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Статті в журналах з теми "Adversarial Attack and Defense"
Park, Sanglee, and Jungmin So. "On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification." Applied Sciences 10, no. 22 (November 14, 2020): 8079. http://dx.doi.org/10.3390/app10228079.
Повний текст джерелаTang, Renzhi, Guowei Shen, Chun Guo, and Yunhe Cui. "SAD: Website Fingerprinting Defense Based on Adversarial Examples." Security and Communication Networks 2022 (April 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/7330465.
Повний текст джерелаZheng, Tianhang, Changyou Chen, and Kui Ren. "Distributionally Adversarial Attack." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2253–60. http://dx.doi.org/10.1609/aaai.v33i01.33012253.
Повний текст джерелаLiang, Hongshuo, Erlu He, Yangyang Zhao, Zhe Jia, and Hao Li. "Adversarial Attack and Defense: A Survey." Electronics 11, no. 8 (April 18, 2022): 1283. http://dx.doi.org/10.3390/electronics11081283.
Повний текст джерелаZeng, Huimin, Chen Zhu, Tom Goldstein, and Furong Huang. "Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10815–23. http://dx.doi.org/10.1609/aaai.v35i12.17292.
Повний текст джерелаRosenberg, Ishai, Asaf Shabtai, Yuval Elovici, and Lior Rokach. "Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain." ACM Computing Surveys 54, no. 5 (June 2021): 1–36. http://dx.doi.org/10.1145/3453158.
Повний текст джерелаQiu, Shilin, Qihe Liu, Shijie Zhou, and Chunjiang Wu. "Review of Artificial Intelligence Adversarial Attack and Defense Technologies." Applied Sciences 9, no. 5 (March 4, 2019): 909. http://dx.doi.org/10.3390/app9050909.
Повний текст джерелаYang, Kaichen, Tzungyu Tsai, Honggang Yu, Tsung-Yi Ho, and Yier Jin. "Beyond Digital Domain: Fooling Deep Learning Based Recognition System in Physical World." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1088–95. http://dx.doi.org/10.1609/aaai.v34i01.5459.
Повний текст джерелаShi, Lin, Teyi Liao, and Jianfeng He. "Defending Adversarial Attacks against DNN Image Classification Models by a Noise-Fusion Method." Electronics 11, no. 12 (June 8, 2022): 1814. http://dx.doi.org/10.3390/electronics11121814.
Повний текст джерелаShieh, Chin-Shiuh, Thanh-Tuan Nguyen, Wan-Wei Lin, Wei Kuang Lai, Mong-Fong Horng, and Denis Miu. "Detection of Adversarial DDoS Attacks Using Symmetric Defense Generative Adversarial Networks." Electronics 11, no. 13 (June 24, 2022): 1977. http://dx.doi.org/10.3390/electronics11131977.
Повний текст джерелаДисертації з теми "Adversarial Attack and Defense"
Branlat, Matthieu. "Challenges to Adversarial Interplay Under High Uncertainty: Staged-World Study of a Cyber Security Event." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1316462733.
Повний текст джерелаKanerva, Anton, and Fredrik Helgesson. "On the Use of Model-Agnostic Interpretation Methods as Defense Against Adversarial Input Attacks on Tabular Data." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20085.
