still satisfying the system requirement on accuracy. The rapid progress on the development of diverse AC techniques allows us to apply approximate computing to many computation-intensive applications. However, the utilization of AC techniques could bring in new unique security threats to computing systems. This work does a survey on existing circuit-, architecture-, and compiler-level approximate mechanisms/algorithms, with special emphasis on potential security vulnerabilities. Qualitative and quantitative analyses are performed to assess the impact of the new security threats on AC systems. Moreover, this work proposes four unique visionary attack models, which systematically cover the attacks that build covert channels, compensate approximation errors, terminate normal error resilience mechanisms, and propagate additional errors. To thwart those attacks, this work further offers the guideline of countermeasure designs. Several case studies are provided to illustrate the implementation of the suggested countermeasures.

Approximate computing (AC) emerges as a promising approach for energy-accuracy trade-off in compute-intensive applications. However, recent work reveals that AC techniques could lead to new security vulnerabilities, which are presented in a format of visionary view. There is a lack of in-depth research on concrete attack models and estimation of the significance of the attacks on approximate arithmetic computing systems. This work presents several practical attack examples and then proposes two attack models with quantitative analysis. Input integrity check and exclusive logic based attack detection methods are proposed to address the attacks on AC systems. The experimental results show that the attack detection failure rate of our method is below $2.2*10^{-3}$ and the area and power overhead is less than 6.8% and 1.5%, respectively.

Approximate computing (AC) techniques have been widely used to improve the performance of computing systems by trading off accuracy. However, recent literature projects that the utilization of approximation could bring in new security threats to computing systems. This work presents two practical attacks on the AC systems for multilayer perceptron (MLP) and Sobel algorithm based image edge detection. The case studies in this work indicate that the approximation mechanism in AC systems can be exploited to conduct stealthy attacks, which suddenly cause significant degradation in accuracy and lead to unpredictable primary outputs. To address the emerging threats on AC systems, this work proposes to blur the boundary between approximate and precise computing submodules in AC systems. This new defense method obscures that boundary with three obfuscation schemes such that adversary could not easily identify the right target to precisely perform hardware tampering attacks. Simulation results show that the proposed method can effectively reduce the attack success rate.

Approximate computing (AC) techniques bring in an alternative way to improve energy efficiency for computing systems, at the cost of acceptable reduction on accuracy. Unfortunately, recent literature indicates that approximate computing systems could be vulnerable to new security threats. Approximation mechanisms maybe leveraged to introduce more errors than the level that the AC systems can tolerate. This work analyzes the existing metrics for accuracy from attack detection point of view. A differential metric is proposed to enlarge the Trojan induced difference on accuracy, delay and power, thus easing Trojan detection. Furthermore, this work proposes a unique attack detection method for AC systems to reduce false negative rate on Trojan detection. Our case study shows that the proposed method can reduce the false negative rate by 93%.

Approximate computing systems improve energy efficiency and computation speed at the cost of reduced accuracy on system outputs. Existing efforts mainly explore the feasible approximation mechanisms and their implementation methods. There is limited work that investigates the security threats brought by approximate computing. To fill this gap, we first analyze the approximate mechanisms used in approximate system, software, storage, and arithmetic circuits, and then propose potential attacks that will challenge the integrity and security of approximate systems. Some illustrative examples are provided accordingly to showcase the consequences of the proposed new attacks.