High Assurance Research Articles

Navigating the Future: A Review on Challenges and Innovations in Healthcare Network Technologies

Healthcare is a vital part of a healthcare system, which is governed by a set of responsibilities and responsibilities. This paper explores the evolution of healthcare systems through the use of the Internet of Things (IoT) and telemedicine. The paper aims to identify areas for improvement and explore strategies to leverage network technologies to enhance healthcare offerings and outcomes. The data analysis technique used was thematic analysis. Thematic analysis identified and categorized key themes related to challenges and innovations in the collected studies. Telemedicine, supported by cloud computing, offers significant potential for improving healthcare delivery but faces challenges related to high assurance, interoperability, and storage adaptability. The development and use of telecommunications technology in healthcare require careful policy considerations to address the significant challenges in healthcare delivery. The research also explores how technology influences the development of healthcare delivery systems known for its efficiency, accessibility, and improved patient welfare.

Target reconstruction and process parameter decision-making for bolt intelligent assembly based on robot and multi-camera

Bolt assembly is widely used in modern manufacturing. And with the development of industrial automation, there is a growing demand for automatic assembly. To form a fully automatic and highly intelligent system for bolt assembly, a novel strategy based robot and multi-camera is proposed. In this strategy, a high assurance components reconstruction scheme is designed, wherein the Mask R-CNN is used for the identification and segmentation of the workpiece, then the shape, size and pose of the workpiece are determined by the local point cloud data and its envelope using basic geometries. The results show that the accuracy of workpiece recognition exceeds 96 %, the maximum positioning accuracy of the hole is 0.334 mm based on multi-camera system. Based on the historical fastening process data, an improved artificial jellyfish search optimizer (AJSO) BP neural network is proposed to automatically determine the optimal values for the bolt fastening speed and torque. The BP neural network optimized by AJSO reduces the maximum prediction error of process parameters from 0.117 mm/s to 0.051 mm/s for speed and from 0.052 N·m to 0.027 N·m for torque. Finally, the feasibility of the proposed strategy is demonstrated by an experimental case, which used a dual robot unit for the bearing seat assembly.

Sec-certs: Examining the security certification practice for better vulnerability mitigation

Products certified under security certification frameworks such as Common Criteria undergo significant scrutiny during the costly certification process. Yet, critical vulnerabilities, including private key recovery (ROCA, Minerva, TPM-Fail...), get discovered in certified products with high assurance levels. Furthermore, assessing which certified products are impacted by such vulnerabilities is complicated due to the large amount of unstructured certification-related data and unclear relationships between the certified products. To address these problems, we conducted a large-scale automated analysis of Common Criteria certificates. We trained unsupervised models to learn which vulnerabilities from NIST’s National Vulnerability Database impact existing certified products and how certified products reference each other. Our tooling automates the analysis of tens of thousands of certification-related documents, extracting machine-readable features where manual analysis is unattainable. Further, we identify the security requirements that are associated with products being affected by fewer and less severe vulnerabilities. This indicates which aspects of certification correlate with higher security. We demonstrate how our tool can be used for better vulnerability mitigation on four case studies of known, high-profile vulnerabilities. All tools and continuously updated results are available at https://sec-certs.org.

Ontology-Based Natural Language Processing for Sentimental Knowledge Analysis Using Deep Learning Architectures

When tested with popular datasets, sentiment categorization using deep learning (DL) algorithms will produce positive results. Building a corpus on novel themes to train machine learning methods in sentiment classification with high assurance, however, will be difficult. This study proposes a way for representing efficient features of a dataset into a word embedding layer of DL methods in sentiment classification known as KPRO (knowledge processing and representation based on ontology), a procedure to embed knowledge in the ontology of opinion datasets. This research proposes novel methods in ontology-based natural language processing utilizing feature extraction as well as classification by a DL technique. Here, input text has been taken as web ontology based text and is processed for word embedding. Then the feature mapping is carried out for this processed text using least square mapping in which the sentiment-based text has been mapped for feature extraction. The feature extraction is carried out using a Markov model based auto-feature encoder (MarMod_AuFeaEnCod). Extracted features are classified by utilizing hierarchical convolutional attention networks. Based on this classified output, the sentiment of the text obtained from web data has been analyzed. Results are carried out for Twitter and Facebook ontology-based sentimental analysis datasets in terms of accuracy, precision, recall, F-1 score, RMSE, and loss curve analysis. For the Twitter dataset, the proposed MarMod_AuFeaEnCod_HCAN attains an accuracy of 98%, precision of 95%, recall of 93%, F-1 score of 91%, RMSE of 88%, and loss curve of 70.2%. For Facebook, ontology web dataset analysis is also carried out with the same parameters in which the proposed MarMod_AuFeaEnCod_HCAN acquires accuracy of 96%, precision of 92%, recall of 94%, F-1 score of 91%, RMSE of 77%, and loss curve of 68.2%.