Повний текст джерелаKontext. Maskininlärning är ett område inom artificiell intelligens som är under konstant utveckling. Mängden domäner som vi sprider maskininlärningsmodeller i växer sig allt större och systemen sprider sig obemärkt nära inpå våra dagliga liv genom olika elektroniska enheter. Genom åren har mycket tid och arbete lagts på att öka dessa modellers prestanda vilket har överskuggat risken för sårbarheter i systemens kärna, den tränade modellen. En relativt ny attack, kallad "adversarial input attack", med målet att lura modellen till felaktiga beslutstaganden har nästan uteslutande forskats på inom bildigenkänning. Men, hotet som adversarial input-attacker utgör sträcker sig utom ramarna för bilddata till andra datadomäner som den tabulära domänen vilken är den vanligaste datadomänen inom industrin. Metoder för att tolka komplexa maskininlärningsmodeller kan hjälpa människor att förstå beteendet hos dessa komplexa maskininlärningssystem samt de beslut som de tar. Att förstå en modells beteende är en viktig komponent för att upptäcka, förstå och mitigera sårbarheter hos modellen. Syfte. Den här studien försöker reducera det forskningsgap som adversarial input-attacker och motsvarande försvarsmetoder i den tabulära domänen utgör. Målet med denna studie är att analysera hur modelloberoende tolkningsmetoder kan användas för att mitigera och detektera adversarial input-attacker mot tabulär data. Metod. Det uppsatta målet nås genom tre på varandra följande experiment där modelltolkningsmetoder analyseras, adversarial input-attacker utvärderas och visualiseras samt där en ny metod baserad på modelltolkning föreslås för detektion av adversarial input-attacker tillsammans med en ny mitigeringsteknik där feature selection används defensivt för att minska attackvektorns storlek. Resultat. Den föreslagna metoden för detektering av adversarial input-attacker visar state-of-the-art-resultat med över 86% träffsäkerhet. Den föreslagna mitigeringstekniken visades framgångsrik i att härda modellen mot adversarial input attacker genom att minska deras attackstyrka med 33% utan att degradera modellens klassifieringsprestanda. Slutsats. Denna studie bidrar med användbara metoder för detektering och mitigering av adversarial input-attacker såväl som metoder för att utvärdera och visualisera svårt förnimbara attacker mot tabulär data.
Harris, Rae. "Spectre: Attack and Defense." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/scripps_theses/1384.
Повний текст джерелаWood, Adrian Michael. "A defensive strategy for detecting targeted adversarial poisoning attacks in machine learning trained malware detection models." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2483.
Повний текст джерелаMoore, Tyler Weston. "Cooperative attack and defense in distributed networks." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612283.
Повний текст джерелаZhang, Ning. "Attack and Defense with Hardware-Aided Security." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72855.
Повний текст джерелаPh. D.
Sohail, Imran, and Sikandar Hayat. "Cooperative Defense Against DDoS Attack using GOSSIP Protocol." Thesis, Blekinge Tekniska Högskola, Avdelningen för telekommunikationssystem, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1224.
Повний текст джерелаTownsend, James R. "Defense of Naval Task Forces from Anti-Ship Missile attack." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA363038.
Повний текст джерелаHaberlin, Richard J. "Analysis of unattended ground sensors in theater Missile Defense Attack Operations." Thesis, Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/26369.
Повний текст джерелаUnattended ground sensors have a tremendous potential for improving Tactical Ballistic Missile Attack Operations. To date, however, this potential has gone unrealized primarily due to a lack of confidence in the systems and a lack of tactical doctrine for their employment. This thesis provides analyses to demonstrate the effective use of sensor technology and provides recommendations as to how they may best be employed. The probabilistic decision model reports the optimal size array for each of the candidate array locations. It also provides an optimal policy for determining the likelihood that the target is a Time Critical Target based on the number of sensors in agreement as to its identity. This policy may vary with each candidate array. Additionally, recommendations are made on the placement of the arrays within the theater of operations and their optimal configuration to maximize information gained while minimizing the likelihood of compromise. Specifics include, inter-sensor spacing, placement patterns, array locations, and off-road distance
Widel, Wojciech. "Formal modeling and quantitative analysis of security using attack- defense trees." Thesis, Rennes, INSA, 2019. http://www.theses.fr/2019ISAR0019.
