Visible to the public Biblio

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2021-07-08
Dovgalyuk, Pavel, Vasiliev, Ivan, Fursova, Natalia, Dmitriev, Denis, Abakumov, Mikhail, Makarov, Vladimir.  2020.  Non-intrusive Virtual Machine Analysis and Reverse Debugging with SWAT. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). :196—203.
This paper presents SWAT - System-Wide Analysis Toolkit. It is based on open source emulation and debugging projects and implements the approaches for non-intrusive system-wide analysis and debugging: lightweight OS-agnostic virtual machine introspection, full system execution replay, non-intrusive debugging with WinDbg, and full system reverse debugging. These features are based on novel non-intrusive introspection and reverse debugging methods. They are useful for stealth debugging and analysis of the platforms with custom kernels. SWAT includes multi-platform emulator QEMU with additional instrumentation and debugging features, GUI for convenient QEMU setup and execution, QEMU plugin for non-intrusive introspection, and modified version of GDB. Our toolkit may be useful for the developers of the virtual platforms, emulators, and firmwares/drivers/operating systems. Virtual machine intospection approach does not require loading any guest agents and source code of the OS. Therefore it may be applied to ROM-based guest systems and enables using of record/replay of the system execution. This paper includes the description of SWAT components, analysis methods, and some SWAT use cases.
2021-06-28
Chen, Yi-Fan, Huang, Ding-Hsiang, Huang, Cheng-Fu, Lin, Yi-Kuei.  2020.  Reliability Evaluation for a Cloud Computer Network with Fog Computing. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :682–683.
The most recent and important developments in the field of computer networks are cloud and fog computing. In this study, modern cloud computer networks comprising computers, internet of things (IoT), fog servers, and cloud servers for data transmission, is investigated. A cloud computer networks can be modeled as a network with nodes and arcs, in which each arc represents a transmission line, and each node represents an IoT device, a fog server, or a cloud server. Each transmission line has several possible capacities and is regarded as a multistate. The network is termed a multi-state cloud computer network (MCCN). this study firstly constructs the mathematic model to elucidate the flow relationship among the IoT devices, edge servers, and cloud servers and subsequently develop an algorithm to evaluate the performance of the MCCN by calculating network reliability which is defined as the probability of the data being successfully processed by the MCCN.
2021-06-01
Zheng, Yang, Chunlin, Yin, Zhengyun, Fang, Na, Zhao.  2020.  Trust Chain Model and Credibility Analysis in Software Systems. 2020 5th International Conference on Computer and Communication Systems (ICCCS). :153–156.
The credibility of software systems is an important indicator in measuring the performance of software systems. Effective analysis of the credibility of systems is a controversial topic in the research of trusted software. In this paper, the trusted boot and integrity metrics of a software system are analyzed. The different trust chain models, chain and star, are obtained by using different methods for credibility detection of functional modules in the system operation. Finally, based on the operation of the system, trust and failure relation graphs are established to analyze and measure the credibility of the system.
2021-05-25
Bosio, Alberto, Canal, Ramon, Di Carlo, Stefano, Gizopoulos, Dimitris, Savino, Alessandro.  2020.  Cross-Layer Soft-Error Resilience Analysis of Computing Systems. 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). :79—79.
In a world with computation at the epicenter of every activity, computing systems must be highly resilient to errors even if miniaturization makes the underlying hardware unreliable. Techniques able to guarantee high reliability are associated to high costs. Early resilience analysis has the potential to support informed design decisions to maximize system-level reliability while minimizing the associated costs. This tutorial focuses on early cross-layer (hardware and software) resilience analysis considering the full computing continuum (from IoT/CPS to HPC applications) with emphasis on soft errors.
2021-05-13
Suriano, Antonio, Striccoli, Domenico, Piro, Giuseppe, Bolla, Raffele, Boggia, Gennaro.  2020.  Attestation of Trusted and Reliable Service Function Chains in the ETSI-NFV Framework. 2020 6th IEEE Conference on Network Softwarization (NetSoft). :479—486.

