Software Testing for cyber Security

Background: Software testing and software security have become one of the most important parts of an application. Many studies have explored each of these topics but there is a gap wherein the relation of software security and software testing in general has not been explored. Objective: This study aims to conduct a systematic literature review to capture the current state-of-the-art in software testing related to security. Methods: The search strategy obtains relevant papers from IEEE Xplore and ScienceDirect. The results of the search are filtered by applying inclusion and exclusion criteria. Results: The search results identified 50 papers. After applying the inclusion/exclusion criteria, we identified 15 primary studies that discuss software security and software testing. We found approaches, aspects, references, and domains that are used in software security and software testing. Conclusion: We found certain approach, aspect, references, and domain are used more often in software security testing Keywords: Software security, Software testing, Security testing approach, Security threats, Systematic literature review

Objectives: To explored the highlighted obliged QoS routing issue in Vehicular Ad hoc Networks (VANETs). Methods/ Statistical Analysis: The issues are tended to firstly by working up an association faithful quality model considering the topological and numerical properties of vehicular advancements and speeds. The VANET correspondence diagram in advancing chart hypothesis is used to model and organize the formed association trustworthiness shown into it. In light of the consequent intensified chart diagram illustrate, the most tried and true course in the framework is picked. However course assurance does not ensure awesome transmission as well. Findings: Vehicular extraordinarily designated Systems (VANETs) are a particular sort of remote framework made by vehicles giving among themselves and with roadside base stations. A broad assortment of organizations has been made for VANETs going from security to infotainment applications. A key need for such organizations is that they are offered with Nature of Administration Quality of Service (QoS) guarantees similar to organization unfaltering quality and availability. QoS routing expect an essential part in recognizing courses that meet the QoS necessities of the offered organization over VANETs. For filtering the achievable courses it subject to various QoS goals is with everything taken into account a NP-difficult issue. Furthermore, the reliability of the routing ought to be given unprecedented thought as correspondence associations, as frequently as conceivable in VANETs. In present situation, the most existing QoS routing calculations are been PSO algorithm for stable VANET system without considering the security and wellbeing of the routing procedure. Along these lines, they are not appropriate for applications in VANETs. Application/Improvements: Low complexity nature advanced administration dependable routing calculation is made to ensure to lessen transmission redundancy with use of Subterranean insect State estimation with better eventual outcomes of course decision and transmission considering better request and sorting calculations in the Particle Swam Optimization (PSO) algorithm work calculation for extremely powerful VANET system with less preferred quality in evaluation with favoured data transmission over interchange QoS counts as shown by the results and satisfy QoS requirements of specific traffic using PSO in the VANET routing protocol environments. Meanwhile, an optimized function with PSO is also proposed to make scheduling decisions so that the dropped packet rates are reduced. The results of PSO algorithm simulations are better and feasible as compared to the previous techniques. Keywords: Graph Theory, Optimised Service Reliable Routing Algorithm, Particle Swam Optimization, Quality of Service, Roadside Base Stations, VANET

As to the high Peak-to-average ratio (PAR) of Space Time Block Coding Multicarrier Code Division Multiple Access (STBC MC-CDMA) systems, a simplified advantage Active Constellation Extension (ACE) and Particle Swam Optimization (PSO) algorithm is proposed. The algorithm first according to the baseband physical model of STBC MC-CDMA system, signal structure and matrix, the signal peak to average ratio of existing problems go to analyze. Then the signals are processed by the improved ACE algorithm, that QAM symbol on MC-CDMA subcarriers was be as complex signal, constellation points correspond to the constellation, the constellation points of modulation symbol are added offset ( time offset vector called trial cut peak vector ). And then using PSO algorithm to find the optimal test cut peak threshold, thus PAP of each subcarrier respectively as the trial cut peak threshold. Finally, through iterative search get PAP optimal trial minimum cut peak threshold, to meet the preset peak to average index. The simulation results and theoretical analysis indicates that the PAR of STBC MC-CDMA system can be efficiently reduced using the new algorithm, these methods can work well on the lower SNR input signals.

The advancing digitalization of vehicles and automotive systems bears many advantages for creating and enhancing comfort and safety-related systems ranging from drive-by-wire, inclusion of advanced displays, entertainment systems up to sophisticated driving assistance and autonomous driving. It, however, also contains the inherent risk of being used for purposes that are not intended for, raging from small non-authorized customizations to the possibility of full-scale cyberattacks that affect several vehicles to whole fleets and vital systems such as steering and engine control. To prevent such conditions and mitigate cybersecurity risks from affecting the safety of road traffic, testing cybersecurity must be adopted into automotive testing at a large scale. Currently, the manual penetration testing processes cannot uphold the increasing demand due to time and cost to test complex systems. We propose an approach for an architecture that (semi-)automates automotive cybersecurity test, allowing for more economic testing and therefore keeping up to the rising demand induced by new vehicle functions as well as the development towards connected and autonomous vehicles.

Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning (ML) has emerged as a transformative solution, introducing predictive and adaptive capabilities that optimize test case selection, automate defect detection, and enhance overall software quality assurance (QA). This study explores the integration of ML in software testing, addressing the challenges of traditional QA methodologies and demonstrating how AI-driven frameworks improve testing efficiency. Methodology - To investigate the impact of ML in software testing, this research adopts a systematic approach by analyzing ML-driven test automation techniques, including predictive testing, adaptive test execution, and automated test case generation. Research reviews how Google Microsoft Facebook IBM and Deep Code put ML-based quality assurance frameworks into operation. The study leverages supervised learning, reinforcement learning, deep learning, and NLP-based techniques to demonstrate how ML models predict software defects, dynamically adapt test cases, and optimize testing resources. The research tests how ML-based testing models operate within CI/CD pipelines to improve ongoing testing and deployment flow. Analysis & Results - The analysis of ML-driven software testing reveals that predictive analytics improves early defect detection rates. It helps developers spend 37% less time debugging their work. Adaptive testing models, including self-healing test scripts, minimize maintenance costs by 50% and enhance test reliability in agile environments. The integration of NLP-based test case generation increases test coverage. NLP technology enables automatic connection between requirements and test cases at 89% success rate. Additionally, reinforcement learning techniques improve test case selection, reducing redundant test executions by 43%. Our research shows different ML methods work well to lessen incorrect error alerts. ML integration for QA surely increasing defect prediction accuracy and optimizing test execution time. Findings & Contributions - This research contributes to the field of AI-driven software testing by providing a comprehensive framework for ML-based QA methodologies. Our study shows that machine learning helps find more software problems better adapts test cases and lowers testing expenses to solve present software development needs. The study also identifies critical challenges, including data availability, model interpretability, and computational overhead, suggesting future research directions in Explainable AI (XAI), hybrid AI-ML testing models, and AI-driven security testing. As the industry moves toward AI-first software testing, this research paves the way for fully autonomous QA frameworks, enabling intelligent, scalable, and cost- effective software validation techniques. Keywords - Machine Learning, Software Testing, Quality Assurance, Predictive Testing, Adaptive Testing, Test Automation, Defect Prediction, Self- Healing Test Scripts, AI-Driven QA, Reinforcement

In today’s world driven by technology and network connectivity, it’s important to know what cyber security is and be able to implement it effectively. Without a safeguard to protect it, systems, sensitive files, data, and other critical virtual resources are at risk. Whether it is IT company or not, every company deserves equal protection. With modern technologies being developed in cybersecurity, attackers are not falling behind in the same way. They follow and enhance the best hacking techniques and target the weakness of many businesses out there. Cyber security is important because military, government, finance, medical, corporate organizations, and more like these organizations store and process their data on systems or PCs. Significant amounts of data can have sensitive information, financial information, intellectual property, personal information, and other kind of data for which an unknown access can cause various concerns. Knowledge of cyber security should be must at least for those who works for these kinds of firms to protect their data from cyber attackers

Security affects every software component in different types of computing systems. Many vulnerabilities and attacks on software systems are due to security weaknesses in the software itself. During the process of software specification, development, or testing, security issues are either taken into consideration insufficiently or not at all. Such software, due to internal weaknesses is prone to new attacks. By teaching secure software engineering techniques for designing and developing software modules, students would learn systematic secure software development techniques, such as defect detecting and security testing. This paper presents a series of modules that are designed to be integrated into undergraduate software engineering courses from a security perspective. The goal of the modules is to teach the building of robust software security requirements, secure software design and development, and secure software verification through a secure software development lifecycle.

Cyber security is the most critical aspect nowadays of our technologically based lives. Government institutions, banking sectors, public and private services, nuclear power plants, power grid operators, water suppliers or waste water treatment companies use information technologies in their day-to-day operations. Everything that uses technologies are based on communication and information systems and that means that it depends on cyber security. The public and private sector each year spend millions of dollars on technologies, security software and hardware devices that will increase the cyber security inside their companies, but they are still vulnerable. The main problem of this situation is that cyber security is still usually treated as a technical aspect or technology which can be easily implemented inside the organization and this implementation will guarantee cyber security. This attitude must change, because cyber security nowadays is something more than just the technology. This article presents the taxonomy of the critical infrastructure attacks, analyzes attack vectors and attack methods used to damage critical infrastructure as well as the most common cyber security mistakes which organizations make in the cyber security field when trying to make themselves safer from vulnerabilities. The main aim of this article is to provide theoretical aspects of the cyber security management model which can be used to ensure security of critical infrastructure in an organization or company. The cyber security management model that is presented in this article is analyzed from management perspectives and is not concerned with technological aspects and products that are used to protect critical infrastructure from cyber security attacks and vulnerabilities.

