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Auswahl der wissenschaftlichen Literatur zum Thema „Model fuzzing“
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Zeitschriftenartikel zum Thema "Model fuzzing"
Zhu, Xue Yong, und Zhi Yong Wu. „A New Fuzzing Technique Using Niche Genetic Algorithm“. Advanced Materials Research 756-759 (September 2013): 4050–58. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4050.
Der volle Inhalt der QuelleKim, Minho, Seongbin Park, Jino Yoon, Minsoo Kim und Bong-Nam Noh. „File Analysis Data Auto-Creation Model For Peach Fuzzing“. Journal of the Korea Institute of Information Security and Cryptology 24, Nr. 2 (30.04.2014): 327–33. http://dx.doi.org/10.13089/jkiisc.2014.24.2.327.
Der volle Inhalt der QuelleSong, Guang Jun, Chun Lan Zhao und Ming Li. „Study on Software Vulnerability Dynamic Discovering System“. Applied Mechanics and Materials 151 (Januar 2012): 673–77. http://dx.doi.org/10.4028/www.scientific.net/amm.151.673.
Der volle Inhalt der QuelleGuan, Quan Long, Guo Xiang Yao, Kai Bin Ni und Mei Xiu Zhou. „Research on Fuzzing Test Data Engine for Web Vulnerability“. Advanced Materials Research 211-212 (Februar 2011): 500–504. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.500.
Der volle Inhalt der QuelleZeng, Yingpei, Mingmin Lin, Shanqing Guo, Yanzhao Shen, Tingting Cui, Ting Wu, Qiuhua Zheng und Qiuhua Wang. „MultiFuzz: A Coverage-Based Multiparty-Protocol Fuzzer for IoT Publish/Subscribe Protocols“. Sensors 20, Nr. 18 (11.09.2020): 5194. http://dx.doi.org/10.3390/s20185194.
Der volle Inhalt der QuelleGao, Sudi, Yueying Luo und Tan Yang. „Research on River Water Environmental Capacity Based on Triangular Fuzzy Technology“. E3S Web of Conferences 236 (2021): 03018. http://dx.doi.org/10.1051/e3sconf/202123603018.
Der volle Inhalt der QuelleDong, Guofang, Pu Sun, Wenbo Shi und Chang Choi. „A novel valuation pruning optimization fuzzing test model based on mutation tree for industrial control systems“. Applied Soft Computing 70 (September 2018): 896–902. http://dx.doi.org/10.1016/j.asoc.2018.02.036.
Der volle Inhalt der QuelleDai, Xinghua, Shengrong Gong, Shan Zhong und Zongming Bao. „Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement“. Applied Sciences 9, Nr. 2 (16.01.2019): 301. http://dx.doi.org/10.3390/app9020301.
Der volle Inhalt der QuelleWang, Xiandong, Jianmin He und Shouwei Li. „Compound Option Pricing under Fuzzy Environment“. Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/875319.
Der volle Inhalt der QuelleLiu, Xiao, Xiaoting Li, Rupesh Prajapati und Dinghao Wu. „DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 1044–51. http://dx.doi.org/10.1609/aaai.v33i01.33011044.
Der volle Inhalt der QuelleDissertationen zum Thema "Model fuzzing"
Duchene, Fabien. „Detection of web vulnerabilities via model inference assisted evolutionary fuzzing“. Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM022/document.
Der volle Inhalt der QuelleTesting is a viable approach for detecting implementation bugs which have a security impact, a.k.a. vulnerabilities. When the source code is not available, it is necessary to use black-box testing techniques. We address the problem of automatically detecting a certain class of vulnerabilities (Cross Site Scripting a.k.a. XSS) in web applications in a black-box test context. We propose an approach for inferring models of web applications and fuzzing from such models and an attack grammar. We infer control plus taint flow automata, from which we produce slices, which narrow the fuzzing search space. Genetic algorithms are then used to schedule the malicious inputs which are sent to the application. We incorporate a test verdict by performing a double taint inference on the browser parse tree and combining this with taint aware vulnerability patterns. Our implementations LigRE and KameleonFuzz outperform current open-source black-box scanners. We discovered 0-day XSS (i.e., previously unknown vulnerabilities) in web applications used by millions of users
Venger, Adam. „Black-box analýza zabezpečení Wi-Fi“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445533.
Der volle Inhalt der QuelleAhmad, Abbas. „Model-Based Testing for IoT Systems : Methods and tools“. Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD008/document.