The new generation of digital services are natively conceived as an ordered set of Virtual Network Functions, deployed across boundaries and organizations. In this context, security threats, variable network conditions, computational and memory capabilities and software vulnerabilities may significantly weaken the whole service chain, thus making very difficult to combat the newest kinds of attacks. It is thus extremely important to conceive a flexible (and standard-compliant) framework able to attest the trustworthiness and the reliability of each single function of a Service Function Chain. At the time of this writing, and to the best of authors knowledge, the scientific literature addressed all of these problems almost separately. To bridge this gap, this paper proposes a novel methodology, properly tailored within the ETSI-NFV framework. From one side, Software-Defined Controllers continuously monitor the properties and the performance indicators taken from networking domains of each single Virtual Network Function available in the architecture. From another side, a high-level orchestrator combines, on demand, the suitable Virtual Network Functions into a Service Function Chain, based on the user requests, targeted security requirements, and measured reliability levels. The paper concludes by further explaining the functionalities of the proposed architecture through a use case.

2021-05-03
Mishra, Shachee, Polychronakis, Michalis.  2020.  Saffire: Context-sensitive Function Specialization against Code Reuse Attacks. 2020 IEEE European Symposium on Security and Privacy (EuroS P). :17–33.
The sophistication and complexity of recent exploitation techniques, which rely on memory disclosure and whole-function reuse to bypass address space layout randomization and control flow integrity, is indicative of the effect that the combination of exploit mitigations has in challenging the construction of reliable exploits. In addition to software diversification and control flow enforcement, recent efforts have focused on the complementary approach of code and API specialization to restrict further the critical operations that an attacker can perform as part of a code reuse exploit. In this paper we propose Saffire, a compiler-level defense against code reuse attacks. For each calling context of a critical function, Saffire creates a specialized and hardened replica of the function with a restricted interface that can accommodate only that particular invocation. This is achieved by applying staticargumentbinding, to eliminate arguments with static values and concretize them within the function body, and dynamicargumentbinding, which applies a narrow-scope form of data flow integrity to restrict the acceptable values of arguments that cannot be statically derived. We have implemented Saffire on top of LLVM, and applied it to a set of 11 applications, including Nginx, Firefox, and Chrome. The results of our experimental evaluation with a set of 17 real-world ROP exploits and three whole-function reuse exploits demonstrate the effectiveness of Saffire in preventing these attacks while incurring a negligible runtime overhead.
2021-04-27
Phillips, T., McJunkin, T., Rieger, C., Gardner, J., Mehrpouyan, H..  2020.  An Operational Resilience Metric for Modern Power Distribution Systems. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :334—342.

The electrical power system is the backbone of our nations critical infrastructure. It has been designed to withstand single component failures based on a set of reliability metrics which have proven acceptable during normal operating conditions. However, in recent years there has been an increasing frequency of extreme weather events. Many have resulted in widespread long-term power outages, proving reliability metrics do not provide adequate energy security. As a result, researchers have focused their efforts resilience metrics to ensure efficient operation of power systems during extreme events. A resilient system has the ability to resist, adapt, and recover from disruptions. Therefore, resilience has demonstrated itself as a promising concept for currently faced challenges in power distribution systems. In this work, we propose an operational resilience metric for modern power distribution systems. The metric is based on the aggregation of system assets adaptive capacity in real and reactive power. This metric gives information to the magnitude and duration of a disturbance the system can withstand. We demonstrate resilience metric in a case study under normal operation and during a power contingency on a microgrid. In the future, this information can be used by operators to make more informed decisions based on system resilience in an effort to prevent power outages.

2021-02-23
Liu, J., Xiao, K., Luo, L., Li, Y., Chen, L..  2020.  An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). :122—129.
With the rapid development of Internet, the issue of cyber security has increasingly gained more attention. An intrusion Detection System (IDS) is an effective technique to defend cyber-attacks and reduce security losses. However, the challenge of IDS lies in the diversity of cyber-attackers and the frequently-changing data requiring a flexible and efficient solution. To address this problem, machine learning approaches are being applied in the IDS field. In this paper, we propose an efficient scalable neural-network-based hybrid IDS framework with the combination of Host-level IDS (HIDS) and Network-level IDS (NIDS). We applied the autoencoders (AE) to NIDS and designed HIDS using word embedding and convolutional neural network. To evaluate the IDS, many experiments are performed on the public datasets NSL-KDD and ADFA. It can detect many attacks and reduce the security risk with high efficiency and excellent scalability.
2021-01-28
Fan, M., Yu, L., Chen, S., Zhou, H., Luo, X., Li, S., Liu, Y., Liu, J., Liu, T..  2020.  An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps. 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE). :253—264.

