Littérature scientifique sur le sujet « Dynamic attack graph »
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Articles de revues sur le sujet "Dynamic attack graph"
Jaiganesh, M., G. ShivajiRao, P. Dhivya, M. Udhayamoorthi et A. Vincent Antony Kumar. « Intrusion Optimal Path Attack detection using ACO for Cloud Computing ». E3S Web of Conferences 472 (2024) : 02009. http://dx.doi.org/10.1051/e3sconf/202447202009.
Texte intégralPal, Arunangshu, et Prasenjit Choudhury. « Mitigating Black Hole Attacks in AODV Routing Protocol Using Dynamic Graph ». Mapana - Journal of Sciences 11, no 4 (22 août 2012) : 65–76. http://dx.doi.org/10.12723/mjs.23.5.
Texte intégralSæther, Sigve Hortemo, Jan Arne Telle et Martin Vatshelle. « Solving #SAT and MAXSAT by Dynamic Programming ». Journal of Artificial Intelligence Research 54 (9 septembre 2015) : 59–82. http://dx.doi.org/10.1613/jair.4831.
Texte intégralRajeshwari, T., et C. Thangamani. « Attack Impact Discovery and Recovery with Dynamic Bayesian Networks ». Asian Journal of Computer Science and Technology 8, S1 (5 février 2019) : 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.
Texte intégralHu, Chenao, et Xuefeng Yan. « Dynamic Trilateral Game Model for Attack Graph Security Game ». IOP Conference Series : Materials Science and Engineering 790 (7 avril 2020) : 012112. http://dx.doi.org/10.1088/1757-899x/790/1/012112.
Texte intégralLv, Huiying, Yuan Zhang et Jie Wang. « Network Threat Identification and Analysis Based on a State Transition Graph ». Cybernetics and Information Technologies 13, Special-Issue (1 décembre 2013) : 51–61. http://dx.doi.org/10.2478/cait-2013-0037.
Texte intégralGao, Xiang, Xue Qin Xu et Min Wang. « Evaluating Network Security Based on Attack Graph ». Advanced Materials Research 756-759 (septembre 2013) : 2374–78. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2374.
Texte intégralLee, Dongjin, Juho Lee et Kijung Shin. « Spear and Shield : Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 12 (24 mars 2024) : 13374–82. http://dx.doi.org/10.1609/aaai.v38i12.29239.
Texte intégralBoudermine, Antoine, Rida Khatoun et Jean-Henri Choyer. « Dynamic logic-based attack graph for risk assessment in complex computer systems ». Computer Networks 228 (juin 2023) : 109730. http://dx.doi.org/10.1016/j.comnet.2023.109730.
Texte intégralGuo, Mingyu, Max Ward, Aneta Neumann, Frank Neumann et Hung Nguyen. « Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 5 (26 juin 2023) : 5649–56. http://dx.doi.org/10.1609/aaai.v37i5.25701.
Texte intégralThèses sur le sujet "Dynamic attack graph"
Hamid, Thaier K. A. « Attack graph approach to dynamic network vulnerability analysis and countermeasures ». Thesis, University of Bedfordshire, 2014. http://hdl.handle.net/10547/576432.
Texte intégralBoudermine, Antoine. « A dynamic attack graphs based approach for impact assessment of vulnerabilities in complex computer systems ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT046.
Texte intégralNowadays, computer networks are used in many fields and their breakdown can strongly impact our daily life. Assessing their security is a necessity to reduce the risk of compromise by an attacker. Nevertheless, the solutions proposed so far are rarely adapted to the high complexity of modern computer systems. They often rely on too much human work and the algorithms used don't scale well. Furthermore, the evolution of the system over time is rarely modeled and is therefore not considered in the evaluation of its security.In this thesis, we propose a new attack graph model built from a dynamic description of the system. We have shown through our experimentations that our model allows to identify more attack paths than a static attack graph model. We then proposed an attack simulation algorithm to approximate the chances of success of system compromise by a malicious actor.We also proved that our solution was able to analyze the security of complex systems. The worst-case time complexity was assessed for each algorithm used. Several tests were performed to measure their real performances. Finally, we applied our solution on an IT network composed of several thousand elements.Future work should be done to improve the performance of the attack graph generation algorithm in order to analyze increasingly complex systems. Solutions should also be found to facilitate the system modeling step which is still a difficult task to perform, especially by humans. Finally, the simulation algorithm could be improved to be more realistic and take into account the real capabilities of the attacker. It would also be interesting to assess the impact of the attacks on the organization and its business processes
Aguessy, François-Xavier. « Évaluation dynamique de risque et calcul de réponses basés sur des modèles d’attaques bayésiens ». Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0016/document.