Machine-checked proofs of privacy against malicious boards for Selene & Co1

Privacy is a notoriously difficult property to achieve in complicated systems and especially in electronic voting schemes. Moreover, electronic voting schemes is a class of systems that require very high assurance. The literature contains a number of ballot privacy definitions along with security proofs for common systems. Some machine-checked security proofs have also appeared. We define a new ballot privacy notion that captures a larger class of voting schemes. This notion improves on the state of the art by taking into account that verification in many schemes will happen or must happen after the tally has been published, not before as in previous definitions. As a case study we give a machine-checked proof of privacy for Selene, which is a remote electronic voting scheme which offers an attractive mix of security properties and usability. Prior to our work, the computational privacy of Selene has never been formally verified. Finally, we also prove that MiniVoting and Belenios satisfies our definition.

Process optimization of IC engine testing: SiO2 nanoparticle dosed Azolla methyl ester

AbstractNanoparticles are now widely used in biodiesel‐diesel combinations for improving the engine performance and reduction of emissions. This present study investigates the Bio‐silica (Silicon‐dioxide, SiO2). The statistical and experimental study demonstrates the utilization of various levels of SiO2 in Azolla Biodiesel blend (ABD) with appropriate engine parameters such as fuel injection pressure, fuel injection timing and compression ratio for minimal emissions and optimal performance employing response surface approach. In a mixture of Azolla biodiesel and diesel, SiO2 nanoparticles were utilized at quantities of 25, 50, and 75 ppm. To determine the existence of Silicon‐dioxide, the produced nanoparticles were subjected to tests such as XRD, SEM, TEM, and EDS. The Response Surface optimizer was used with DOE L29 array and the engine predictors with a desirability value of 0.6180, were18.5°bTDC (IT), 238 Bar (IP), 17.1237 (CR), and 45.885 ppm SiO2 nanoparticle. The results were determined to be fit statistically and with high assurance levels. Thus, the addition of SiO2 nanoadditives along with aquatic fern Azolla oil methyl ester reduced the emission without affecting the engine performance.

A Detection and Response Architecture for Stealthy Attacks on Cyber-Physical Systems

There has been an increased reliance on interconnected Cyber-Physical Systems (CPS) applications. This reliance has caused tremendous growth in high assurance challenges. Due to the functional interdependence between the internal systems of CPS applications, the utilities' ability to reliably provide services could be disrupted if security threats are not addressed. To address this challenge, we propose a multi-level, multi-agent detection and response architecture built on the formalisms of Hidden Markov Models (HMM) and Markov Decision Processes (MDP). We have evaluated the performance of the proposed architecture on one of the critical smart grid applications, Advanced Metering Infrastructure (AMI). This paper utilizes a simulation tool called SecAMI for performance evaluation. A Stealthy attack scenario contains multiple distinct multi-stage attacks deployed concurrently in a network to compromise the system and stop several critical services in a CPS. The results show that the proposed architecture effectively detects and responds to stealthy attack scenarios against Cyber-Physical Systems. In particular, the simulation results show that the proposed system can preserve the availability of more than 93% of the AMI network under stealthy attacks. A future study may evaluate the effectiveness of various stealthy attack strategies and detection and response systems. The high availability of any AMI should be protected against new attack techniques. The proposed system will also determine a distributed IDS's efficient placement for intrusion detection sensors and response nodes within an AMI.