The purpose of the General Data Protection Regulation (GDPR) is to provide improved privacy protection. If an app controls personal data from users, it needs to be compliant with GDPR. However, GDPR lists general rules rather than exact step-by-step guidelines about how to develop an app that fulfills the requirements. Therefore, there may exist GDPR compliance violations in existing apps, which would pose severe privacy threats to app users. In this paper, we take mobile health applications (mHealth apps) as a peephole to examine the status quo of GDPR compliance in Android apps. We first propose an automated system, named HPDROID, to bridge the semantic gap between the general rules of GDPR and the app implementations by identifying the data practices declared in the app privacy policy and the data relevant behaviors in the app code. Then, based on HPDROID, we detect three kinds of GDPR compliance violations, including the incompleteness of privacy policy, the inconsistency of data collections, and the insecurity of data transmission. We perform an empirical evaluation of 796 mHealth apps. The results reveal that 189 (23.7%) of them do not provide complete privacy policies. Moreover, 59 apps collect sensitive data through different measures, but 46 (77.9%) of them contain at least one inconsistent collection behavior. Even worse, among the 59 apps, only 8 apps try to ensure the transmission security of collected data. However, all of them contain at least one encryption or SSL misuse. Our work exposes severe privacy issues to raise awareness of privacy protection for app users and developers.

2021-01-20
Li, H., Xie, R., Kong, X., Wang, L., Li, B..  2020.  An Analysis of Utility for API Recommendation: Do the Matched Results Have the Same Efforts? 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). :479—488.

The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.

2020-11-04
Huang, B., Zhang, P..  2018.  Software Runtime Accumulative Testing. 2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS). :218—222.

The "aging" phenomenon occurs after the long-term running of software, with the fault rate rising and running efficiency dropping. As there is no corresponding testing type for this phenomenon among conventional software tests, "software runtime accumulative testing" is proposed. Through analyzing several examples of software aging causing serious accidents, software is placed in the system environment required for running and the occurrence mechanism of software aging is analyzed. In addition, corresponding testing contents and recommended testing methods are designed with regard to all factors causing software aging, and the testing process and key points of testing requirement analysis for carrying out runtime accumulative testing are summarized, thereby providing a method and guidance for carrying out "software runtime accumulative testing" in software engineering.

2020-11-02
Ping, C., Jun-Zhe, Z..  2019.  Research on Intelligent Evaluation Method of Transient Analysis Software Function Test. 2019 International Conference on Advances in Construction Machinery and Vehicle Engineering (ICACMVE). :58–61.

In transient distributed cloud computing environment, software is vulnerable to attack, which leads to software functional completeness, so it is necessary to carry out functional testing. In order to solve the problem of high overhead and high complexity of unsupervised test methods, an intelligent evaluation method for transient analysis software function testing based on active depth learning algorithm is proposed. Firstly, the active deep learning mathematical model of transient analysis software function test is constructed by using association rule mining method, and the correlation dimension characteristics of software function failure are analyzed. Then the reliability of the software is measured by the spectral density distribution method of software functional completeness. The intelligent evaluation model of transient analysis software function testing is established in the transient distributed cloud computing environment, and the function testing and reliability intelligent evaluation are realized. Finally, the performance of the transient analysis software is verified by the simulation experiment. The results show that the accuracy of the software functional integrity positioning is high and the intelligent evaluation of the transient analysis software function testing has a good self-adaptability by using this method to carry out the function test of the transient analysis software. It ensures the safe and reliable operation of the software.

Chong, T., Anu, V., Sultana, K. Z..  2019.  Using Software Metrics for Predicting Vulnerable Code-Components: A Study on Java and Python Open Source Projects. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :98–103.

Software vulnerabilities often remain hidden until an attacker exploits the weak/insecure code. Therefore, testing the software from a vulnerability discovery perspective becomes challenging for developers if they do not inspect their code thoroughly (which is time-consuming). We propose that vulnerability prediction using certain software metrics can support the testing process by identifying vulnerable code-components (e.g., functions, classes, etc.). Once a code-component is predicted as vulnerable, the developers can focus their testing efforts on it, thereby avoiding the time/effort required for testing the entire application. The current paper presents a study that compares how software metrics perform as vulnerability predictors for software projects developed in two different languages (Java vs Python). The goal of this research is to analyze the vulnerability prediction performance of software metrics for different programming languages. We designed and conducted experiments on security vulnerabilities reported for three Java projects (Apache Tomcat 6, Tomcat 7, Apache CXF) and two Python projects (Django and Keystone). In this paper, we focus on a specific type of code component: Functions. We apply Machine Learning models for predicting vulnerable functions. Overall results show that software metrics-based vulnerability prediction is more useful for Java projects than Python projects (i.e., software metrics when used as features were able to predict Java vulnerable functions with a higher recall and precision compared to Python vulnerable functions prediction).

