Gotowa bibliografia na temat „AFLGo”

Directed greybox fuzzing guides fuzzers to explore specific objective code areas and has achieved good performance in some scenarios such as patch testing. However, if there are multiple objective code to explore, existing directed greybox fuzzers, such as AFLGo and Hawkeye, often neglect some targets because they use harmonic means of distance and prefers to test those targets with shorter reachable path. Besides, existing directed greybox fuzzers cannot calculate the accurate distance due to indirect calls in the program. In addition, existing directed greybox fuzzers fail to address the exploration and exploitation problem and have poor efficiency in seed scheduling. To address these problems, we propose a dynamic seed distance calculation scheme, it increase the seed distance dynamically when the reachable path encounter indirect call. Besides, the seed distance calculation can deal with the bias problem in multi-targets scenarios. With the seed distance calculation method, we propose a new seed scheduling algorithm based on the upper confidence bound algorithm to deal with the exploration and exploitation problem in drected greybox fuzzing. We implemented a prototype RLTG and evaluate it on real-world programs. Evaluation of our prototype shows that our approach outperforms a state-of-the-art directed fuzzer AFLGo. On the multi-targets benchmark Magma, RLTG reproduces bugs with 6.9x speedup and finds 66.7% more bugs than AFLGo.

In recent years, fuzzing has become a powerful tool for security researchers to uncover security vulnerabilities. It is used to discover software vulnerabilities by continuously generating malformed inputs to trigger bugs. Directed grey-box fuzzing has also been widely used in the verification of patch testing and in vulnerability reproduction. For directed grey-box fuzzing, the core problem is to make test cases reach the target and trigger vulnerabilities faster. Selecting seeds that are closer to the target site to be mutated first is an effective method. For this purpose, the DGF calculates the distance between the execution path and the target site by a specific algorithm. However, as time elapses in the execution process, the seeds covering a larger amount of basic blocks may be overlooked due to their long distances. At the same time, directed fuzzing often ignores the impact of coverage on test efficiency, resulting in a local optimum problem without accumulating enough valuable test cases. In this paper, we analyze and discuss these problems and propose SwitchFuzz, a fuzzer that can switch short-term goals during execution. SwitchFuzz keeps shortening the distance of test cases to reach the target point when it performs well and prioritizes reaching the target point. When positive feedback is not achieved over a period of time, SwitchFuzz tries to explore more possibilities. We compared the efficiency of SwitchFuzz with that of AFLGO in setting single target and multiple targets for crash recurrence in our experiments, respectively. The results show that SwitchFuzz produces a significant improvement over AFLGO in both the speed and the probability of triggering a specified crash. SwitchFuzz can discover more edges than AFLGO in the same amount of time and can generate seeds with smaller distances.

Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing efficiency. However, the sample generation algorithm used in target-oriented vulnerability detection technology has some problems, such as weak guidance, weak sample penetration, and difficult sample generation. This paper proposes a new target-oriented fuzzer, PSOFuzzer, that uses particle swarm optimization to generate samples. PSOFuzzer can quickly learn high-quality features in historical samples and implant them into new samples that can be led to execute the suspected vulnerable point. The experimental results show that PSOFuzzer can generate more samples in the test process to reach the target point and can trigger vulnerabilities with 79% and 423% higher probability than AFLGo and Sidewinder, respectively, on tested software programs.

Fuzz testing is the process of testing programs by continually producing unique inputs in order to detect and identify security flaws. It is often used in vulnerability mining. The most prevalent fuzzing approach is grey-box fuzzing, which combines lightweight code instrumentation with data-feedback-driven generation of fresh program input seeds. AFL (American Fuzzy Lop) is an outstanding grey-box fuzzing tool that is well known for its quick fork server execution, dependable genetic algorithm, and numerous mutation techniques. AFLGO proposes and executes power scheduling based on a simulated annealing process for a more appropriate energy allocation to seeds, however it is neither reliable nor successful. To tackle this issue, we offer an energy-dynamic scheduling strategy based on the algorithm of the fruit fly. Adjusting the energy of the seeds dynamically controls the production of test cases. The findings demonstrate that the approach suggested in this research can test the target region more rapidly and thoroughly and has a high application value for patch testing and vulnerability replication.

Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components. It is a promising way to make better use of available resources, especially in testing large-scale programs. However, by observing the state-of-the-art-directed fuzzing engine (AFLGo), we argue that there are two universal limitations, the balance problem between the exploration and the exploitation and the blindness in mutation toward the target code areas. In this paper, we present a new prototype RDFuzz to address these two limitations. In RDFuzz, we first introduce the frequency-guided strategy in the exploration and improve its accuracy by adopting the branch-level instead of the path-level frequency. Then, we introduce the input-distance-based evaluation strategy in the exploitation stage and present an optimized mutation to distinguish and protect the distance sensitive input content. Moreover, an intertwined testing schedule is leveraged to perform the exploration and exploitation in turn. We test RDFuzz on 7 benchmarks, and the experimental results demonstrate that RDFuzz is skilled at driving the program toward the target code areas, and it is not easily stuck by the balance problem of the exploration and the exploitation.

