Design of secure and robust cognitive system for malware detection

Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.Comment: arXiv admin note: substantial text overlap with arXiv:2104.0665

Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, yet Effective Time Series CNN-Based Approach

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on detecting malicious applications that are spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. Next, to address the challenge of stealthy malware detection at the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based approach to accurately detect stealthy malware trace at run-time using branch instructions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that using state-of-the-art ML-based malware detection methods is not effective in detecting stealthy malware samples since the captured HPC data not only represents malware but also carries benign applications’ microarchitectural data. The experimental results demonstrate that with the aid of our novel intelligent approach, stealthy malware can be detected at run-time with 94% detection performance on average with only one HPC feature, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively

Overcoming the Intuition Wall: Measurement and Analysis in Computer Architecture

These are exciting times for computer architecture research. Today there is significant demand to improve the performance and energy-efficiency of emerging, transformative applications which are being hammered out by the hundreds for new computing platforms and usage models. This booming growth of applications and the variety of programming languages used to create them is challenging our ability as architects to rapidly and rigorously characterize these applications. Concurrently, hardware has become more complex with the emergence of accelerators, multicore systems, and heterogeneity caused by further divergence between processor market segments. No one architect can now understand all the complexities of many systems and reason about the full impact of changes or new applications. To that end, this dissertation presents four case studies in quantitative methods. Each case study attacks a different application and proposes a new measurement or analytical technique. In each case study we find at least one surprising or unintuitive result which would likely not have been found without the application of our method

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