Evaluating Current Techniques for Detecting Vulnerabilities in Ethereum Smart Contracts

Ethereum intelligent contract security must be guaranteed since these decentralized apps oversee large-scale financial transactions independently. To strengthen the dependability and credibility of Ethereum smart contracts, this paper assesses existing methods for finding weaknesses in them. The primary goals are to evaluate how well hybrid approaches, formal verification, dynamic analysis, and static analysis find vulnerabilities. Methodologically, a thorough assessment of available resources and instruments was carried out to evaluate the advantages and disadvantages of each approach. Important discoveries show that although static analysis covers a large area, it ignores runtime-specific problems and produces false positives. While highly effective in finding runtime vulnerabilities, dynamic analysis is resource-intensive. High assurance is provided by formal verification, although it is complex and resource-intensive. Hybrid approaches combine several approaches to provide a well-rounded strategy but must be used carefully. The policy implications emphasize that to limit risks effectively, it is crucial to embrace multifaceted security techniques, set explicit norms, and promote easily accessible verification tools. This research advances our knowledge of smart contract security and guides policymakers and developers on securing blockchain applications.

Determining the Ability of the Vision, Aphasia, and Neglect (VAN) Stroke Scale to Identify Large Vessel Occlusion Strokes Within the Prehospital Setting: A Prospective Cohort Study

Introduction: There are several stroke assessment scales designed to identify large vessel occlusions (LVOs), and a rising area of research is concerned with identifying those that outperform others in accuracy. One scale that has shown promise in identifying identify large vessel occlusions (LVOs) is the vision, aphasia, and neglect (VAN) scale. Our understanding of this scale’s true performance, however, is limited as a majority of studies have been carried out in the hospital setting. The objective of this study is to evaluate the ability of the VAN scale to predict LVOs in the prehospital setting. Methods: Prospective cohort study comparing emergency medical service personnel administered VAN results to hospital discharge diagnoses to evaluate VAN’s ability to predict a large vessel occlusion stroke. Main outcome measures included VAN sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Results: Emergency medical service personnel administered the VAN assessment to 185 patients suspected of having a stroke. VAN had a sensitivity of 0.81 (CI, 0.61 to 0.93), specificity 0.56 (CI, 0.48 to 0.64), positive predictive value 0.24 (CI, 0.61 to 0.34), negative predictive value 0.95 (CI, 0.87 to 0.98), positive likelihood ratio 1.87 (CI, 1.45 – 2.40), negative likelihood ratio 0.33 (CI, 0.15 – 0.73), and 60% accuracy (CI, 53% - 61%) for large vessel occlusion identification. Conclusion: When negative, VAN offers relatively high assurance that the patient is not suffering a large vessel occlusion stroke. However, VAN is non-specific for large vessel occlusions and results in many false positives. Therefore, pre-hospital decision-making regarding triage should not rely exclusively on VAN due to possible over-triage to facilities with endovascular capabilities.

Model-driven development for the seL4 microkernel using the HAMR framework

Verified microkernels such as seL4 provide trustworthy foundations for safety- and security-critical systems. However, their full potential remains unrealized due, in part, to the lack of application development environments that help engineers integrate the microkernel’s configuration and hosting of application code with modeling, analysis, and verification tools that address broader aspects of the development lifecycle.This paper presents a model-driven tool chain for the seL4 microkernel based on the open source High Assurance Modeling and Rapid engineering (HAMR) code generation framework for the Architecture and Analysis Definition Language (AADL). We describe how the semantics of AADL communication and threading can be realized in terms of the access primitives and strong spatial and temporal partitioning mechanisms provided by seL4. For AADL users, seL4 provides a high-assurance platform with formally verified enforcement of component boundaries and communication pathways. For seL4 users, AADL provides high-level abstractions for developing seL4 applications, along with an ecosystem of system engineering and analysis tools. We illustrate the framework by applying a model-based development environment for increasing resiliency against cyber attacks to an unmanned aircraft flight control system.

Flexible Loading Pattern Approach in Overkill Steam Sterilization Based on the Physical Properties of Steam and Thermodynamics of Sterilization.

