Relevant bibliographies by topics / Dynamic attack graph

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Academic literature on the topic 'Dynamic attack graph'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Dynamic attack graph"

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Pasupuleti, Murali Krishna. "Graph-Theoretical Intelligence for Adaptive Cybersecurity: Multi-Modal Models, Dynamic Defense, and Federated Threat Detection." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 347–56. https://doi.org/10.62311/nesx/rp3125.

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Abstract: The rise of sophisticated cyber threats demands adaptive, resilient, and intelligent cybersecurity mechanisms. Graph theory has emerged as a foundational tool for modeling, detecting, and responding to complex attack patterns. This paper critically interprets recent advancements across cybersecurity knowledge graphs, graph neural networks, attack graphs, and federated graph learning. A multi-modal, graph-theoretical intelligence framework is proposed to enhance dynamic defense and collaborative threat detection while maintaining privacy. Statistical analysis, visual models, and new s
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Li, Haoyang, Shimin Di, Calvin Hong Yi Li, Lei Chen, and Xiaofang Zhou. "Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense." Proceedings of the VLDB Endowment 17, no. 8 (2024): 2050–63. http://dx.doi.org/10.14778/3659437.3659457.

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Graph neural networks (GNNs) have achieved great success on various graph tasks. However, recent studies have revealed that GNNs are vulnerable to injective attacks. Due to the openness of platforms, attackers can inject malicious nodes with carefully designed edges and node features, making GNNs misclassify the labels of target nodes. To resist such adversarial attacks, recent researchers propose GNN defenders. They assume that the attack patterns have been known, e.g., attackers tend to add edges between dissimilar nodes. Then, they remove edges between dissimilar nodes from attacked graphs,
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Li, Jiate, Meng Pang, and Binghui Wang. "Practicable Black-Box Evasion Attacks on Link Prediction in Dynamic Graphs—a Graph Sequential Embedding Method." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26265–72. https://doi.org/10.1609/aaai.v39i25.34824.

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Link prediction in dynamic graphs (LPDG) has been widely applied to real-world applications such as website recommendation, traffic flow prediction, organizational studies, etc. These models are usually kept local and secure, with only the interactive interface restrictively available to the public. Thus, the problem of the black-box evasion attack on the LPDG model, where model interactions and data perturbations are restricted, seems to be essential and meaningful in practice. In this paper, we propose the first practicable black-box evasion attack method that achieves effective attacks agai
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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.

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As the cloud infrastructure is simultaneously shared by millions of consumers, heinous use of cloud resources are also increasing. It makes ways to attackers to set up attacks by exploiting the vulnerabilities. And obviously, these attacks are leading to severe disasters as innocent consumers are unknowingly sharing cloud resources with harmful attackers. To prevent the occurrence of cloud attacks, attack graph based framework is proposed in this paper. Here, an attack path sketches an attack scenario by a streak of threats ranging in severity rating that shows how popular a particular cloud n
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Mangesh Pujari. "Edge AI-Powered Cyber-Physical Attack Mitigation: A Graph Reinforcement Learning Approach for Autonomous Threat Response." Journal of Information Systems Engineering and Management 10, no. 44s (2025): 953–66. https://doi.org/10.52783/jisem.v10i44s.8692.

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Cyber-physical systems (CPS) at the edge require real-time autonomous defense mechanisms. We propose GRL-Shield, a graph reinforcement learning (GRL) framework that models CPS networks as dynamic graphs and autonomously mitigates multi-vector attacks. Using attention-based graph neural networks (GATs) and proximal policy optimization (PPO), GRL-Shield reduces false positives by 34% while maintaining 98.6% attack detection rate on the SWaT dataset. Edge deployment on NVIDIA Jetson AGX Orin shows sub-second response times, outperforming rule-based IDS by 60% in mitigation speed.
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Pal, Arunangshu, and Prasenjit Choudhury. "Mitigating Black Hole Attacks in AODV Routing Protocol Using Dynamic Graph." Mapana - Journal of Sciences 11, no. 4 (2012): 65–76. http://dx.doi.org/10.12723/mjs.23.5.