Повний текст джерелаRisk analysis is a very complex process. It requires rigorous representation and in-depth assessment of threats and countermeasures. This thesis focuses on the formal modelling of security using attack and defence trees. These are used to represent and quantify potential attacks in order to better understand the security issues that the analyzed system may face. They therefore make it possible to guide an expert in the choice of countermeasures to be implemented to secure their system. The main contributions of this thesis are as follows: - The enrichment of the attack and defence tree model allowing the analysis of real security scenarios. In particular, we have developed the theoretical foundations and quantitative evaluation algorithms for the model where an attacker's action can contribute to several attacks and a countermeasure can prevent several threats. - The development of a methodology based on Pareto dominance and allowing several quantitative aspects to be taken into account simultaneously (e.g., cost, time, probability, difficulty, etc.) during a risk analysis. - The design of a technique, using linear programming methods, for selecting an optimal set of countermeasures, taking into account the budget available for protecting the analyzed system. It is a generic technique that can be applied to several optimization problems, for example, maximizing the attack surface coverage, or maximizing the attacker's investment. To ensure their practical applicability, the model and mathematical algorithms developed were implemented in a freely available open source tool. All the results were also validated with a practical study on an industrial scenario of alteration of electricity consumption meters
Книги з теми "Adversarial Attack and Defense"
Attack and defense. Broomall, PA: Mason Crest Publishers, 2003.
Знайти повний текст джерелаAttack and defense. Sidney, N.S.W: Weldon Owen, 2008.
Знайти повний текст джерелаJajodia, Sushil, George Cybenko, Peng Liu, Cliff Wang, and Michael Wellman, eds. Adversarial and Uncertain Reasoning for Adaptive Cyber Defense. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30719-6.
Повний текст джерелаLutes, W. John. Scandinavian defense: Anderssen counter attack.--. Coraopolis: Chess Enterprises Inc., 1988.
Знайти повний текст джерелаC, Kenski Henry, ed. Attack politics: Strategy and defense. New York: Praeger, 1990.
Знайти повний текст джерелаCoaching football's attack & pursue 50 defense. West Nyack, N.Y: Parker Pub. Co., 1985.
Знайти повний текст джерелаRiley, Kathy. Weird and wonderful: Attack and defense. New York: Kingfisher, 2011.
Знайти повний текст джерелаJajodia, Sushil. Moving Target Defense II: Application of Game Theory and Adversarial Modeling. New York, NY: Springer New York, 2013.
Знайти повний текст джерелаCarson, Harry. Point of attack: The defense strikes back. New York: McGraw-Hill, 1987.
Знайти повний текст джерелаQ, Elvee Richard, ed. The end of science?: Attack and defense. Lanham, Md: University Press of America, 1992.
Знайти повний текст джерелаЧастини книг з теми "Adversarial Attack and Defense"
Zhou, Mo, Zhenxing Niu, Le Wang, Qilin Zhang, and Gang Hua. "Adversarial Ranking Attack and Defense." In Computer Vision – ECCV 2020, 781–99. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58568-6_46.
Повний текст джерелаYang, Jie, Man Wu, and Xiao-Zhang Liu. "Defense Against Adversarial Attack Using PCA." In Communications in Computer and Information Science, 627–36. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8086-4_59.
Повний текст джерелаHu, Nianyan, Ting Lu, Wenjing Guo, Qiubo Huang, Guohua Liu, Shan Chang, Jiafei Song, and Yiyang Luo. "Random Sparsity Defense Against Adversarial Attack." In PRICAI 2021: Trends in Artificial Intelligence, 597–607. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89363-7_45.
Повний текст джерелаKuribayashi, Minoru. "Defense Against Adversarial Attacks." In Frontiers in Fake Media Generation and Detection, 131–48. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1524-6_6.
Повний текст джерелаWu, Ji, Chaoqun Ye, and Shiyao Jin. "Adversarial Organization Modeling for Network Attack/Defense." In Information Security Practice and Experience, 90–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11689522_9.
Повний текст джерелаZhang, Yanjing, Jianming Cui, and Ming Liu. "Research on Adversarial Patch Attack Defense Method for Traffic Sign Detection." In Communications in Computer and Information Science, 199–210. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8285-9_15.
Повний текст джерелаZhang, Yuxiao, Qingfeng Chen, Xinkun Hao, Haiming Pan, Qian Yu, and Kexin Huang. "Defense Against Adversarial Attack on Knowledge Graph Embedding." In Emerging Trends in Cybersecurity Applications, 441–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09640-2_20.
Повний текст джерелаYin, Zhizhou, Wei Liu, and Sanjay Chawla. "Adversarial Attack, Defense, and Applications with Deep Learning Frameworks." In Deep Learning Applications for Cyber Security, 1–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13057-2_1.