Because overkill steam sterilization processes in autoclaves are considered critical, they are highly scrutinized, and the use of autoclaves in fixed loading patterns is a common approach to the interpretation of regulatory requirements. Many such regulations are attributed to tradition and a buildup of restrictions that aim to improve the levels of assurance of the process and minimize risk. However, these measures complicate the operation and qualification of autoclaves, becoming cumbersome, time-consuming, and costly. In actuality, overkill sterilization is one of several processes in the pharmaceutical industry that provides the highest levels of sterility assurance. This method provides a minimum reduction of highly durable spore populations of 12 logs, achieving a probability of a nonsterile unit (PNSU) of 10-6 Because these spores are far sturdier than the common microorganisms that can be found in pharmaceutical facilities, overkill sterilization effects significantly lower PNSU values for the latter. The physical properties of steam and the thermodynamics of steam sterilization constitute a predictable and repeatable process that can be monitored and verified. The high assurance level of overkill sterilization, combined with the properties of steam, actualizes a high safety margin that encompasses nearly every load type and load configuration when the cycle is performed under certain basic rules. The aim of this article is to present data that advocate and favor an approach that allows greater freedom and variability in arranging items in an autoclave when running overkill cycles, without the need to qualify each configuration.

An Review of Medical Cyber Physical System Architecture and STP Issues

Medical Cyber-Physical System (MCPS) is a system of diagnostic instruments that is vital to healthcare. Hospitals are increasingly using these technologies to ensure that patients get high-quality treatment at all times. To name just a few, the MCPS system software confronts high assurance requirements, inoperability, security and privacy, and a host of other issues. There is an assessment and discussion of the CPS infrastructure in the present work. The study into interconnected medical device systems (MDs) to improve healthcare safety and efficiency was improved by this article. In addition, experts in medical device design may benefit from this software, which can aid them in overcoming critical obstacles and challenges in the design of the healthcare device's networks. Wireless Sensor Networks (WSNs) and networking are both discussed, as well as their respective security. After that, the CPS technologies and frameworks were built, and greater attention was paid to CPS-based healthcare.

CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection

Deep neural networks (DNNs) could be affected by the regression level of learning frameworks and challenging changes caused by external factors; their deep expressiveness is greatly restricted. Inspired by the fine-tuned DNNs with sensitivity disparity to the pixels of two states, in this paper, we propose a novel change detection scheme served by sensitivity disparity networks (CD-SDN). The CD-SDN is proposed for detecting changes in bi-temporal hyper-spectral images captured by the AVIRIS sensor and HYPERION sensor over time. In the CD-SDN, two deep learning frameworks, unchanged sensitivity network (USNet) and changed sensitivity network (CSNet), are utilized as the dominant part for the generation of binary argument map (BAM) and high assurance map (HAM). Then two approaches, arithmetic mean and argument learning, are employed to re-estimate the changes of BAM. Finally, the detected results are merged with HAM and obtain the final detected binary change maps (BCMs). Experiments are performed on three real-world hyperspectral image datasets, and the results indicate the good universality and adaptability of the proposed scheme, as well as its superiority over other existing state-of-the-art algorithms.

Consider the Consequences: A Risk Assessment Approach for Industrial Control Systems

The development of information and communication technologies extended the application of digitalized industrial control systems (ICSs) to critical infrastructure. With this circumstance, emerging sophisticated cyberattacks by adversaries, including nation-backed terrorists, target ICSs due to their strategic value that critical infrastructure can cause severe consequences to equipment, people, and the environment due to the cyberattacks on ICSs. Therefore, critical infrastructure owners should provide high assurance to those involved, such as neighboring residents and governments, that the facility is adequately protected against cyberattacks. The risk assessment that identifies, estimates, and prioritizes risks is vital to provide high assurance. This study proposes a framework for evaluating risks by quantifying the likelihood of cyber exploitation and the consequences of cyberattacks. The quantification of the likelihood of cyber exploitation is inspired by research on Bayesian attack graphs (BAGs), allowing probability evaluation that considers the causal relationship between ICSs and multistage attacks. For the cyberattack consequences quantification, we propose a methodology to evaluate how far an impact will spread and thus how many functions will be influenced when an ICS is exploited. The methodology is conducted by ICS experts identifying and listing functional dependencies and essential function goals among ICSs that they are already familiar with and do not require in-depth cybersecurity knowledge. Through experiments, we demonstrated how to apply our framework to assess the risks of the plant protection system, which is a safety-grade digital system used in nuclear power plants. The result shows that risk can be multidimensionally assessed than previous literature, such as discovering that components that were not considered important have high risk due to their functional connectivity.