Texte intégralInformation systems constitute an increasingly attractive target for attackers. Given the number and complexity of attacks, security teams need to focus their actions, in order to select the most appropriate security controls. Because of the threat posed by advanced multi-step attacks, it is difficult for security operators to fully cover all vulnerabilities when deploying countermeasures. In this PhD thesis, we build a complete framework for static and dynamic risk assessment including prior knowledge on the information system and dynamic events, proposing responses to prevent future attacks. First, we study how to remediate the potential attacks that can happen in a system, using logical attack graphs. We build a remediation methodology to prevent the most relevant attack paths extracted from a logical attack graph. In order to help an operator to choose between several remediation candidates, we rank them according to a cost of remediation combining operational and impact costs. Then, we study the dynamic attacks that can occur in a system. Attack graphs are not directly suited for dynamic risk assessment. Thus, we extend this mode to build dynamic risk assessment models to evaluate the attacks that are the most likely. The hybrid model is subdivided in two complementary models: (1) the first ones analysing ongoing attacks and provide the hosts' compromise probabilities, and (2) the second ones assessing the most likely future attacks. We study the sensitivity of their probabilistic parameters. Finally, we validate the accuracy and usage of both models in the domain of cybersecurity, by building them from a topological attack graph
Aguessy, François-Xavier. « Évaluation dynamique de risque et calcul de réponses basés sur des modèles d’attaques bayésiens ». Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0016.
Texte intégralInformation systems constitute an increasingly attractive target for attackers. Given the number and complexity of attacks, security teams need to focus their actions, in order to select the most appropriate security controls. Because of the threat posed by advanced multi-step attacks, it is difficult for security operators to fully cover all vulnerabilities when deploying countermeasures. In this PhD thesis, we build a complete framework for static and dynamic risk assessment including prior knowledge on the information system and dynamic events, proposing responses to prevent future attacks. First, we study how to remediate the potential attacks that can happen in a system, using logical attack graphs. We build a remediation methodology to prevent the most relevant attack paths extracted from a logical attack graph. In order to help an operator to choose between several remediation candidates, we rank them according to a cost of remediation combining operational and impact costs. Then, we study the dynamic attacks that can occur in a system. Attack graphs are not directly suited for dynamic risk assessment. Thus, we extend this mode to build dynamic risk assessment models to evaluate the attacks that are the most likely. The hybrid model is subdivided in two complementary models: (1) the first ones analysing ongoing attacks and provide the hosts' compromise probabilities, and (2) the second ones assessing the most likely future attacks. We study the sensitivity of their probabilistic parameters. Finally, we validate the accuracy and usage of both models in the domain of cybersecurity, by building them from a topological attack graph
Saman, Nariman Goran. « A Framework for Secure Structural Adaptation ». Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78658.
Texte intégralMensah, Pernelle. « Generation and Dynamic Update of Attack Graphs in Cloud Providers Infrastructures ». Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0011.
Texte intégralIn traditional environments, attack graphs can paint a picture of the security exposure of the environment. Indeed, they represent a model allowing to depict the many steps an attacker can take to compromise an asset. They can represent a basis for automated risk assessment, relying on an identification and valuation of critical assets in the network. This allows to design pro-active and reactive counter-measures for risk mitigation and can be leveraged for security monitoring and network hardening.Our thesis aims to apply a similar approach in Cloud environments, which implies to consider new challenges incurred by these modern infrastructures, since the majority of attack graph methods were designed with traditional environments in mind. Novel virtualization attack scenarios, as well as inherent properties of the Cloud, namely elasticity and dynamism are a cause for concern.To realize this objective, a thorough inventory of virtualization vulnerabilities was performed, for the extension of existing vulnerability templates. Based on an attack graph representation model suitable to the Cloud scale, we were able to leverage Cloud and SDN technologies, with the purpose of building Cloud attack graphs and maintain them in an up-to-date state. Algorithms able to cope with the frequent rate of change occurring in virtualized environments were designed and extensively tested on a real scale Cloud platform for performance evaluation, confirming the validity of the methods proposed in this thesis, in order to enable Cloud administrator to dispose of an up-to-date Cloud attack graph
KALLAS, KASSEM. « A Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks ». Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1005735.
Texte intégralChapitres de livres sur le sujet "Dynamic attack graph"
Grammatikakis, Konstantinos-Panagiotis, et Nicholas Kolokotronis. « Attack Graph Generation ». Dans Cyber-Security Threats, Actors, and Dynamic Mitigation, 281–334. Boca Raton : CRC Press, 2021. : CRC Press, 2021. http://dx.doi.org/10.1201/9781003006145-8.
Texte intégralGain, Ayan, et Mridul Sankar Barik. « Attack Graph Based Security Metrics for Dynamic Networks ». Dans Information Systems Security, 109–28. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49099-6_7.
