Application stress testing Achieving cyber security by testing cyber attacks

We present the Chained Attacks approach, an automated model-based approach to test the security of web applications that does not require a background in formal methods. Starting from a set of HTTP conversations and a configuration file providing the testing surface and purpose, a model of the System Under Test (SUT) is generated and input, along with the web attacker model we defined, to a model checker acting as test oracle. The HTTP conversations, payload libraries, and a mapping created while generating the model aid the concretization of the test cases, allowing for their execution on the SUT's implementation. We applied our approach to a real-life case study and we were able to find a combination of different attacks representing the concrete chained attack performed by a bug bounty hunter.

Security has been always treated as an add-on feature in the software development lifecycle, and addressed by security professionals using firewalls, proxies, intrusion prevention systems, antivirus and platform security. Software is at the root of all common computer security problems, and hence hackers don't create security holes, but rather exploit them. Security holes in software applications are the result of bad design and implementation of software systems and applications. To address this problem, several initiatives for integrating security in the software development lifecycle have been proposed, along with tools to support a security-centric software development lifecycle. This paper introduces a framework for evaluating security static analysis tools such as source code analyzers, and offers evaluation of non-commercial static analysis tools such as Yasca, CAT.NET, and FindBugs. In order to evaluate the effectiveness of such tools, common software weaknesses are defined based on CWE/SANS Top 25, OWASP Top Ten and NIST source code weaknesses. The evaluation methodology is based on the NIST Software Assurance Metrics And Tool Evaluation (SAMATE). Results show that security static analysis tools are, to some extent, effective in detecting security holes in source code; source code analyzers are able to detect more weaknesses than bytecode and binary code scanners; and while tools can assist the development team in security code review activities, they are not enough to uncover all common weaknesses in software. The new test cases developed for this research have been contributed to the NIST Software Assurance Reference Dataset (samate.nist.gov/SARD).

Validation and Verification are necessary in the life cycle of any safety-critical software system. It answers the question of “are we building the right product?” It's very important to be able to decide if its outputs are correct and system meets specifications, failing to do so can result in loss of human lives or huge financial loss. V&V process and its planning must start early in SDLC (Software Development Life Cycle). Both aspects are essential, If specifications are met that doesn't mean it's correct and vice versa. There are different V&V techniques available for different stages of the SDLC. In this paper I will analyze different V&V techniques available for critical software systems and will conduct a survey, which will produce results showing which techniques are best for safety-critical software systems.

Security issues of web applications are still a current topic of interest especially when considering the consequences of unintended behaviour. Such services might handle sensitive data about several thousands or millions of users. Hence, exploiting services or other undesired effects that cause harm on users has to be avoided. Therefore, for software developers of such applications one of the major tasks in providing security is to embed testing methodologies into the software development cycle, thus minimizing the subsequent damage resulting in debugging and time intensive upgrading. Model-based testing evolved as one of the methodologies which offer several theoretical and practical approaches in testing the system under test (SUT) that combine several input generation strategies like mutation testing, using of concrete and symbolic execution etc. by putting the emphasis on specification of the model of an application. In this work we propose an approach that makes use of an attack pattern model in form of a UML state machine for test case generation and execution. The paper also discusses the current implementation of our attack pattern testing tool using a XSS attack pattern and demonstrates the execution in a case study.

Security issues of web applications are still a current topic of interest especially when considering the consequences of unintended behaviour. Such services might handle sensitive data about several thousands or millions of users. Hence, exploiting services or other undesired effects that cause harm on users has to be avoided. Therefore, for software developers of such applications one of the major tasks in providing security is to embed testing methodologies into the software development cycle, thus minimizing the subsequent damage resulting in debugging and time intensive upgrading. Model-based testing evolved as one of the methodologies which offer several theoretical and practical approaches in testing the system under test (SUT) that combine several input generation strategies like mutation testing, using of concrete and symbolic execution etc. by putting the emphasis on specification of the model of an application. In this work we propose an approach that makes use of an attack pattern model in form of a UML state machine for test case generation and execution. The paper also discusses the current implementation of our attack pattern testing tool using a XSS attack pattern and demonstrates the execution in a case study.