Hardware/Software Co-Assurance using the Rust Programming Language and ACL2

The Rust programming language has garnered significant interest and use as a modern, type-safe, memory-safe, and potentially formally analyzable programming language. Our interest in Rust stems from its potential as a hardware/software co-assurance language, with application to critical systems such as autonomous vehicles. We report on the first known use of Rust as a High-Level Synthesis (HLS) language. Most incumbent HLS languages are a subset of C. A Rust-based HLS brings a single modern, type-safe, and memory-safe expression language for both hardware and software realizations with high assurance. As a a study of the suitability of Rust as an HLS, we have crafted a Rust subset, inspired by Russinoff's Restricted Algorithmic C (RAC), which we have imaginatively named Restricted Algorithmic Rust, or RAR. In our first implementation of a RAR toolchain, we simply transpile the RAR source into RAC. By so doing, we leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort. In this paper, we describe the RAR Rust subset, detail our prototype RAR toolchain, and describe the implementation and verification of several representative algorithms and data structures written in RAR, with proofs of correctness conducted using the ACL2 theorem prover.

High Assurance in the Twenty-First Century With Roger Schell

Bob Blakley: Welcome, everybody, to episode three of the IEEE Over the Rainbow podcast. I am your host Bob Blakley, and we’re here today with one of the founders of the field of information security, Roger Schell. Roger, back in the early 1980s, was one of the leaders of the production of a U.S. government standard called the Orange Book [formally titled the Trusted Computer System Evaluation Criteria, (TCSEC)], which essentially defined the model for a secure computer system. I have a copy of it here. Roger, you’ll be happy to see that I used it heavily enough that I had to take the staples out. I think it’s probably a good time to be having this conversation because this year is the 50th anniversary of the reference monitor model. Nineteen seventy-two saw the publication of the Anderson report, which introduced the concept, as far as I’m aware, in the public literature—and that’ll be 50 years ago this year. And—as I think you’re aware, Roger—the title of our podcast is a sort of multilayered pun, and one of the aspects of the pun is a reference to the Rainbow Series, which the Orange Book was the inaugural volume of. One of the sly suggestions we were hinting at in the title was that, in some sense, the computer security industry is over the Rainbow Series—over in the sense of sort of wanting to leave it in the past. But I think one of the things that we’re going to talk about today is that it’s not easy to leave the Orange Book in the past and also possibly not wise. So, with that introduction, Lorrie, I’ll hand it to you to start our questions.

Coronavirus Genus Recognition Based on Prototype Virus Variants

Method named as variant approach to recognizing genus of coronavirus that is based on frequency of codon distribution in viral ORF1ab and genes of structural proteins (S, M and N) was proposed in the work. This method uses modified statistics whose efficiency was demonstrated earlier for flavivirus species recognition. To recognize genus of coronavirus the variant approach considers both various combinations of several structural coronavirus genes and individual structural genes. Finally, coronavirus genus is determined in the result of analysis of all variants considered. The method proposed was developed with the help of learning sample from prototype viral variants of Alphacoronavirus, Betacoronavirus, Deltacoronavirus and Gammacoronavirus genus. Application of the variant approach to recognizing genus of coronavirus has demonstrated the approach high assurance at level of 95 %. Among all variants of joint analysis, the most reliability (98 %) in recognizing genus has been achieved if codon frequency of the ORF1ab was used. Variant approach has revealed a phenomenon of mosaic structure in coronavirus genomes, i.e., when the results of genus recognition for a few genes differ from final conclusion about coronavirus genus. It seems that such phenomenon reflects homologous recombinations of the genes between various species of the coronaviruses and plasticity of their genomes in evolutionary processes.

Formal Specification and Verification of Data Separation for Muen Separation Kernel

