Search-based Multi-Vulnerability Testing of XML Injections in Web Applications

in Empirical Software Engineering (2019), 24(6), 36963729

Modern web applications often interact with internal web services, which are not directly accessible to users. However, malicious user inputs can be used to exploit security vulnerabilities in web ... [more ▼]

Modern web applications often interact with internal web services, which are not directly accessible to users. However, malicious user inputs can be used to exploit security vulnerabilities in web services through the application front-ends. Therefore, testing techniques have been proposed to reveal security flaws in the interactions with back-end web services, e.g., XML Injections (XMLi). Given a potentially malicious message between a web application and web services, search-based techniques have been used to find input data to mislead the web application into sending such a message, possibly compromising the target web service. However, state-of-the-art techniques focus on (search for) one single malicious message at a time. Since, in practice, there can be many different kinds of malicious messages, with only a few of them which can possibly be generated by a given front-end, searching for one single message at a time is ineffective and may not scale. To overcome these limitations, we propose a novel co-evolutionary algorithm (COMIX) that is tailored to our problem and uncover multiple vulnerabilities at the same time. Our experiments show that COMIX outperforms a single-target search approach for XMLi and other multi-target search algorithms originally defined for white-box unit testing. [less ▲]

A Machine Learning-Driven Evolutionary Approach for Testing Web Application Firewalls

in IEEE Transactions on Reliability (2018), 67(3), 733-757

Web application firewalls (WAF) are an essential protection mechanism for online software systems. Because of the relentless flow of new kinds of attacks as well as their increased sophistication, WAFs ... [more ▼]

Web application firewalls (WAF) are an essential protection mechanism for online software systems. Because of the relentless flow of new kinds of attacks as well as their increased sophistication, WAFs have to be updated and tested regularly to prevent attackers from easily circumventing them. In this paper, we focus on testing WAFs for SQL injection attacks, but the general principles and strategy we propose can be adapted to other contexts. We present ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them. Initially, ML-Driven automatically generates a diverse set of attacks and submit them to the system being protected by the target WAF. Then, ML-Driven selects attacks that exhibit patterns (substrings) associated with bypassing the WAF and evolve them to generate new successful bypassing attacks. Machine learning is used to incrementally learn attack patterns from previously generated attacks according to their testing results, i.e., if they are blocked or bypass the WAF. We implemented ML-Driven in a tool and evaluated it on ModSecurity, a widely used open-source WAF, and a proprietary WAF protecting a financial institution. Our empirical results indicate that ML-Driven is effective and efficient at generating SQL injection attacks bypassing WAFs and identifying attack patterns. [less ▲]

An Investigation of Compression Techniques to Speed up Mutation Testing

in Proceedings of 11th IEEE Conference on Software Testing, Validation and Verification, 2018 (2018)

Mutation testing is widely considered as a high-end test coverage criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of ... [more ▼]

Mutation testing is widely considered as a high-end test coverage criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, in practice, the scalability issue remains. In this paper, we explore whether we can use compression techniques to improve the efficiency of strong mutation based on weak mutation information. Our investigation is centred around six mutation compression strategies that we have devised. More specifically, we adopt overlapped grouping and Formal Concept Analysis (FCA) to cluster mutants and test cases based on the reachability (code covergae) and necessity (weak mutation) conditions. Moreover, we leverage mutation knowledge (mutation locations and mutation operator types) during compression. To evaluate our method, we conducted a study on 20 open source Java projects using manually written tests. We also compare our method with pure random sampling and weak mutation. The overall results show that mutant compression techniques are a better choice than random sampling and weak mutation in practice: they can effectively speed up strong mutation 6.3 to 94.3 times with an accuracy of >90%. [less ▲]

A Search-based Approach for Accurate Identification of Log Message Formats

in Proceedings of the 26th IEEE/ACM International Conference on Program Comprehension (ICPC ’18) (2018)

Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the ... [more ▼]

Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the actual events recorded in the log. Each event is denoted by a log message, which is composed of a fixed part-called (event) template-that is the same for all occurrences of the same event type, and a variable part, which may vary with each event occurrence. The formats of log messages, in complex and evolving systems, have numerous variations, are typically not entirely known, and change on a frequent basis; therefore, they need to be identified automatically. The log message format identification problem deals with the identification of the different templates used in the messages of a log. Any solution to this problem has to generate templates that meet two main goals: generating templates that are not too general, so as to distinguish different events, but also not too specific, so as not to consider different occurrences of the same event as following different templates; however, these goals are conflicting. In this paper, we present the MoLFI approach, which recasts the log message identification problem as a multi-objective problem. MoLFI uses an evolutionary approach to solve this problem, by tailoring the NSGA-II algorithm to search the space of solutions for a Pareto optimal set of message templates. We have implemented MoLFI in a tool, which we have evaluated on six real-world datasets, containing log files with a number of entries ranging from 2K to 300K. The experiments results show that MoLFI extracts by far the highest number of correct log message templates, significantly outperforming two state-of-the-art approaches on all datasets. [less ▲]

LIPS vs MOSA: a Replicated Empirical Study on Automated Test Case Generation

in International Symposium on Search Based Software Engineering (SSBSE) 2017 (2017, September 09)

Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The ... [more ▼]

Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The proponents of LIPS, a novel single-target test generator, conducted a preliminary empirical study to compare their approach with MOSA, an alternative many-objective test generator. However, their empirical investigation suffers from several external and internal validity threats, does not consider complex programs with many branches and does not include any qualitative analysis to interpret the results. In this paper, we report the results of a replication of the original study designed to address its major limitations and threats to validity. The new findings draw a completely different picture on the pros and cons of single-target vs many-objective approaches to test case generation. [less ▲]

Java Unit Testing Tool Competition — Fifth Round

in 10th International Workshop on Search- Based Software Testing (SBST) 2017 (2017, May 22)

After four successful JUnit tool competitions, we report on the achievements of a new Java Unit Testing Tool Competition. This 5th contest introduces statistical analyses in the benchmark infrastructure ... [more ▼]

After four successful JUnit tool competitions, we report on the achievements of a new Java Unit Testing Tool Competition. This 5th contest introduces statistical analyses in the benchmark infrastructure and has been validated with significance against the results of the previous 4th edition. Overall, the competition evaluates four automated JUnit testing tools taking as baseline human written test cases from real projects. The paper details the modifications performed to the methodology and provides full results of the competition. [less ▲]

Speeding-Up Mutation Testing via Data Compression and State Infection

in IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) 2017 (2017, March 13)

Mutation testing is widely considered as a high-end test criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing ... [more ▼]

Mutation testing is widely considered as a high-end test criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, its scalability issue remains in practice. In this paper, we introduce a novel method to speed up mutation testing based on state infection information. In addition to filtering out uninfected test executions, we further select a subset of mutants and a subset of test cases to run leveraging data-compression techniques. In particular, we adopt Formal Concept Analysis (FCA) to group similar mutants together and then select test cases to cover these mutants. To evaluate our method, we conducted an experimental study on six open source Java projects. We used EvoSuite to automatically generate test cases and to collect mutation data. The initial results show that our method can reduce the execution time by 83.93% with only 0.257% loss in precision. [less ▲]

Software-Based Energy Profiling of Android Apps: Simple, Efficient and Reliable?

in Proceedings of the 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2017) (2017, February 21)

Modeling the power profile of mobile applications is a crucial activity to identify the causes behind energy leaks. To this aim, researchers have proposed hardware-based tools as well as model-based and ... [more ▼]

Modeling the power profile of mobile applications is a crucial activity to identify the causes behind energy leaks. To this aim, researchers have proposed hardware-based tools as well as model-based and software-based techniques to approximate the actual energy profile. However, all these solutions present their own advantages and disadvantages. Hardware-based tools are highly precise, but at the same time their use is bound to the acquisition of costly hardware components. Model-based tools require the calibration of parameters needed to correctly create a model on a specific hardware device. Software-based approaches do not need any hardware components, but they rely on battery measurements and, thus, they are hardware-assisted. These tools are cheaper and easier to use than hardware-based tools, but they are believed to be less precise. In this paper, we take a deeper look at the pros and cons of software-based solutions investigating to what extent their measurements depart from hardware-based solutions. To this aim, we propose a software-based tool named PETRA that we compare with the hardware-based MONSOON toolkit on 54 Android apps. The results show that PETRA performs similarly to MONSOON despite not using any sophisticated hardware components. In fact, in all the apps the mean relative error with respect to MONSOON is lower than 0.05. Moreover, for 95% of the analyzed methods the estimation error is within 5% of the actual values measured using the hardware-based toolkit. [less ▲]