Texte intégralWang, Haiping, Binbin Li, Tianning Zang, Yifei Yang, Zisen Qi, Siyu Jia et Yu Ding. « Real-Time Aggregation for Massive Alerts Based on Dynamic Attack Granularity Graph ». Dans Science of Cyber Security, 225–43. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45933-7_14.
Texte intégralHusák, Martin, Joseph Khoury, Đorđe Klisura et Elias Bou-Harb. « On the Provision of Network-Wide Cyber Situational Awareness via Graph-Based Analytics ». Dans Complex Computational Ecosystems, 167–79. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44355-8_12.
Texte intégralChen, Xihui, Ema Këpuska, Sjouke Mauw et Yunior Ramírez-Cruz. « Active Re-identification Attacks on Periodically Released Dynamic Social Graphs ». Dans Computer Security – ESORICS 2020, 185–205. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59013-0_10.
Texte intégralXu, Hongcai, et Junpeng Bao. « Dynamic Knowledge Graph-Based Dialogue Generation with Improved Adversarial Meta-Learning ». Dans Artificial Intelligence and Human-Computer Interaction. IOS Press, 2024. http://dx.doi.org/10.3233/faia240132.
Texte intégralCatta, Davide, Jean Leneutre et Vadim Malvone. « Obstruction Logic : A Strategic Temporal Logic to Reason About Dynamic Game Models ». Dans Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230292.
Texte intégralBonabeau, Eric, Marco Dorigo et Guy Theraulaz. « Self-Organization and Templates : Application to Data Analysis and Graph Partitioning ». Dans Swarm Intelligence. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195131581.003.0009.
Texte intégralActes de conférences sur le sujet "Dynamic attack graph"
He, Siying, Mi Wen, Xiumin Li et Zhou Su. « An Approach for Attack Scenario Construction Based on Dynamic Attack Path Graph ». Dans 2023 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2023. http://dx.doi.org/10.1109/iccc57788.2023.10233417.
Texte intégralAlrehaili, Meaad, et Adel Alshamrani. « An Attack Scenario Reconstruction Approach Using Alerts Correlation and a Dynamic Attack Graph ». Dans 2023 Eighth International Conference On Mobile And Secure Services (MobiSecServ). IEEE, 2023. http://dx.doi.org/10.1109/mobisecserv58080.2023.10329144.
Texte intégralBoudermine, Antoine, Rida Khatoun et Jean-Henri Choyer. « Attack Graph-based Solution for Vulnerabilities Impact Assessment in Dynamic Environment ». Dans 2022 5th Conference on Cloud and Internet of Things (CIoT). IEEE, 2022. http://dx.doi.org/10.1109/ciot53061.2022.9766588.
Texte intégralWu, Hua, Yu Gu, Guang Cheng et Yuyang Zhou. « Effectiveness Evaluation Method for Cyber Deception Based on Dynamic Bayesian Attack Graph ». Dans CSSE 2020 : 2020 3rd International Conference on Computer Science and Software Engineering. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3403746.3403897.
Texte intégralLin, Pengwen, et Yonghong Chen. « Dynamic Network Security Situation Prediction based on Bayesian Attack Graph and Big Data ». Dans 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2018. http://dx.doi.org/10.1109/itoec.2018.8740765.
Texte intégralWei, Xingshen, Peng Gao, Junxian Xu, Haotian Zhang, Qiuhan Tian et Zengzhou Ma. « Research on attack behaviour detection based on dynamic graph neural network in power IoT system ». Dans International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), sous la direction de Yongquan Yan. SPIE, 2022. http://dx.doi.org/10.1117/12.2640979.
Texte intégralNikseresht, Ilnaz, Issa Traore et Amirali Baniasadi. « Data Visualization of Graph-Based Threat Detection System ». Dans 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120913.
Texte intégralSharma, Kartik, Rakshit Trivedi, Rohit Sridhar et Srijan Kumar. « Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models ». Dans KDD '23 : The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2023. http://dx.doi.org/10.1145/3580305.3599517.
Texte intégralGamarra, Marco, Sachin Shetty, David M. Nicol, Oscar Gonzalez, Charles A. Kamhoua et Laurent Njilla. « Analysis of Stepping Stone Attacks in Dynamic Vulnerability Graphs ». Dans 2018 IEEE International Conference on Communications (ICC 2018). IEEE, 2018. http://dx.doi.org/10.1109/icc.2018.8422723.
Texte intégralWu, Songyang, Yong Zhang et Xiao Chen. « Security Assessment of Dynamic Networks with an Approach of Integrating Semantic Reasoning and Attack Graphs ». Dans 2018 IEEE 4th International Conference on Computer and Communications (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/compcomm.2018.8780998.
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