2020-10-12
Brenner, Bernhard, Weippl, Edgar, Ekelhart, Andreas.  2019.  Security Related Technical Debt in the Cyber-Physical Production Systems Engineering Process. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 1:3012–3017.

Technical debt is an analogy introduced in 1992 by Cunningham to help explain how intentional decisions not to follow a gold standard or best practice in order to save time or effort during creation of software can later on lead to a product of lower quality in terms of product quality itself, reliability, maintainability or extensibility. Little work has been done so far that applies this analogy to cyber physical (production) systems (CP(P)S). Also there is only little work that uses this analogy for security related issues. This work aims to fill this gap: We want to find out which security related symptoms within the field of cyber physical production systems can be traced back to TD items during all phases, from requirements and design down to maintenance and operation. This work shall support experts from the field by being a first step in exploring the relationship between not following security best practices and concrete increase of costs due to TD as consequence.

2020-09-28
Mohammadi, Mahmoud, Chu, Bill, Richter Lipford, Heather.  2019.  Automated Repair of Cross-Site Scripting Vulnerabilities through Unit Testing. 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). :370–377.
Many web applications are vulnerable to Cross Site Scripting (XSS) attacks enabling attackers to steal sensitive information and commit frauds. Much research in this area have focused on detecting vulnerable web pages using static and dynamic program analysis. The best practice to prevent XSS vulnerabilities is to encode untrusted dynamic content. However, a common programming error is the use of a wrong type of encoder to sanitize untrusted data, leaving the application vulnerable. We propose a new approach that can automatically fix this common type of XSS vulnerability in many situations. This approach is integrated into the software maintenance life cycle through unit testing. Vulnerable codes are refactored to reflect the suggested encoder and then verified using an attack evaluating mechanism to find a proper repair. Evaluation of this approach has been conducted on an open source medical record application with over 200 web pages written in JSP.
2020-07-06
Frias, Alex Davila, Yodo, Nita, Yadav, Om Prakash.  2019.  Mixed-Degradation Profiles Assessment of Critical Components in Cyber-Physical Systems. 2019 Annual Reliability and Maintainability Symposium (RAMS). :1–6.
This paper presents a general model to assess the mixed-degradation profiles of critical components in a Cyber-Physical System (CPS) based on the reliability of its critical physical and software components. In the proposed assessment, the cyber aspect of a CPS was approached from a software reliability perspective. Although extensive research has been done on physical components degradation and software reliability separately, research for the combined physical-software systems is still scarce. The non-homogeneous Poisson Processes (NHPP) software reliability models are deemed to fit well with the real data and have descriptive and predictive abilities, which could make them appropriate to estimate software components reliability. To show the feasibility of the proposed approach, a case study for mixed-degradation profiles assessment is presented with n physical components and one major software component forming a critical subsystem in CPS. Two physical components were assumed to have different degradation paths with the dependency between them. Series and parallel structures were investigated for physical components. The software component failure data was taken from a wireless network switching center and fitted into a Weibull software reliability model. The case study results revealed that mix-degradation profiles of physical components, combined with software component profile, produced a different CPS reliability profile.
2020-04-03
Jabeen, Gul, Ping, Luo.  2019.  A Unified Measurable Software Trustworthy Model Based on Vulnerability Loss Speed Index. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :18—25.

As trust becomes increasingly important in the software domain. Due to its complex composite concept, people face great challenges, especially in today's dynamic and constantly changing internet technology. In addition, measuring the software trustworthiness correctly and effectively plays a significant role in gaining users trust in choosing different software. In the context of security, trust is previously measured based on the vulnerability time occurrence to predict the total number of vulnerabilities or their future occurrence time. In this study, we proposed a new unified index called "loss speed index" that integrates the most important variables of software security such as vulnerability occurrence time, number and severity loss, which are used to evaluate the overall software trust measurement. Based on this new definition, a new model called software trustworthy security growth model (STSGM) has been proposed. This paper also aims at filling the gap by addressing the severity of vulnerabilities and proposed a vulnerability severity prediction model, the results are further evaluated by STSGM to estimate the future loss speed index. Our work has several features such as: (1) It is used to predict the vulnerability severity/type in future, (2) Unlike traditional evaluation methods like expert scoring, our model uses historical data to predict the future loss speed of software, (3) The loss metric value is used to evaluate the risk associated with different software, which has a direct impact on software trustworthiness. Experiments performed on real software vulnerability datasets and its results are analyzed to check the correctness and effectiveness of the proposed model.

