在本文中，我们提出了设计的错误弹性机器学习架构采用分布式机器学习框架分类集成（CE）。CE结合了几个简单的分类器，以获得一个强大的。相比之下，集中式机器学习采用单个复杂块。我们比较了随机森林（RF）和支持向量机（SVM），这是从CE和集中式框架的代表技术，分别。利用UCI机器学习库中的数据集和商用45 nm CMOS工艺中的架构级误差模型，证明了在由于近阈值电压（NTV）区域（0.3 V - 0.7 V）的工艺变化而存在定时误差的情况下，基于RF的架构比SVM架构显著更鲁棒。具体地，RF架构表现出变化3.2%的检测精度（P_{det}），同时在28.9%的门级延迟变化处保持中值P_{det} > 0.9。相比之下，SVM表现出变化16.8%的P_{det}。此外，我们提出了一个错误加权投票技术，结合了NTV电路结构的定时误差统计，以进一步提高鲁棒性。仿真结果证实，误差加权投票实现了变化仅为1.4%的P_{det}，这是12倍，低于SVM。
英文摘要：
In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE). CE combines several simple classifiers to obtain a strong one. In contrast, centralized machine learning employs a single complex block. We compare the random forest (RF) and the support vector machine (SVM), which are representative techniques from the CE and centralized frameworks, respectively. Employing the dataset from UCI machine learning repository and architectural-level error models in a commercial 45 nm CMOS process, it is demonstrated that RF-based architectures are significantly more robust than SVM architectures in presence of timing errors due to process variations in near-threshold voltage (NTV) regions (0.3 V - 0.7 V). In particular, the RF architecture exhibits a detection accuracy (P_{det}) that varies by 3.2% while maintaining a median P_{det} > 0.9 at a gate level delay variation of 28.9% . In comparison, SVM exhibits a P_{det} that varies by 16.8%. Additionally, we propose an error weighted voting technique that incorporates the timing error statistics of the NTV circuit fabric to further enhance robustness. Simulation results confirm that the error weighted voting achieves a P_{det} that varies by only 1.4%, which is 12X lower compared to SVM.
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore, highly predictive features should be used first during classification in order to determine the set of possible output labels. Our methodology focuses the problem of designing resilient classifiers into a problem of designing resilient feature extractors for these highly predic…
George Mason University（乔治梅森大学）；Dhaka University of Engineering & Technology（达卡工程技术大学）；Universite Polytechnique Hauts-De-France（上法兰西理工大学）；CNRS（法国国家科学研究中心）；IEMN（微电子与纳米技术研究所）
Machine learning-based hardware malware detectors (HMDs) offer a potential game changing advantage in defending systems against malware. However, HMDs suffer from adversarial attacks, can be effectively reverse-engineered and subsequently be evaded, allowing malware to hide from detection. We address this issue by proposing a novel HMDs (Stochastic-HMDs) through approximate computing, which makes HMDs' inference computation-stochastic, thereby making HMDs resilient against adversarial evasion attacks. Specifically, we propose to leverage voltage overscaling to induce stochastic computation in …
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smaller compared to the size of the data set. Class imbalance poses a key challenge that plagues most multi-label learning methods. Ensemble of Classifier Chains (ECC), one of the most prominent multi-label learning methods, is no exception to this rule, as each of the binary models it builds is trained from all positive and negative examples of a label. To make ECC resilient to class imbalance, we first couple it with ran…
ETH Zürich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）
Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented by reliable standard-cell memories to safely store critical data under aggressive voltage scaling. On a prototype in 22nm FDX technology, we demonstrate…
The damage caused by crypto-ransomware, due to encryption, is difficult to revert and cause data losses. In this paper, a machine learning (ML) classifier was built to early detect ransomware (called crypto-ransomware) that uses cryptography by program behavior. If a signature-based detection was missed, a behavior-based detector can be the last line of defense to detect and contain the damages. We find that input/output activities of ransomware and the file-content entropy are unique traits to detect crypto-ransomware. A deep-learning (DL) classifier can detect ransomware with a high accuracy…
There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improvement in detection rates and lower false positive rates at large scale compared with traditional malware analysis methods. ML in threat detection has demonstrated to be a good cop to guard platform security. However it is imperative to evaluate - is ML-powered security resilient enough? In this paper, we juxtapose the resiliency and trustworthiness of ML algorithms for security, via a case study of evaluating the res…
Ensemble Learning methods combine multiple algorithms performing the same task to build a group with superior quality. These systems are well adapted to the distributed setup, where each peer or machine of the network hosts one algorithm and communicate its results to its peers. Ensemble learning methods are naturally resilient to the absence of several peers thanks to the ensemble redundancy. However, the network can be corrupted, altering the prediction accuracy of a peer, which has a deleterious effect on the ensemble quality. In this paper, we propose a noise-resilient ensemble classificat…
University of Akli Mouhand Oulhadj-Bouira（阿克利·穆汉德·乌尔哈吉-布伊拉大学）；Ferhat Abbas Sétif 1 University（费尔哈特·阿巴斯·塞蒂夫第一大学）；Hassiba Benbouali University（哈西巴·本布阿里大学）
A sensorless speed, average temperature and resistance estimation technique based on Neural Network (NN) for brushed DC machines is proposed in this paper. The literature on parameters and state spaces estimations of the Brushed DC machines, shows a variety of approaches. However, these observers are sensitive to a noise, on the model accuracy also are difficult to stabilize and to converge. Furthermore, the majority of earlier works, estimate either the speed or the temperature or the winding resistance. According to the literatures, the Resilient backpropagation (RBP) as is the known as the …
Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；State Grid Chengde Power Supply Company（国网承德供电公司）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）
Deep neural network (DNN) classifiers are powerful tools that drive a broad spectrum of important applications, from image recognition to autonomous vehicles. Unfortunately, DNNs are known to be vulnerable to adversarial attacks that affect virtually all state-of-the-art models. These attacks make small imperceptible modifications to inputs that are sufficient to induce the DNNs to produce the wrong classification. In this paper we propose a novel, lightweight adversarial correction and/or detection mechanism for image classifiers that relies on undervolting (running a chip at a voltage that i…