In today's world, using modern technology and especially the smartphone can ease a lot our work and other activities. But also the risk is very high. We can talk about several security concerns related to Internet usage. Many individuals are not aware of the threats that this usage is creating. Having installed an IT security software it is not mandatory for the moment, but it is more than necessary in protecting our personal information, documents, pictures and any other data. Security concerns already are limiting or preventing the individuals from ordering or buying goods or services from the Internet, or from carrying out internet banking, or from providing personal information to social or professional networking services, or from communicating with public services or administrations, downloading and submitting official forms, or from downloading software or apps, music, video files, games or other data files. The routine day-to-day activities on the Internet like sending or receiving e-mails, telephoning or video calls, posting messages to social media sites or instant messaging, participating in social networks, finding information about goods and services, reading or downloading online newspaper or news, downloading software, internet banking, travel and accommodation services, job search or sending an application, communication, finding different information that we don't' usually use or downloading /listening to/ watching / playing music, films and/or games it is something that people will always do. With the help of the data provided by EUROSTAT, we analyzed the main risks concerning ICT trust, security and privacy in EU Countries.

Modern vehicles are more complex and interconnected than ever before, which also means that attack surfaces for vehicles have increased significantly. Malicious cyberattacks will not only exploit personal privacy and property, but also affect the functional safety of electrical/electronic (E/E) safety-critical systems by controlling the driving functionality, which is life-threatening. Therefore, it is necessary to conduct cybersecurity testing on vehicles to reveal and address relevant security threats and vulnerabilities. Cybersecurity standards and regulations issued in recent years, such as ISO/SAE 21434 and UNECE WP.29 regulations (R155 and R156), also emphasize the indispensability of cybersecurity verification and validation in the development lifecycle but lack specific technical details. Thus, this paper conducts a systematic and comprehensive review of the research and practice in the field of automotive cybersecurity testing, which can provide reference and advice for automotive security researchers and testers. We classify and discuss the security testing methods and testbeds in automotive engineering. Furthermore, we identify gaps and limitations in existing research and point out future challenges.

Administering energy and resource management are two vital managing components of cloud data centers. From last two decades, most of cloud data centers (CDC) are suffering from these two; the former has become a serious issue nowadays. In this paper, we focused on effective virtual machine placement (VMP). Evolutionary approach is applied to place the virtual machine in an effective way which properly utilizes the underutilized resources and reduced the active physical servers. After experiencing the performance of particle swam optimization (PSO) algorithm for combinatorial problems, a distributed PSO approach is modeled to minimize energy consumption of CDCs. The proposed PSO and DPSO algorithms are applied on VMP over large distributed cloud data centers. Experimental results of PSO and distributed PSO algorithms are presented. The model is applied with variety of placement problems with varying data center network topology. The performance of the model outperforms the traditional heuristic and several optimizations approaches.

Security testing is a key technology for software security. The testing results can reflect the relationship between software testing and software security, and they can help program designers for evaluating and improving software security. However, it is difficult to describe by mathematics the relationship between the results of software functional testing and software nonfunctional security indexes. In this paper, we propose a mathematics model (MSMAM) based on principal component analysis and multiattribute utility theory. This model can get nonfunctional security indexes by analyzing quantized results of functional tests. It can also evaluate software security and guide the effective allocation of testing resources in the process of software testing. The feasibility and effectiveness of MSMAM is verified by experiments.

Object-oriented technology has been widely used in the area of computer software. However, traditional software testing techniques cannot perform effectively on object-oriented software. The differences between object-oriented software testing and traditional software testing are compared and elaborated from three aspects: testing model, testing software and testing cases.

This paper proposes the design of specific programme dedicated for students who want to specialize in software testing at postgraduate level. The motivation behind this proposal is to encourage more higher learning institutions to offer variety of software engineering related programme, in particular software testing so that this area is recognized as another key contributor for developing competent testing professionals. Although there are many software testing professional certifications available in the market, there is still a need for offering this kind of programme in university at postgraduate level to nurture the importance of software testing in producing quality software. The proposed programme is designed based on coursework basis to mainly cater for the working students. As the structure emphasizes on mix of theories and practical knowledge with the use of suitable testing tools, thus the substance of this programme should at least cover the fundamentals knowledge of testing, every key types of testing performed today such as functional testing, security testing, performance testing, usability testing and compatibility testing as well as latest trends in testing such as testing in agile environment. It is expected that the proposed programme should be able to produce graduates that demonstrate advanced competencies in software testing and able to apply the acquired skills in producing high quality software.

Out of all the phases of Software Development Life Cycle (SDLC), the software testing phase consumes the maximum amount of time when done manually. To reduce the expenses and fasten the process of software testing, researchers have proposed many models to automate the software testing to make it efficient, scaled, and less expensive. However, to automate the software testing process, an optimal and efficient set of test cases is required which can test the software for all possible errors and failures. If test cases are generated just randomly, then the test cases may not be optimal and even if the set is large, it can skip some error checking of software. Teaching learning-based optimization (TLBO) algorithm is used in this paper to generate software test case. Approach level and combined fitness function are used to optimize the test cases such that every path including critical path can be covered. Performance of the approach is compared for both fitness functions considering triangle classification problem (TCP). It is observed that TLBO is able to generate better test cases than some existing models.KeywordsSoftware testingTeaching learning-based optimizationTest case generationApproximation levelBranch distance