Directed fuzzing aims to focus on fuzzing specific locations within a target program to enhance the efficiency of vulnerability discovery. However, directed fuzzing may yield fewer vulnerabilities and obtain lower code coverage when the specified locations have little to no vulnerabilities. Additionally, the existing directed fuzzing approaches often overlook the differences in variable values when calculating distances between seeds and specific locations. In order to address these issues, this paper introduces RegFuzz, a method that improves seed scheduling in directed fuzzing. RegFuzz utilizes a linear regression model to predict the effectiveness of a seed and allocates more fuzzing opportunities to efficient seeds. Specifically, first, RegFuzz defines several labels with the corresponding trainable weights for each seed. These labels encompass seed coverage, crash efficiency, seed distance, and more. In the calculation of seed distance, RegFuzz takes into account not only the basic block distance but also the variable distance contained within those basic blocks. Second, the linear regression model continually optimizes the label weights during fuzzing, and these optimized weights are employed to predict the effectiveness of seeds. In comparison with AFLGo, AFL, and AFL++, RegFuzz demonstrates higher code coverage and a more efficient bug-finding capability across seven real-world open-source programs.

Greybox Fuzzing is the most reliable and essentially powerful technique for automated software testing. Notwithstanding, a majority of greybox fuzzers are not effective in directed fuzzing, for example, towards complicated patches, as well as towards suspicious and critical sites. To overcome these limitations of greybox fuzzers, Directed Greybox Fuzzing (DGF) approaches were recently proposed. Current DGFs are powerful and efficient approaches that can compete with Coverage-Based Fuzzers. Nevertheless, DGFs neglect to accomplish stability between usefulness and proficiency, and random mutations make it hard to handle complex paths. To alleviate this problem, we propose an innovative methodology, a target-oriented hybrid fuzzing tool that utilizes a fuzzer and dynamic symbolic execution (also referred to as a concolic execution) engine. Our proposed method aims to generate inputs that can quickly reach the target sites in each sequence and trigger potential hard-to-reach vulnerabilities in the program binary. Specifically, to dive deep into the target binary, we designed a proposed technique named BugMiner, and to demonstrate the capability of our implementation, we evaluated it comprehensively on bug hunting and crash reproduction. Evaluation results showed that our proposed implementation could not only trigger hard-to-reach bugs 3.1, 4.3, 2.9, 2.0, 1.8, and 1.9 times faster than Hawkeye, AFLGo, AFL, AFLFast, QSYM, and ParmeSan respectively but also scale to several real-world programs.

The purpose of this study was to develop a four-step cascade drug-release system for transcatheter arterial chemoembolization (TACE) therapeutic applications according to disease-driven and patient-focused design theories. The four steps underlying these strategies involve the blockage of nutrient supply, nanoparticles, codelivery and the cell cytotoxic effect. Calibrated spherical gellan gum (GG) and nanoparticle-containing gellan gum microspheres were prepared using a water-in-oil emulsification method. Self-assembled nanoparticles featuring amine-functionalized graphene oxide (AFGO) as the doxorubicin (Dox) carrier were prepared. The results confirm that, as a drug carrier, AFGO–Dox nanoparticles can facilitate the transport of doxorubicin into HepG2 liver cancer cells. Subsequently, AFGO–Dox was introduced into gellan gum (GG) microspheres, thus forming GG/AFGO–Dox microspheres with a mean size of 200–700 μm. After a drug release experiment lasting 28 days, the amount of doxorubicin released from 674 and 226 μm GG/AFGO–Dox microspheres was 2.31 and 1.18 μg/mg, respectively. GG/AFGO–Dox microspheres were applied in a rabbit ear embolization model, where ischemic necrosis was visible on the ear after 12 days. Our aim for the future is to provide better embolization agents for transcatheter arterial chemoembolization (TACE) using this device.

ObjectiveThis study aims to measure the extent of illicit cigarette consumption from single stick sales, to determine the nature and types of illicit cigarettes present in Ghana, and to identify the factors associated with illicit cigarette consumption in Ghana.DesignA cross-sectional study using empty cigarette packs generated by 1 day’s single stick cigarette sales collected from cigarette vendors.SettingFive large cities (Accra, Kumasi, Takoradi, Tamale and Bolgatanga) and three border towns (Aflao, Paga/Hamele and Elubo) in the northern, middle and coastal belt of Ghana.Procedure and participantsTen areas were randomly selected in each city/town, and all shops selling cigarettes within 1 km of the central point were surveyed.Outcome measures(1) Estimates of the share of illicit cigarette packs in the total cigarette sales from vendors selling single stick cigarettes in Ghana; (2) nature and types of illicit cigarette packs; (3) factors associated with illicit cigarette sales in Ghana.ResultsOf a total of 4461 packs, about 20% (95% CI 18.3 to 20.7) were found to be illicit. Aflao (Ghana-Togo border) and Tamale (Ghana-Burkina Faso border) had the highest percentage of illicit cigarette sales at 99% and 46%, respectively (p<0.001). Over half of the illicit packs originated from Togo (51%), followed by Nigeria (15%) and then Cote d’Ivoire (10%). Adjusted and unadjusted logistic regression models indicated that convenience stores, border towns, pack price and the northern zone had higher odds of illicit cigarette sales.ConclusionTo effectively tackle illicit cigarettes, market surveillance and strengthening supply chain control are required, particularly at the border towns and the northern region of the country.