2020-03-23
Karlsson, Linus, Paladi, Nicolae.  2019.  Privacy-Enabled Recommendations for Software Vulnerabilities. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :564–571.
New software vulnerabilities are published daily. Prioritizing vulnerabilities according to their relevance to the collection of software an organization uses is a costly and slow process. While recommender systems were earlier proposed to address this issue, they ignore the security of the vulnerability prioritization data. As a result, a malicious operator or a third party adversary can collect vulnerability prioritization data to identify the security assets in the enterprise deployments of client organizations. To address this, we propose a solution that leverages isolated execution to protect the privacy of vulnerability profiles without compromising data integrity. To validate an implementation of the proposed solution we integrated it with an existing recommender system for software vulnerabilities. The evaluation of our implementation shows that the proposed solution can effectively complement existing recommender systems for software vulnerabilities.
2020-03-09
Song, Zekun, Wang, Yichen, Zong, Pengyang, Ren, Zhiwei, Qi, Di.  2019.  An Empirical Study of Comparison of Code Metric Aggregation Methods–on Embedded Software. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :114–119.

How to evaluate software reliability based on historical data of embedded software projects is one of the problems we have to face in practical engineering. Therefore, we establish a software reliability evaluation model based on code metrics. This evaluation technique requires the aggregation of software code metrics into project metrics. Statistical value methods, metric distribution methods, and econometric methods are commonly-used aggregation methods. What are the differences between these methods in the software reliability evaluation process, and which methods can improve the accuracy of the reliability assessment model we have established are our concerns. In view of these concerns, we conduct an empirical study on the application of software code metric aggregation methods based on actual projects. We find the distribution of code metrics for the projects under study. Using these distribution laws, we optimize the aggregation method of code metrics and improve the accuracy of the software reliability evaluation model.

2020-03-02
Kharchenko, Vyacheslav, Ponochovniy, Yuriy, Abdulmunem, Al-Sudani Mustafa Qahtan, Shulga, Iryna.  2019.  AvTA Based Assessment of Dependability Considering Recovery After Failures and Attacks on Vulnerabilities. 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). 2:1036–1040.

The paper describes modification of the ATA (Attack Tree Analysis) technique for assessment of instrumentation and control systems (ICS) dependability (reliability, availability and cyber security) called AvTA (Availability Tree Analysis). The techniques FMEA, FMECA and IMECA applied to carry out preliminary semi-formal and criticality oriented analysis before AvTA based assessment are described. AvTA models combine reliability and cyber security subtrees considering probabilities of ICS recovery in case of hardware (physical) and software (design) failures and attacks on components casing failures. Successful recovery events (SREs) avoid corresponding failures in tree using OR gates if probabilities of SRE for assumed time are more than required. Case for dependability AvTA based assessment (model, availability function and technology of decision-making for choice of component and system parameters) for smart building ICS (Building Automation Systems, BAS) is discussed.