Der volle Inhalt der QuelleThe Internet of Things (IoT) is nowadays globally a mean of innovation and transformation for many companies. Applications extend to a large number of domains, such as smart cities, smart homes, healthcare, etc. The Gartner Group estimates an increase up to 21 billion connected things by 2020. The large span of "things" introduces problematic aspects, such as conformance and interoperability due to the heterogeneity of communication protocols and the lack of a globally-accepted standard. The large span of usages introduces problems regarding secure deployments and scalability of the network over large-scale infrastructures. This thesis deals with the problem of the validation of the Internet of Things to meet the challenges of IoT systems. For that, we propose an approach using the generation of tests from models (MBT). We have confronted this approach through multiple experiments using real systems thanks to our participation in international projects. The important effort which is needed to be placed on the testing aspects reminds every IoT system developer that doing nothing is more expensive later on than doing it on the go
Lone, Sang Fernand. „Protection des systèmes informatiques contre les attaques par entrées-sorties“. Phd thesis, INSA de Toulouse, 2012. http://tel.archives-ouvertes.fr/tel-00863020.
Der volle Inhalt der QuelleLiao, Feng-Ze, und 廖峰澤. „Browser Fuzzing by Scheduled Mutation and Generation of Document Object Models“. Thesis, 2015. http://ndltd.ncl.edu.tw/handle/34522736060995439796.
Der volle Inhalt der Quelle國立交通大學
網路工程研究所
103
Internet applications have made our daily life fruitful. However, they also cause many security problems if these applications are leveraged by intruders. Thus, it is important to find and fix vulnerabilities timely to prevent application vulnerabilities from being exploited. Fuzz testing is a popular methodology that effectively finds vulnerabilities in application programs with seed input mutation. However, it is not a satisfied solution for the web browsers. In this work, we propose a solution, called scheduled DOM fuzzing (SDF), which integrates several related browser fuzzing tools and the fuzzing framework called BFF. To explore more crash possibilities, we revise the browser fuzzing architecture and schedule seed input selection and mutation dynamically. We also propose two probability computing methods in scheduling mechanism which tries to improve the performance by determining which combinations of seed and mutation would produce more crashes. Our experiments show that SDF is 2.27 time more efficient in terms of the number of crashes and vulnerabilities found at most. SDF also has the capacity for finding 23 exploitable crashes in Windows 7 within five days. The experimental results reveals that a good scheduling method for seed and mutations in browser fuzzing is able to find more exploitable crashes than fuzzers with the fixed seed input.
Bücher zum Thema "Model fuzzing"
Wikert, Steven Micheal. Cherish me always: Teddy bears & warm fuzzies : antique photographs of children with stuffed animals. Grantsville, Md: Hobby House Press, 2001.
Den vollen Inhalt der Quelle findenWikert, Steven Micheal, und Mary McMurray Wikert. Teddy Bears & Warm Fuzzies: Antique Photographs of Children with Stuffed Animals. Hobby House Press, 2001.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Model fuzzing"
Chen, Chen, Zhouguo Chen, Yongle Hao und Baojiang Cui. „Mocov: Model Based Fuzzing Through Coverage Guided Technology“. In Advances on Broad-Band Wireless Computing, Communication and Applications, 404–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69811-3_37.
Der volle Inhalt der QuelleWidl, Magdalena. „Test Case Generation by Grammar-Based Fuzzing for Model-Driven Engineering“. In Hardware and Software: Verification and Testing, 278–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39611-3_28.
Der volle Inhalt der QuelleChen, Yixiong, Yang Yang, Zhanyao Lei, Mingyuan Xia und Zhengwei Qi. „Bootstrapping Automated Testing for RESTful Web Services“. In Fundamental Approaches to Software Engineering, 46–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71500-7_3.
Der volle Inhalt der QuelleAlshmrany, Kaled M., Rafael S. Menezes, Mikhail R. Gadelha und Lucas C. Cordeiro. „FuSeBMC: A White-Box Fuzzer for Finding Security Vulnerabilities in C Programs (Competition Contribution)“. In Fundamental Approaches to Software Engineering, 363–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71500-7_19.
Der volle Inhalt der QuelleIdowu, Peter Adebayo, Sarumi Olusegun Ajibola, Jeremiah Ademola Balogun und Oluwadare Ogunlade. „Development of a Fuzzy Logic-Based Model for Monitoring Cardiovascular Risk“. In Coronary and Cardiothoracic Critical Care, 172–90. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8185-7.ch009.