Power system automation and communication standards are spearheading the power system transition towards a smart grid. IEC 61850 is one such standard, which is widely used for substation automation and protection. It enables real-time communication and data exchange between critical substation automation and protection devices within digital substations. However, IEC 61850 is not cyber secure. In this paper, we demonstrate the dangerous implications of not securing IEC 61850 standard. Cyber attacks may exploit the vulnerabilities of the Sampled Values (SV) and Generic Object-Oriented Substation Event (GOOSE) protocols of IEC 61850. The cyber attacks may be realised by injecting spoofed SV and GOOSE data frames into the substation communication network at the bay level. We demonstrate that such cyber attacks may lead to obstruction or tripping of multiple protective relays. Coordinated cyber attacks against the protection system in digital substations may cause generation and line disconnections, triggering cascading failures in the power grid. This may eventually result in a partial or complete blackout. The attack model, impact on system dynamics and cascading failures are veri ed experimentally through a proposed cyber-physical experimental framework that closely resembles real-world conditions within a digital substation, including Intelligent Electronic Devices (IEDs) and protection schemes. It is implemented through Hardware-in-the-Loop (HIL) simulations of commercial relays with a Real-Time Digital Simulator (RTDS).

This paper presents two concept models to compare and contrast conventional software development life cycle(SDLC) and the development of smart applications where Artificial Intelligence/Machine Learning (AI/ML) modules are integrated into the software products. The first concept model illustrates the intersections and differences between SDLC and AI/ML-based application development; whereas, the second concept model depicts the processes/concepts involved in data preprocessing stage of AI/ML-based application development. The concept models show that while there are some similarities between SDLC and AI/ML-based application development; AI/ML-based development follows its own unique features and challenges that need development of further software engineering techniques.

In recent times, software industry has seen the immense expansion in its popularity. Graphical user interface (GUI) is of the most important and ground breaking factor behind the exponential growth of acceptance of software systems. Beside this popularity, software organizations are trying best to optimize the quality of delivered products. In software development life cycle, software testing is one major phase having focus towards improved software quality. Increased attention towards GUI in development helped in materialization of GUI testing as a vital branch of software testing. Manual efforts to test GUI have revealed a lot of problems, which, ultimately led towards automation of GUI testing. This paper presents a novel approach towards automation of GUI test coverage analysis based on an evolutionary algorithm, Particle Swarm Optimization (PSO). Another objective that has been tried to achieve is keeping number of test cases at minimum side.

Honeypots are the information system resources used for capturing and analysis of cyber attacks. Highinteraction Honeypots are capable of capturing attacks in their totality and hence are an ideal choice for capturing multi-stage cyber attacks. The term multi-stage attack is an abstraction that refers to a class of cyber attacks consisting of multiple attack stages. These attack stages are executed either by malicious codes, scripts or sometimes even inbuilt system tools. In the work presented in this paper we have proposed a framework for capturing, analysis and modelling of multi-stage cyber attacks. The objective of our work is to devise an effective mechanism for the classification of multi-stage cyber attacks. The proposed framework comprise of a network of high interaction honeypots augmented with an attack analysis engine. The analysis engine performs rule based labeling of captured honeypot data. The labeling engine labels the attack data as generic events. These events are further fused to generate attack graphs. The hence generated attack graphs are used to characterize and later classify the multi-stage cyber attacks.

This research is to study the application software development of the distributed control system (DCS) in a manufacturing company named ABC (unreal name). The company delivered a DCS product including both hardware and software. Since the hardware is manufactured aboard, this research focuses only at the software development from the process of gathering the customer requirements to the software inspection test and product delivery. The developed application software is made-to-order and unique. The company provides different software according to the different needs of different customers. The writer has used the failure mode and effect analysis (FMEA) and the fault tree analysis (FTA) as quality tools for analysing the potential failure modes and their effects in the DCS project execution in a systematic way. From the study, the writer has discovered three major problems in the DCS project execution. These included (1) lack of procedures to control the project execution, (2) lack of the document and data control, and (3) technical problems. The result of these problems heavily affects to the DCS project execution. The company is unable to deliver the DCS to the customer on time for several projects and software bugs are also embedded to the delivered software product. These lead to the customer dissatisfaction and the company itself has made the profit much less than it was expected. The results of the analysis using the FMEA technique have leaded to the establishment of the quality assurance system for the DCS project execution which include checklists, standard document and prodedures, and the engineering database pool software (EDP). The EDP retains the useful engineering knowledge and the application software functions of the past DCS projects. The EDP is developed with the purpose to share and enhance engineering knowledge of engineers and to reuse the past application software functions so that engineers do not have to redesign the customer requested functions which have already been created before. The established quality assurance system has been implemented to one DCS project in the company and the results were the improvement in software quality and on-time delivery. 37 software errors were detected during the pre-inspection test and there were only 8 errors during the inspection test with the customer, comparing to the average of 50 to 60 errors found during the inspection test in the past projects. In the view of changes in RPN (Risk Priority Number), the percentage of RPN for each critical process of the project execution comparing between before and after implementation decreased 33 to 94%.

