Architectural Support for Securing Systems Against Software Vulnerabilities

Cyberattacks are the fastest growing crime in the U.S., and they are increasing in size, sophistication, and cost. These attacks use vulnerabilities to compromise systems to leak Information (Yahoo 2016, Marriott 2018, and Facebook 2019), steal identity information (Equifax 2017), or even effecting politics (by attacking the governmental election process). Traditionally, security researchers and practitioners have viewed security as a software problem -- originating in software and to be solved by software. Recently, the Spectre and Meltdown attacks have shown that hardware should also be considered when evaluating the system security. Conversely, because many aspects of security are computationally expensive, hardware can play a role in promoting software security through computational support as well as the development of new abstractions that promote security. Under this general umbrella, the research in this dissertation pursues two research directions that demonstrate how hardware can promote software security, and how we can design hardware that is secure against Spectre and Meltdown attacks. In the first direction, security exploits and ensuant malware pose an increasing challenge to computing systems as the variety and complexity of attacks continue to increase. In response, software-based malware detection tools have grown in complexity, thus making it computationally difficult to use them to protect systems in real-time. Against this drawback, hardware-based malware detectors (HMDs) are a promising new approach to defend against malware. HMDs collect low-level architectural features and use them to classify malware from normal programs. With simple hardware support, HMDs can be always on, operating as a first line of defense that prioritizes the application of more expensive and more accurate software-detector. In this dissertation, our goal is to make HMDs practical for deployment in two ways: (1) Improving the detection accuracy of HMDs: We use specialized detectors targeted towards a specific type of malware to improve the detection of each type. Next, we use ensemble learning techniques to improve the overall accuracy by combining detectors. We explore detectors based on logistic regression (LR) and neural networks (NN). The proposed detectors reduce the false-positive rate by more than half compared to using a single detector, while increasing their sensitivity. We develop metrics to estimate detection overhead; the proposed detectors achieve more than 16.6x overhead reduction during online detection compared to an idealized software-only detector, with an 8x improvement in relative detection time. NN detectors outperform LR detectors in accuracy, overhead (by 40\%), and time-to-detection of the hardware component (by 5x). Finally, we characterize the hardware complexity by extending an open-core and synthesizing it on an FPGA platform, showing that the overhead is minimal. (2) Make them resilient to evasion attacks: we explore the question of how well evasive malware can avoid detection by HMDs. We show that existing HMDs can be effectively reverse-engineered and subsequently evaded, allowing malware to hide from detection without substantially slowing it down (which is important for certain types of malware). This result demonstrates that the current generation of HMDs can be easily defeated by evasive malware. Next, we explore how well a detector can evolve if it is exposed to this evasive malware during training. We show that simple detectors, such as logistic regression, cannot detect the evasive malware even with retraining. More sophisticated detectors can be retrained to detect evasive malware, but the retrained detectors can be reverse-engineered and evaded again. To address these limitations, we propose a new type of Resilient HMDs (RHMDs) that stochastically switch between different detectors. These detectors can be shown to be provably more difficult to reverse engineer based on resent results in probably approximately correct (PAC) learnability theory. We show that indeed such detectors are resilient to both reverse engineering and evasion, and that the resilience increases with the number and diversity of the individual detectors. Our results demonstrate that these HMDs offer effective defense against evasive malware at low additional complexity. In the second direction, the recent Spectre and Meltdown attacks show that speculative execution, which is used pervasively in modern CPUs, can leave side effects in the processor caches and other structures even when the speculated instructions do not commit and their direct effect is not visible. Therefore, they utilize this behavior to expose privileged information accessed speculatively to an unprivileged attacker. In particular, the attack forces the speculative execution of a code gadget that will carry out the illegal read, which eventually gets squashed, but which leaves a side-channel trail that can be used by the attacker to infer the value. Several attack variations are possible, allowing arbitrary exposure of the full kernel memory to an unprivileged attacker. In this dissertation, we introduce a new model (SafeSpec) for supporting speculation in a way that is immune to the side- channel leakage necessary for attacks such as Meltdown and Spectre. In particular, SafeSpec stores side effects of speculation in separate structures while the instructions are speculative. The speculative state is then either committed to the main CPU structures if the branch commits, or squashed if it does not, making all direct side effects of speculative code invisible. The solution must also address the possibility of a covert channel from speculative instructions to committed instructions before these instructions are committed (i.e., while they share the speculative state). We show that SafeSpec prevents all three variants of Spectre and Meltdown, as well as new variants that we introduce. We also develop a cycle accurate model of modified design of an x86-64 processor and show that the performance impact is negligible (in fact a small performance improvement is achieved). We build prototypes of the hardware support in a hardware description language to show that the additional overhead is acceptable. SafeSpec completely closes this class of attacks, retaining the benefits of speculation, and is practical to implement

The Implications of Naturalism as an Educational Philosophy in Jordan from the Perspectives of Childhood Education Teachers