Directed testing is a technique to analyze user-specified target locations in the program. It reduces the time and effort of developers by excluding irrelevant parts of the program from testing and focusing on reaching the target location. Existing tools for directed testing employ either symbolic execution with heavy-weight program analysis or fuzz testing mixed with hand-tuned heuristics. In this thesis, we explore the feasibility of using a data-driven approach for directed testing. We aim to leverage the data generated by fuzz testing tools. We train an agent on the data collected from the fuzzers to learn a better mutation strategy based on the program input. The agent then directs the fuzzer towards the target location by instructing the optimal action for each program input. We use reinforcement learning based algorithms to train the agent. We implemented a prototype of our approach and tested it on synthetic as well as real-world programs. We evaluated and compared different reward functions. In our experiments, we observe that for simple synthetic programs, our approach can reach the target location with fewer mutations compared to AFL and AFLGo that employ random mutations. However, for complex programs, the results are mixed. No one technique can perform consistently for all programs.

碩士
中國文化大學
紡織產業碩士專班
104
The purpose of this study is the effect of purchase intention on country of manufacture、corporation image、product evaluation. And I do marketing strategy analysis that is AFUGO Brand of Fu Hsun Fiber Industrial Co.,Ltd. The results confirmed by questionnaires: (1) Corporate image for the purchase intention has a positive impact, when the company has a positive image, help to improve the consumer' purchase intention.(2)Products manufactured in Taiwan for the purchase intention has a positive impact, when the product is labeled as made in Taiwan , help to improve the consumer' purchase intention. (3) Product evaluation for the purchase intention has a positive impact, when the product evaluation is better , help to improve the consumer' purchase intention.

Selecting the appropriate level of filtration for a gas turbine helps to minimize overall unit costs and maximize net revenue. When selecting a filter type and configuration, one must consider the initial costs, operational costs, and ongoing maintenance costs for both the filter and corresponding impacts on unit performance. Calculations are complex, and a fully functional framework is needed to properly account for all aspects of the life cycle and provide an opportunity to optimize filter selection and water wash scenarios for specific plant operating conditions. Decisions can generally be based on several different criteria. For instance, one may wish to minimize maintenance costs, maximize revenue, minimize fuel consumption, etc. For criteria that can be expressed in monetary terms, Life Cycle Cost Analysis (LCCA) is a means to simultaneously consider all criteria and reduce them to a single parameter for optimization using present value arithmetic. To be practically applied, the analysis must include all the significant inputs that would have an impact on the relative comparison between alternatives, while excluding minor inputs that would unduly add to complexity. This paper provides an integrated, quantitative, and transparent approach to life cycle cost analysis for gas turbine inlet filtration. Most prior art tends to focus either on how to perform the life cycle cost analysis (with simplified assumptions on the impact of filtration on performance), or on a specific technical aspect of filtration such as filter efficiency and performance, the impact of dust on compressor blading and fouling, or the impact of fouling on overall gas turbine performance. Many of these studies provide useful insight into specific aspects of gas turbine degradation due to fouling, but make simplifying assumptions that can lead to inaccuracies in application. By heavily leveraging prior work, this paper provides the reader with an overview of all aspects of the functionality required to perform such a life cycle analysis for gas turbine filtration. This work also serves as a technical summary of the underlying physics models that lead to the development of EPRI’s Air Filter Life-Cycle Optimizer (AFLCO) software. The software tool provides a method to account for the influence of gas turbine type, operating conditions, load profile, filtration choices, and wash type and frequency on overall life-cycle costs. The AFLCO tool is focused on guiding the user to make optimum filter selections and water wash scheduling, accounting for all the significant parameters that affect the economic outcome. Revenue and cost quantities are considered simultaneously to determine the net present value of gross revenue minus filtration and water wash costs over a multiple year analysis period. The user defines the scenarios and the software displays the net present value (NPV) and present value difference between the scenarios. The preferred configuration from an LCCA perspective is that which yields the highest present value for net revenue. The user can iterate on multiple scenarios to seek further increases in NPV. The paper provides relevant example case studies to illustrate how LCCA evaluations of inlet air filters and water wash frequency can be applied to optimize gas turbine economic performance. The intent of the paper is to provide the user with a summary of prior work that can be integrated to provide a more holistic, complete life cycle cost analysis and describes the framework used within the AFLCO software. The underlying technical analysis in this paper can be applied to any life cycle cost analysis.