Centralized system protection and emergency control schemes are playing critical roles in maintaining the stability of modern power system. Being able to make optimal decisions based on global information and coordinate complex protection/control activities is the most desirable feature of a centralized scheme. On the other hand, centralized intelligence is quite vulnerable to various malicious cyber attacks, and the risk of losing it might be extremely high. This paper proposes a multi-agent based system protection and emergency control scheme with decentralized intelligence. The proposed decentralized scheme can detect coordinated cyber attacks with special patterns and carry out the best effort remedial actions in an adaptable manner, and such that system stability is maintained even part of the scheme is compromised. One decentralized load rejection scheme is designed and implemented in IEEE 39 bus system as a study case. Scheme performance is tested when under various attack vectors and desirable cyber attack resiliency is exhibited.

Subsynchronous resonance (SSR) is among the most severe instability conditions that may happen when grid-tied inverter-based renewable energy sources (RESs), like wind power, connect to a weak transmission grid. The potential impact of SSR includes loss of wind power generation, physical equipment damage, or instability that could spread to a larger area. Such risks make the subsynchronous stability of permanent magnet synchronous generator (PMSG) based wind farms a potential target for adversaries. To this end, this paper investigates and models two new cyber attack schemes targeting SSR in PMSG-based wind farms, which have high energy output and less maintenance. Considering the major causes and different damping controls for SSR in PMSG-based wind farms, this paper demonstrates the feasibility of the threat from the two proposed cyber attacks and compares them using the IEEE 9-bus benchmark. The results show that smartly craft cyber attacks can successfully degrade SSR damping, trigger an SSR, and even destabilize the power grid.

In Cyber-Physical Production Systems (CPPS), integrity and availability of hardware and software components are necessary to ensure product quality and the safety of employees and customers, while the confidentiality of engineering artifacts and product details must be kept to hide company secrets. At the same time, an increasing number of Internet connected control systems causes the presence of new attack vectors. As a result, unauthorized hardware/software modifications of CPPS components through cyber attacks become more prevalent. This development raises the demand for proper protection measures significantly, not only to ensure product quality and security but also the safety of people working with the machinery. In this chapter, we describe vulnerable assets of Operational Technology (OT) and identify information security requirements for these assets. Based on this assessment, possible attack vectors and threat models are discussed. Furthermore, measures against the mentioned threats and security relevant differences between OT and Information Technology (IT) systems are outlined. To manage a CPPS and its related threats, risk management will be addressed in more detail. Although safety and security should no longer be viewed as isolated, there are several challenges of integrating safety and security, which can lead to struggles and trade-offs. For this reason, the “Safety and Security Lab in Industry” currently investigates different aspects of future integrated solutions covering both safety and security. Challenges of such integrated solutions are outlined at the end of the chapter.

An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience

Small and Medium-sized Enterprises (SMEs) face unique challenges in software deployment, often dealing with limited resources, budget constraints, and the need for fast, reliable updates. This review outlines a Continuous Integration and Continuous Deployment (CI/CD) model specifically tailored to optimize software deployment in SME environments. The proposed CI/CD framework enhances operational efficiency by automating the integration, testing, and deployment processes, significantly reducing the risk of human error and deployment failures. The model leverages key CI/CD practices, including automated testing, version control, and continuous monitoring, to ensure seamless updates and minimal downtime. By integrating tools like Jenkins, GitLab, and Docker, SMEs can streamline code integration, enhance collaboration between development and operations teams, and enable faster, more frequent releases. Automated testing ensures that code changes are validated before deployment, identifying and addressing potential issues early in the development cycle. The framework also emphasizes rollback capabilities and monitoring systems to further reduce deployment risks. Rollbacks allow for quick recovery in case of failure, while continuous monitoring enables real-time detection of performance issues, helping SMEs maintain operational uptime and minimize service disruptions. Moreover, the model supports a microservices architecture, allowing SMEs to scale their applications efficiently without compromising stability. Incorporating CI/CD practices fosters agility, enabling SMEs to respond rapidly to market demands and customer feedback. This model not only improves software quality and deployment speed but also aligns with the budgetary and operational limitations of SMEs. It provides a cost-effective solution that enhances overall system reliability and reduces the frequency of software-related incidents. This CI/CD model serves as a practical approach to software deployment, empowering SMEs to optimize their software development lifecycle while ensuring continuous improvement and operational stability.