Повний текст джерелаShibly, Kabid Hassan, Md Delwar Hossain, Hiroyuki Inoue, Yuzo Taenaka, and Youki Kadobayashi. "Autonomous Driving Model Defense Study on Hijacking Adversarial Attack." In Lecture Notes in Computer Science, 546–57. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_46.
Повний текст джерелаVasconcellos Vargas, Danilo. "Learning Systems Under Attack—Adversarial Attacks, Defenses and Beyond." In Autonomous Vehicles, 147–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9255-3_7.
Повний текст джерелаТези доповідей конференцій з теми "Adversarial Attack and Defense"
Wu, Huijun, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. "Adversarial Examples for Graph Data: Deep Insights into Attack and Defense." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/669.
Повний текст джерелаWang, Xiao, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, and Sang Chin. "Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/833.
Повний текст джерелаNguyen, Thanh H., Arunesh Sinha, and He He. "Partial Adversarial Behavior Deception in Security Games." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/40.
Повний текст джерелаXiao, Chaowei, Bo Li, Jun-yan Zhu, Warren He, Mingyan Liu, and Dawn Song. "Generating Adversarial Examples with Adversarial Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/543.
Повний текст джерелаXu, Kaidi, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin. "Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/550.
Повний текст джерелаSimonetto, Thibault, Salijona Dyrmishi, Salah Ghamizi, Maxime Cordy, and Yves Le Traon. "A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/183.
Повний текст джерелаLi, Yeni, Hany S. Abdel-Khalik, and Elisa Bertino. "Online Adversarial Learning of Reactor State." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-82372.
Повний текст джерелаZhang, Chaoning, Philipp Benz, Chenguo Lin, Adil Karjauv, Jing Wu, and In So Kweon. "A Survey on Universal Adversarial Attack." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/635.
Повний текст джерелаChhabra, Saheb, Akshay Agarwal, Richa Singh, and Mayank Vatsa. "Attack Agnostic Adversarial Defense via Visual Imperceptible Bound." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412663.
Повний текст джерелаZhao, Zhe, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, and Jun Sun. "Attack as defense: characterizing adversarial examples using robustness." In ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460319.3464822.
Повний текст джерелаЗвіти організацій з теми "Adversarial Attack and Defense"
Maxion, Roy A., Kevin S. Killourhy, and Kymie M. Tan. Developing a Defense-Centric Attack Taxonomy. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada435079.
Повний текст джерелаBest, Carole N. Computer Network Defense and Attack: Information Warfare in the Department of Defense. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada394187.
Повний текст джерелаHanson, Kraig. Organization of DoD Computer Network Defense, Exploitation, and Attack Forces. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada500822.
Повний текст джерелаWatanabe, Nathan K., and Shannon M. Huffman. Missile Defense Attack Operations (Joint Force Quartery, Winter 2000-2001). Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada426706.
Повний текст джерелаBlackert, W. J., R. L. Hom, A. K. Castner, R. M. Jokerst, and D. M. Gregg. Distributed Denial of Service-Defense Attack Tradeoff Analysis (DDOS-DATA). Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada429339.
Повний текст джерелаWhite, Gregory B. Center for Infrastructure Assurance and Security - Attack and Defense Exercises. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada523898.
Повний текст джерелаFU, Fabian. End-to-End and Network-wide Attack Defense Solution -Overhaul Carrier Network Security. Denmark: River Publishers, July 2016. http://dx.doi.org/10.13052/popcas006.
Повний текст джерелаLetchford, Joshua. Game Theory for Proactive Dynamic Defense and Attack Mitigation in Cyber-Physical Systems. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1330190.
Повний текст джерелаAliberti, David M. Preparing for a Nightmare: USNORTHCOM'S Homeland Defense Mission Against Chemical and Biological Attack. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada609815.
Повний текст джерелаMorrow, Walter. Report of the Defense Science Board Task Force on Deep Attack Weapons Mix Study (DAWMS). Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada345434.
Повний текст джерела