Model-Based Verification for SIMULINK Design

Testing a Model-Based design is the only way to determine the correctness of the designed model but not enough to conclude that the design is error free. Verification exposes all the design errors and describes the functionality of the system. Assertion based verification helps to determine whether the model obey the actual design requirements. This thesis work is mainly based on verification of a Water Tank control system modeling using SIMULINK model

Code Integrity Attestation for PLCs using Black Box Neural Network Predictions

Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs). Unfortunately, traditional techniques for attesting code integrity (i.e. verifying that it has not been modified) rely on firmware access or roots-of-trust, neither of which proprietary or legacy PLCs are likely to provide. In this paper, we propose a practical code integrity checking solution based on privacy-preserving black box models that instead attest the input/output behaviour of PLC programs. Using faithful offline copies of the PLC programs, we identify their most important inputs through an information flow analysis, execute them on multiple combinations to collect data, then train neural networks able to predict PLC outputs (i.e. actuator commands) from their inputs. By exploiting the black box nature of the model, our solution maintains the privacy of the original PLC code and does not assume that attackers are unaware of its presence. The trust instead comes from the fact that it is extremely hard to attack the PLC code and neural networks at the same time and with consistent outcomes. We evaluated our approach on a modern six-stage water treatment plant testbed, finding that it could predict actuator states from PLC inputs with near-100% accuracy, and thus could detect all 120 effective code mutations that we subjected the PLCs to. Finally, we found that it is not practically possible to simultaneously modify the PLC code and apply discreet adversarial noise to our attesters in a way that leads to consistent (mis-)predictions.Comment: Accepted by the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021

Potential Errors and Test Assessment in Software Product Line Engineering

Software product lines (SPL) are a method for the development of variant-rich software systems. Compared to non-variable systems, testing SPLs is extensive due to an increasingly amount of possible products. Different approaches exist for testing SPLs, but there is less research for assessing the quality of these tests by means of error detection capability. Such test assessment is based on error injection into correct version of the system under test. However to our knowledge, potential errors in SPL engineering have never been systematically identified before. This article presents an overview over existing paradigms for specifying software product lines and the errors that can occur during the respective specification processes. For assessment of test quality, we leverage mutation testing techniques to SPL engineering and implement the identified errors as mutation operators. This allows us to run existing tests against defective products for the purpose of test assessment. From the results, we draw conclusions about the error-proneness of the surveyed SPL design paradigms and how quality of SPL tests can be improved.Comment: In Proceedings MBT 2015, arXiv:1504.0192

Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

The control logic models built by Simulink or Ptolemy have been widely used in industry scenes. It is an urgent need to ensure the safety and security of the control logic models. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art model testing tools employ model checking techniques or search-based methods to generate test cases. Traditional search based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. In this manner, our work combines the advantages of the model checking methods and fuzzing techniques in a novel way. Since the control logic models always receive signal inputs, we specifically design novel mutation operators for signals to improve the existing fuzzing method in model testing. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works.Comment: 10 page

Toward Biologically-Inspired Self-Healing, Resilient Architectures for Digital Instrumentation and Control Systems and Embedded Devices

Digital Instrumentation and Control (I&C) systems in safety-related applications of next generation industrial automation systems require high levels of resilience against different fault classes. One of the more essential concepts for achieving this goal is the notion of resilient and survivable digital I&C systems. In recent years, self-healing concepts based on biological physiology have received attention for the design of robust digital systems. However, many of these approaches have not been architected from the outset with safety in mind, nor have they been targeted for the automation community where a significant need exists. This dissertation presents a new self-healing digital I&C architecture called BioSymPLe, inspired from the way nature responds, defends and heals: the stem cells in the immune system of living organisms, the life cycle of the living cell, and the pathway from Deoxyribonucleic acid (DNA) to protein. The BioSymPLe architecture is integrating biological concepts, fault tolerance techniques, and operational schematics for the international standard IEC 61131-3 to facilitate adoption in the automation industry. BioSymPLe is organized into three hierarchical levels: the local function migration layer from the top side, the critical service layer in the middle, and the global function migration layer from the bottom side. The local layer is used to monitor the correct execution of functions at the cellular level and to activate healing mechanisms at the critical service level. The critical layer is allocating a group of functional B cells which represent the building block that executes the intended functionality of critical application based on the expression for DNA genetic codes stored inside each cell. The global layer uses a concept of embryonic stem cells by differentiating these type of cells to repair the faulty T cells and supervising all repair mechanisms. Finally, two industrial applications have been mapped on the proposed architecture, which are capable of tolerating a significant number of faults (transient, permanent, and hardware common cause failures CCFs) that can stem from environmental disturbances and we believe the nexus of its concepts can positively impact the next generation of critical systems in the automation industry

Systematic Model-based Design Assurance and Property-based Fault Injection for Safety Critical Digital Systems

With advances in sensing, wireless communications, computing, control, and automation technologies, we are witnessing the rapid uptake of Cyber-Physical Systems across many applications including connected vehicles, healthcare, energy, manufacturing, smart homes etc. Many of these applications are safety-critical in nature and they depend on the correct and safe execution of software and hardware that are intrinsically subject to faults. These faults can be design faults (Software Faults, Specification faults, etc.) or physically occurring faults (hardware failures, Single-event-upsets, etc.). Both types of faults must be addressed during the design and development of these critical systems. Several safety-critical industries have widely adopted Model-Based Engineering paradigms to manage the design assurance processes of these complex CPSs. This thesis studies the application of IEC 61508 compliant model-based design assurance methodology on a representative safety-critical digital architecture targeted for the Nuclear power generation facilities. The study presents detailed experiences and results to demonstrate the benefits of Model testing in finding design flaws and its relevance to subsequent verification steps in the workflow. Additionally, to study the impact of physical faults on the digital architecture we develop a novel property-based fault injection method that overcomes few deficiencies of traditional fault injection methods. The model-based fault injection approach presented here guarantees high efficiency and near-exhaustive input/state/fault space coverage, by utilizing formal model checking principles to identify fault activation conditions and prove the fault tolerance features. The fault injection framework facilitates automated integration of fault saboteurs throughout the model to enable exhaustive fault location coverage in the model