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With the advancement of wireless technologies, Mobile Ad hoc NETwork (MANET) has been an important field of study. MANETs find useful applications in the real world, for example in military battlefield and disaster management. Since MANET is dynamic in nature, it must be represented by dynamic graph. Evolving graph, a form of dynamic graph, may be used for the purpose. When we talk about a network, a routing protocol comes into the question, and one of the most popular routing protocols is AODV. However, since AODV suffers from a drawback that it may be a victim of black hole attack, we need t
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Rajeshwari, T., and C. Thangamani. "Attack Impact Discovery and Recovery with Dynamic Bayesian Networks." Asian Journal of Computer Science and Technology 8, S1 (2019): 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.

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The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the d
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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.

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We look at dynamic programming algorithms for propositional model counting, also called #SAT, and MaxSAT. Tools from graph structure theory, in particular treewidth, have been used to successfully identify tractable cases in many subfields of AI, including SAT, Constraint Satisfaction Problems (CSP), Bayesian reasoning, and planning. In this paper we attack #SAT and MaxSAT using similar, but more modern, graph structure tools. The tractable cases will include formulas whose class of incidence graphs have not only unbounded treewidth but also unbounded clique-width. We show that our algorithms
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Cerotti, Davide, Daniele Codetta Raiteri, Giovanna Dondossola, et al. "SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems." Energies 17, no. 16 (2024): 3882. http://dx.doi.org/10.3390/en17163882.

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SecuriDN v. 0.1 is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that
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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.

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Dissertations / Theses on the topic "Dynamic attack graph"

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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.

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It is widely accepted that modern computer networks (often presented as a heterogeneous collection of functioning organisations, applications, software, and hardware) contain vulnerabilities. This research proposes a new methodology to compute a dynamic severity cost for each state. Here a state refers to the behaviour of a system during an attack; an example of a state is where an attacker could influence the information on an application to alter the credentials. This is performed by utilising a modified variant of the Common Vulnerability Scoring System (CVSS), referred to as a Dynamic Vuln
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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.

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De nos jours, les réseaux informatiques sont utilisés dans de nombreux domaines et leur défaillance peut avoir un fort impact sur notre vie quotidienne. L'évaluation de leur sécurité est une nécessité pour réduire le risque de compromission par un attaquant. Néanmoins, les solutions proposées jusqu'à présent sont rarement adaptées à la grande complexité des systèmes informatiques modernes. Elles reposent souvent sur un travail humain trop important et les algorithmes utilisés ne sont pas assez performants. De plus, l'évolution du système dans le temps est rarement modélisée et n'est donc pas p
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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.

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Les systèmes d'information sont une cible de plus en plus attractive pour les attaquants. Dans cette thèse de doctorat, nous construisons une méthodologie complète d'analyse statique et dynamique de risque prenant en compte la connaissance à priori d'un système avec les événements dynamiques, afin de proposer des réponses permettant d'empêcher les attaques futures. Tout d'abord, nous étudions comment corriger les attaques potentielles qui peuvent arriver dans un système, en s'appuyant sur les graphes d'attaque logiques. Nous proposons une méthodologie de remédiation corrigeant les chemins d'at
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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.

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Les systèmes d'information sont une cible de plus en plus attractive pour les attaquants. Dans cette thèse de doctorat, nous construisons une méthodologie complète d'analyse statique et dynamique de risque prenant en compte la connaissance à priori d'un système avec les événements dynamiques, afin de proposer des réponses permettant d'empêcher les attaques futures. Tout d'abord, nous étudions comment corriger les attaques potentielles qui peuvent arriver dans un système, en s'appuyant sur les graphes d'attaque logiques. Nous proposons une méthodologie de remédiation corrigeant les chemins d'at
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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.

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A (self-) adaptive system is a system that can dynamically adapt its behavior or structure during execution to "adapt" to changes to its environment or the system itself. From a security standpoint, there has been some research pertaining to (self-) adaptive systems in general but not enough care has been shown towards the adaptation itself. Security of systems can be reasoned about using threat models to discover security issues in the system. Essentially that entails abstracting away details not relevant to the security of the system in order to focus on the important aspects related to secu
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Mensah, Pernelle. "Generation and Dynamic Update of Attack Graphs in Cloud Providers Infrastructures." Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0011.