Der volle Inhalt der QuelleJaggi, Chandra K., Bimal Kumar Mishra und T. C. Panda. „A Fuzzy EOQ Model for Deteriorating Items With Allowable Shortage and Inspection Under the Trade Credit“. In Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques, 233–49. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3232-3.ch014.
Der volle Inhalt der QuelleChakraborti, Debjani, Valentina E. Balas und Bijay Baran Pal. „Genetic Algorithm for FGP Model of a Multiobjective Bilevel Programming Problem in Uncertain Environment“. In Handbook of Research on Natural Computing for Optimization Problems, 870–88. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0058-2.ch035.
Der volle Inhalt der QuelleKumar, Mousumi, Valentina E. Balas und Bijay Baran Pal. „Using Fuzzy Goal Programming with Penalty Functions for Solving EEPGD Problem via Genetic Algorithm“. In Handbook of Research on Natural Computing for Optimization Problems, 847–69. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0058-2.ch034.
Der volle Inhalt der QuelleSaha, Sumana, und Tripti Chakrabarti. „Imprecise Inventory Model for Items With Imperfect Quality Subject to Learning Effects Having Shortages“. In Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques, 284–304. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3232-3.ch016.
Der volle Inhalt der Quelle„Development of Fuzzy Multi-Objective Stochastic Fractional Programming Models“. In Multi-Objective Stochastic Programming in Fuzzy Environments, 128–76. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8301-1.ch004.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Model fuzzing"
Schneider, Martin, Jurgen Grossmann, Ina Schieferdecker und Andrej Pietschker. „Online Model-Based Behavioral Fuzzing“. In 2013 IEEE 6th International Conference On Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2013. http://dx.doi.org/10.1109/icstw.2013.61.
Der volle Inhalt der QuelleWang, Jiajie, Puhan Zhang, Lei Zhang, Haowen Zhu und Xiaojun Ye. „A model-based fuzzing approach for DBMS“. In 2013 8th International Conference on Communications and Networking in China (CHINACOM). IEEE, 2013. http://dx.doi.org/10.1109/chinacom.2013.6694634.
Der volle Inhalt der QuellePham, Van-Thuan, Marcel Böhme und Abhik Roychoudhury. „Model-based whitebox fuzzing for program binaries“. In ASE'16: ACM/IEEE International Conference on Automated Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2970276.2970316.
Der volle Inhalt der QuelleGao, Zicong, Weiyu Dong, Rui Chang und Chengwei Ai. „The Stacked Seq2seq-attention Model for Protocol Fuzzing“. In 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2019. http://dx.doi.org/10.1109/iccsnt47585.2019.8962499.
Der volle Inhalt der QuelleHe, HuiHui, und YongJun Wang. „PNFUZZ: A Stateful Network Protocol Fuzzing Approach Based on Packet Clustering“. In 6th International Conference on Computer Science, Engineering And Applications (CSEA 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101805.
Der volle Inhalt der QuelleSun, Xiaoshan, Yu Fu, Yun Dong, Zhihao Liu und Yang Zhang. „Improving Fitness Function for Language Fuzzing with PCFG Model“. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2018. http://dx.doi.org/10.1109/compsac.2018.00098.
Der volle Inhalt der QuelleDuchene, Fabien, Roland Groz, Sanjay Rawat und Jean-Luc Richier. „XSS Vulnerability Detection Using Model Inference Assisted Evolutionary Fuzzing“. In 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation (ICST). IEEE, 2012. http://dx.doi.org/10.1109/icst.2012.181.
Der volle Inhalt der QuelleWang, Jiajie, Tao Guo, Puhan Zhang und Qixue Xiao. „A Model-Based Behavioral Fuzzing Approach for Network Service“. In 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2013. http://dx.doi.org/10.1109/imccc.2013.250.
Der volle Inhalt der QuelleXu, Haoran, Yongjun Wang, Shuhui Fan, Peidai Xie und Aizhi Liu. „DSmith: Compiler Fuzzing through Generative Deep Learning Model with Attention“. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206911.
Der volle Inhalt der QuelleJohansson, William, Martin Svensson, Ulf E. Larson, Magnus Almgren und Vincenzo Gulisano. „T-Fuzz: Model-Based Fuzzing for Robustness Testing of Telecommunication Protocols“. In 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation (ICST). IEEE, 2014. http://dx.doi.org/10.1109/icst.2014.45.
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