2020-02-10
Yang, Jinqiu, Tan, Lin, Peyton, John, A Duer, Kristofer.  2019.  Towards Better Utilizing Static Application Security Testing. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). :51–60.
Static application security testing (SAST) detects vulnerability warnings through static program analysis. Fixing the vulnerability warnings tremendously improves software quality. However, SAST has not been fully utilized by developers due to various reasons: difficulties in handling a large number of reported warnings, a high rate of false warnings, and lack of guidance in fixing the reported warnings. In this paper, we collaborated with security experts from a commercial SAST product and propose a set of approaches (Priv) to help developers better utilize SAST techniques. First, Priv identifies preferred fix locations for the detected vulnerability warnings, and group them based on the common fix locations. Priv also leverages visualization techniques so that developers can quickly investigate the warnings in groups and prioritize their quality-assurance effort. Second, Priv identifies actionable vulnerability warnings by removing SAST-specific false positives. Finally, Priv provides customized fix suggestions for vulnerability warnings. Our evaluation of Priv on six web applications highlights the accuracy and effectiveness of Priv. For 75.3% of the vulnerability warnings, the preferred fix locations found by Priv are identical to the ones annotated by security experts. The visualization based on shared preferred fix locations is useful for prioritizing quality-assurance efforts. Priv reduces the rate of SAST-specific false positives significantly. Finally, Priv is able to provide fully complete and correct fix suggestions for 75.6% of the evaluated warnings. Priv is well received by security experts and some features are already integrated into industrial practice.
Cetin, Cagri, Goldgof, Dmitry, Ligatti, Jay.  2019.  SQL-Identifier Injection Attacks. 2019 IEEE Conference on Communications and Network Security (CNS). :151–159.
This paper defines a class of SQL-injection attacks that are based on injecting identifiers, such as table and column names, into SQL statements. An automated analysis of GitHub shows that 15.7% of 120,412 posted Java source files contain code vulnerable to SQL-Identifier Injection Attacks (SQL-IDIAs). We have manually verified that some of the 18,939 Java files identified during the automated analysis are indeed vulnerable to SQL-ID IAs, including deployed Electronic Medical Record software for which SQL-IDIAs enable discovery of confidential patient information. Although prepared statements are the standard defense against SQL injection attacks, existing prepared-statement APIs do not protect against SQL-IDIAs. This paper therefore proposes and evaluates an extended prepared-statement API to protect against SQL-IDIAs.
2019-10-14
Angelini, M., Blasilli, G., Borrello, P., Coppa, E., D’Elia, D. C., Ferracci, S., Lenti, S., Santucci, G..  2018.  ROPMate: Visually Assisting the Creation of ROP-based Exploits. 2018 IEEE Symposium on Visualization for Cyber Security (VizSec). :1–8.

Exploits based on ROP (Return-Oriented Programming) are increasingly present in advanced attack scenarios. Testing systems for ROP-based attacks can be valuable for improving the security and reliability of software. In this paper, we propose ROPMATE, the first Visual Analytics system specifically designed to assist human red team ROP exploit builders. In contrast, previous ROP tools typically require users to inspect a puzzle of hundreds or thousands of lines of textual information, making it a daunting task. ROPMATE presents builders with a clear interface of well-defined and semantically meaningful gadgets, i.e., fragments of code already present in the binary application that can be chained to form fully-functional exploits. The system supports incrementally building exploits by suggesting gadget candidates filtered according to constraints on preserved registers and accessed memory. Several visual aids are offered to identify suitable gadgets and assemble them into semantically correct chains. We report on a preliminary user study that shows how ROPMATE can assist users in building ROP chains.

2019-07-01
Carrasco, A., Ropero, J., Clavijo, P. Ruiz de, Benjumea, J., Luque, A..  2018.  A Proposal for a New Way of Classifying Network Security Metrics: Study of the Information Collected through a Honeypot. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :633–634.

Nowadays, honeypots are a key tool to attract attackers and study their activity. They help us in the tasks of evaluating attacker's behaviour, discovering new types of attacks, and collecting information and statistics associated with them. However, the gathered data cannot be directly interpreted, but must be analyzed to obtain useful information. In this paper, we present a SSH honeypot-based system designed to simulate a vulnerable server. Thus, we propose an approach for the classification of metrics from the data collected by the honeypot along 19 months.

2018-11-14
Shao, Y., Liu, B., Li, G., Yan, R..  2017.  A Fault Diagnosis Expert System for Flight Control Software Based on SFMEA and SFTA. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :626–627.
Many accidents occurred frequently in aerospace applications, traditional software reliability analysis methods are not enough for modern flight control software. Developing a comprehensive, effective and intelligent method for software fault diagnosis is urgent for airborne software engineering. Under this background, we constructed a fault diagnosis expert system for flight control software which combines software failure mode and effect analysis with software fault tree analysis. To simplify the analysis, the software fault knowledge of four modules is acquired by reliability analysis methods. Then by taking full advantage of the CLIPS shell, knowledge representation and inference engine can be realized smoothly. Finally, we integrated CLIPS into VC++ to achieve visualization, fault diagnosis and inference for flight control software can be performed in the human-computer interaction interface. The results illustrate that the system is able to diagnose software fault, analysis the reasons and present some reasonable solutions like a human expert.