Introduction: Development of integrated mixed-criticality systems is becoming increasingly popular for application-specific systems, which needs separation mechanism for available onboard resources and the processors equipped with hardware virtualization. Hardware virtualization allow the partitions to physical resources, which include processor cores, memory, and I/O devices, among guest virtual machines (VMs). For building mixed criticality computing environment, traditional virtual machine systems are inappropriate because they use hypervisors to schedule separate VMs on physical processor cores. In this article, we discuss the design of an environment for mixed-criticality systems: The Muen an x86/64 separation kernel for high assurance. The Muen Separation Kernel is an Open Source microkernel which has no runtime errors at the source code level. The Muen separation kernel has been designed precisely to encounter the challenging requirements of high-assurance systems built on the Intel x86/64 platform. Muen is under active development, and none of the kernel properties of it has been verified yet. In this paper, we present a novel work of verifying one of the kernel properties formally. Method: The CTL used in NuSMV is a first-order modal along with data-depended processes and regular formulas. CTL is a branching-time logic, meaning that its model of time is a tree-like structure in which the future is not determined; there are different paths in the future, any one of which might be an actual path that is realized . This section shows the verification of all the requirements mentioned in section 3. In NuSMV tool the command used for verification of the formulas written in CTL is checkctlspec -p ”CTL-expression”. The nearest quantifier binds each occurrence of a variable in the scope of the bound variable, which has the same name and the same number of arguments. Result: Formal methods have been applied to various projects for specification and verification purpose. Some of them are the SCOMP , SeaView , LOCK,and Multinet Gateway projects. The TLS was written formally. Several mappings were done between the TLS and the SCOMP code: Informal English language to TLS, TLS to actual code , and TLS to pseudo-code. The authors present an ACL2 model for a generic separation kernel also known as GWV approach. Conclusion: We consider the formal verification of data separation property which is one of the crucial modules to achieve the separation functionality. The verification of the data separation manager is carried out on the design level using the NuSMV tool. Furthermore, we present the complete model of the data separation unit along with its code written in the NuSMV modelling language. Finally, we have converted the non-functional requirements into the formal logic, which then has verified the model formally.

Structural Efficiency of Non-Prismatic Hollow Reinforced Concrete Beams Retrofitted with CFRP Sheets

Non-prismatic reinforced concrete (RC) beams are widely used for various practical purposes, including enhancing architectural aesthetics and increasing the overall thickness in the support area above the column, which gives high assurance to services that this will not result in the distortion of construction features and can reduce heights. The hollow sections (recess) can also be used for the maintenance of large structural sections and the safe passage of utility lines of water, gas, telecommunications, electricity, etc. They are generally used in large and complex civil engineering works like bridges. This study conducted a numerical study using the commercial finite element software ANSYS version 15 for analysing RC beams, hollow longitudinally sectioned and retrofitted with carbon fibre reinforced polymers (CFRPs), which were subjected to concentrated vertical loads. The numerical analysis results on the simulated beam models were in excellent agreements with the previous experimental test results. This convergence was confirmed by a statistical analysis, which considered the correlation coefficients, individual arithmetic means and standard deviations for all the calculated deflections of the simulated beam models. A proposed numerical simulation model with the hypotheses can be considered suitable for modelling the behaviours of simple supported non-prismatic RC beams under vertical concentrated loads. The numerical results showed that altering the cross-section from solid to hollow could reduce the load carrying capacities of the beams by up to 53% and increase the corresponding deflections by up to 40%, respectively. Using steel pipes for making recesses could enhance the loading capacity by up to 56%, increase the ductility, and reduce the corresponding deflections by up to 30%, respectively. Finally, it was found that bonding the CFRP sheets in the lower middle tensile areas of the hollow beams could improve the resistance and reduce the deformations by up to 27%. The failure patterns for all the numerical models were shear failure. The cylinder compressive strength could be used as a mechanical parameter for modelling and assessing the structural behaviours of the beam models, as its increase could improve the load carrying capacities and reduce the deflections by 30–50%.

A Pre-Silicon Approach to Discovering Microarchitectural Vulnerabilities in Security Critical Applications

Microarchitectural vulnerabilities have become an increasingly effective attack vector. This is especially problematic for security critical applications, which handle sensitive data and may employ software-level hardening in order to thwart data leakage. These strategies rely on necessary assumptions about the underlying microarchitectural implementation, which may (and have proven to be) incorrect in some instances, leading to exploits. Consequently, devising early-stage design tools for reasoning about and verifying the correctness of high assurance applications with respect to a given hardware design is an increasingly important problem. This letter presents a principled dynamic testing methodology to reveal and analyze <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-dependent</i> microarchitectural behavior with the potential to violate assumptions and requirements of security critical software. A differential analysis is performed of the microarchitectural state space explored during register transfer-level (RTL) simulation to reveal internal activity which correlates to sensitive data used in computation. We demonstrate the utility of the proposed methodology through it's ability to identify secret data leakage from selected case studies with known vulnerabilities.