Academic literature on the topic 'Dynamic attack graph'
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Journal articles on the topic "Dynamic attack graph":
Jaiganesh, M., G. ShivajiRao, P. Dhivya, M. Udhayamoorthi, and 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.
Pal, Arunangshu, and Prasenjit Choudhury. "Mitigating Black Hole Attacks in AODV Routing Protocol Using Dynamic Graph." Mapana - Journal of Sciences 11, no. 4 (August 22, 2012): 65–76. http://dx.doi.org/10.12723/mjs.23.5.
Sæther, Sigve Hortemo, Jan Arne Telle, and Martin Vatshelle. "Solving #SAT and MAXSAT by Dynamic Programming." Journal of Artificial Intelligence Research 54 (September 9, 2015): 59–82. http://dx.doi.org/10.1613/jair.4831.
Rajeshwari, T., and C. Thangamani. "Attack Impact Discovery and Recovery with Dynamic Bayesian Networks." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.
Hu, Chenao, and Xuefeng Yan. "Dynamic Trilateral Game Model for Attack Graph Security Game." IOP Conference Series: Materials Science and Engineering 790 (April 7, 2020): 012112. http://dx.doi.org/10.1088/1757-899x/790/1/012112.
Lv, Huiying, Yuan Zhang, and Jie Wang. "Network Threat Identification and Analysis Based on a State Transition Graph." Cybernetics and Information Technologies 13, Special-Issue (December 1, 2013): 51–61. http://dx.doi.org/10.2478/cait-2013-0037.
Gao, Xiang, Xue Qin Xu, and Min Wang. "Evaluating Network Security Based on Attack Graph." Advanced Materials Research 756-759 (September 2013): 2374–78. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2374.
Lee, Dongjin, Juho Lee, and 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 (March 24, 2024): 13374–82. http://dx.doi.org/10.1609/aaai.v38i12.29239.
Boudermine, Antoine, Rida Khatoun, and Jean-Henri Choyer. "Dynamic logic-based attack graph for risk assessment in complex computer systems." Computer Networks 228 (June 2023): 109730. http://dx.doi.org/10.1016/j.comnet.2023.109730.
Guo, Mingyu, Max Ward, Aneta Neumann, Frank Neumann, and Hung Nguyen. "Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (June 26, 2023): 5649–56. http://dx.doi.org/10.1609/aaai.v37i5.25701.
Dissertations / Theses on the topic "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.
Boudermine, 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.
Nowadays, 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.
Information 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.
Information 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.
Mensah, Pernelle. "Generation and Dynamic Update of Attack Graphs in Cloud Providers Infrastructures." Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0011.
In 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.
Book chapters on the topic "Dynamic attack graph":
Grammatikakis, Konstantinos-Panagiotis, and Nicholas Kolokotronis. "Attack Graph Generation." In 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.
Gain, Ayan, and Mridul Sankar Barik. "Attack Graph Based Security Metrics for Dynamic Networks." In Information Systems Security, 109–28. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49099-6_7.
Wang, Haiping, Binbin Li, Tianning Zang, Yifei Yang, Zisen Qi, Siyu Jia, and Yu Ding. "Real-Time Aggregation for Massive Alerts Based on Dynamic Attack Granularity Graph." In Science of Cyber Security, 225–43. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45933-7_14.
Husák, Martin, Joseph Khoury, Đorđe Klisura, and Elias Bou-Harb. "On the Provision of Network-Wide Cyber Situational Awareness via Graph-Based Analytics." In Complex Computational Ecosystems, 167–79. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44355-8_12.
Chen, Xihui, Ema Këpuska, Sjouke Mauw, and Yunior Ramírez-Cruz. "Active Re-identification Attacks on Periodically Released Dynamic Social Graphs." In Computer Security – ESORICS 2020, 185–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59013-0_10.
Xu, Hongcai, and Junpeng Bao. "Dynamic Knowledge Graph-Based Dialogue Generation with Improved Adversarial Meta-Learning." In Artificial Intelligence and Human-Computer Interaction. IOS Press, 2024. http://dx.doi.org/10.3233/faia240132.
Catta, Davide, Jean Leneutre, and Vadim Malvone. "Obstruction Logic: A Strategic Temporal Logic to Reason About Dynamic Game Models." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230292.
Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz. "Self-Organization and Templates: Application to Data Analysis and Graph Partitioning." In Swarm Intelligence. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195131581.003.0009.
Conference papers on the topic "Dynamic attack graph":
He, Siying, Mi Wen, Xiumin Li, and Zhou Su. "An Approach for Attack Scenario Construction Based on Dynamic Attack Path Graph." In 2023 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2023. http://dx.doi.org/10.1109/iccc57788.2023.10233417.
Alrehaili, Meaad, and Adel Alshamrani. "An Attack Scenario Reconstruction Approach Using Alerts Correlation and a Dynamic Attack Graph." In 2023 Eighth International Conference On Mobile And Secure Services (MobiSecServ). IEEE, 2023. http://dx.doi.org/10.1109/mobisecserv58080.2023.10329144.
Boudermine, Antoine, Rida Khatoun, and Jean-Henri Choyer. "Attack Graph-based Solution for Vulnerabilities Impact Assessment in Dynamic Environment." In 2022 5th Conference on Cloud and Internet of Things (CIoT). IEEE, 2022. http://dx.doi.org/10.1109/ciot53061.2022.9766588.
Wu, Hua, Yu Gu, Guang Cheng, and Yuyang Zhou. "Effectiveness Evaluation Method for Cyber Deception Based on Dynamic Bayesian Attack Graph." In 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.
Lin, Pengwen, and Yonghong Chen. "Dynamic Network Security Situation Prediction based on Bayesian Attack Graph and Big Data." In 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2018. http://dx.doi.org/10.1109/itoec.2018.8740765.
Wei, Xingshen, Peng Gao, Junxian Xu, Haotian Zhang, Qiuhan Tian, and Zengzhou Ma. "Research on attack behaviour detection based on dynamic graph neural network in power IoT system." In International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), edited by Yongquan Yan. SPIE, 2022. http://dx.doi.org/10.1117/12.2640979.
Nikseresht, Ilnaz, Issa Traore, and Amirali Baniasadi. "Data Visualization of Graph-Based Threat Detection System." In 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.
Sharma, Kartik, Rakshit Trivedi, Rohit Sridhar, and Srijan Kumar. "Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models." In 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.
Gamarra, Marco, Sachin Shetty, David M. Nicol, Oscar Gonzalez, Charles A. Kamhoua, and Laurent Njilla. "Analysis of Stepping Stone Attacks in Dynamic Vulnerability Graphs." In 2018 IEEE International Conference on Communications (ICC 2018). IEEE, 2018. http://dx.doi.org/10.1109/icc.2018.8422723.
Wu, Songyang, Yong Zhang, and Xiao Chen. "Security Assessment of Dynamic Networks with an Approach of Integrating Semantic Reasoning and Attack Graphs." In 2018 IEEE 4th International Conference on Computer and Communications (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/compcomm.2018.8780998.