The purpose of this study was to identify the educational implications of naturalism as an educational philosophy from the Jordanian childhood education teachers' perspectives. Each philosophy simply represents a unique conviction concerning the nature of the teaching/learning process. This study could serve as a grounded theory for Jordanian childhood teachers to comprehend the need for a clear educational philosophy within the Jordanian educational system. In addition, this research study would draw Jordanian childhood teachers' interest to be acquainted more with the educational principles of such philosophical theory. The researchers employed a questionnaire consisted of twenty one items, which correspond to the educational principles of naturalism. The quantitative approach is used to gather data as one of the techniques and descriptive due to its suitability for this study. The study findings revealed that Jordanian childhood education teachers' perspectives toward the implications of naturalism as an educational philosophy were positive for all domains; curriculum, aims, and activities. Based on the findings, the researchers provided some relevant recommendations. Keywords: Naturalism, Educational Philosophy, Childhood Education Teachers, Jordan.

The application of polynomial discriminant function classifiers to isolated arabic speech recognition

In this paper, we apply polynomial discriminant function classifiers for isolated-word speaker-independent Arabic digit recognition. The performance of the polynomial classifier is evaluated for different implementations. We also provide a performance comparison between the polynomial classifier and Dynamic Time Warping (DTW). The polynomial classifier is found to outperform DTW in many aspects such as recognition rate, and computational and memory requirements

Defensive Approximation: Securing CNNs using Approximate Computing

In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide range of real-life problems. However, these architectures are vulnerable to adversarial attacks. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine learning classifiers. We show that our approximate computing implementation achieves robustness across a wide range of attack scenarios. Specifically, for black-box and grey-box attack scenarios, we show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has access to the internal implementation of the approximate classifier. We explain some of the possible reasons for this robustness through analysis of the internal operation of the approximate implementation. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5 and an Alexnet CNNs by up to 99% and 87%, respectively for strong grey-box adversarial attacks along with up to 67% saving in energy consumption due to the simpler nature of the approximate logic. We also show that a white-box attack requires a remarkably higher noise budget to fool the approximate classifier, causing an average of 4db degradation of the PSNR of the input image relative to the images that succeed in fooling the exact classifierComment: ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2021

مستوى الدعم الاجتماعي لدى طلبة مساقات الجمباز في كلية التربية الرياضية في جامعة اليرموك

هدفت الدراسة التعرف إلى مستوى الدعم الاجتماعي لدى طلبة مساقات الجمباز في كلية التربية الرياضية في جامعة اليرموك، اعتمدت الدراسة على الأسلوب الوصفي المسحي، وتم تطبيق الاستبانة على عينة عشوائية بلغت (80) طالباً وطالبة من طلبة مساقات الجمباز مستوى (تعليم وتدريب1+2). أظهرت نتائج الدراسة أن مستوى المساندة الاجتماعية لدى طلبة كلية التربية الرياضية جاء مرتفعاً، حيث أن المتوسطات الحسابية لجميع مجالات مقياس المساندة الاجتماعية جاءت بدرجة تقييم مرتفعة، حيث جاء بالمرتبة الأولى مجال المساندة من قبل الزملاء، وبالمرتبة الثانية مجال المساندة من مدرسين مساقات الجمباز، وبالمرتبة الثالثة والأخيرة جاء مجال المساندة من الأسرة، وكشفت الدراسة عدم وجود فروق إحصائية في مستوى المساندة الاجتماعية لدى الطلبة وفقاً لمتغيري الجنس ومستوى المساق، وأوصت الدراسة بالعمل على توعية أولياء أمور الطلبة بأهمية تقديم الدعم والمساندة الاجتماعية سواء المعنوية أم المادية لأبنائهم الطلبة. The article aimed to meet the level of social support provided to the students of gymnastics at the faculty of physical education at Yarmouk University. The study adopted the descriptive-analytic method. A questionnaire was applied on a random sample of (80) male and female students majoring in gymnastics levels (1+2) the results of the study showed that the levels of social support for the students of the faculty of physical education were high, arithmetic means for all levels of social support scale came out with high evaluation degrees. At first place came the field of support from collagens. In second place the field of support from teachers of gymnastics. In third place is the field of support from family. The study revealed that there were no static differences among students in the level of social support. According to the two variables of gender and course level. The study came out with a commendation on raising awareness among students\u27 parents on the importance of providing social support to their sons and daughters morally and financially

Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks

Machine-learning architectures, such as Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks: inputs crafted carefully to force the system output to a wrong label. Since machine-learning is being deployed in safety-critical and security-sensitive domains, such attacks may have catastrophic security and safety consequences. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine-learning classifiers. We show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has unrestricted access to the approximate classifier implementation: in this case, we show that substantially higher levels of adversarial noise are needed to produce adversarial examples. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5, Alexnet and VGG-11 CNNs considerably with up to 50% by-product saving in energy consumption due to the simpler nature of the approximate logic.Comment: arXiv admin note: substantial text overlap with arXiv:2006.0770

Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses.Comment: 6 pages, Accepted and to appear in ISQED 202