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Dans les infrastructures traditionnelles, les graphes d’attaque permettent de brosser un tableau de la sécurité, car ils sont un modèle décrivant les différentes étapes suivies par un attaquant dans le but de compromettre un actif du réseau. Ces graphes peuvent ainsi servir de base à l’évaluation automatisée des risques, en s’appuyant sur l’identification et l’évaluation des actifs essentiels. Cela permet de concevoir des contre-mesures proactives et réactives pour la réduction des risques et peut être utilisé pour la surveillance et le renforcement de la sécurité du réseau.Cette thèse vise à
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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.

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Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a genera
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Book chapters on the topic "Dynamic attack graph"

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Grammatikakis, Konstantinos-Panagiotis, and Nicholas Kolokotronis. "Attack Graph Generation." In Cyber-Security Threats, Actors, and Dynamic Mitigation. CRC Press, 2021. http://dx.doi.org/10.1201/9781003006145-8.

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Zheng, Dongyang, Chengliang Gao, Jiaxu Xing, Ximing Chen, Rongrong Chen, and Jing Qiu. "Dynamic Analysis of Attack Paths Based on Bayesian Attack Graph." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4506-0_4.

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Gain, Ayan, and Mridul Sankar Barik. "Attack Graph Based Security Metrics for Dynamic Networks." In Information Systems Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49099-6_7.

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Wang, Haiping, Binbin Li, Tianning Zang, et al. "Real-Time Aggregation for Massive Alerts Based on Dynamic Attack Granularity Graph." In Science of Cyber Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45933-7_14.

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Xia, Hui, Yanan Jiang, Mingda Ma, Rui Zhang, and Yang You. "Topological Vulnerability-Based Imperceptible Node Injection Attack Against Dynamic Graph Neural Network." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1093-8_45.

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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. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44355-8_12.

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AbstractIn this paper, we posit how semi-static (i.e., not changing very often) complex computer network-based intelligence using graph-based analytics can become enablers of Cyber Situational Awareness (CSA) (i.e., perception, comprehension, and projection of situations in a cyber environment). A plethora of newly surfaced cyber security researchers have used graph-based analytics to facilitate particular down tasks in dynamic complex cyber environments. This includes graph-, node- and edge-level detection, classification, and others (e.g., credit card fraudulent transactions as an edge class
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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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59013-0_10.

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Wang, Dongqi, Ruiguo Hu, Dongming Chen, Zeyu Lv, and Jie Wang. "Guarding Graph Neural Networks Against Backdoor Attacks-A Training Loss Dynamics Approach." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-95-0006-2_21.

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Makhijani, Jagdish, Yashwant Pathak, and Soumya Bajpai. "AI-Enhanced Attack Graphs Using Markov Decision Processes for Proactive Threat Hunting and Risk Forecasting." In Artificial Intelligence in Cybersecurity for Risk Assessment and Transparent Threat Detection Frameworks. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552029-04.

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The increasing sophistication of cyber threats and the expanding attack surface of modern networks necessitate advanced methodologies for proactive risk assessment and threat mitigation. Traditional attack graphs provide a structured representation of potential attack paths but often struggle with scalability, adaptability, and real-time threat intelligence integration. To address these limitations, this chapter explores the integration of AI-enhanced attack graphs with Markov Decision Processes (MDPs) for proactive threat hunting and cyber risk forecasting. AI-driven techniques, including gra
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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.

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Knowledge graph-based dialogue systems are capable of generating more informative responses and can implement sophisticated reasoning mechanisms. However, these models do not take into account the sparseness and incompleteness of knowledge graph (KG) and cannot be applied to dynamic KG. This paper proposes a dynamic Knowledge graph-based dialogue generation method with improved adversarial Meta-Learning (ADML). ADML formulates dynamic knowledge triples as a problem of adversarial attack and incorporates the objective of quickly adapting to dynamic knowledge-aware dialogue generation. The model
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Conference papers on the topic "Dynamic attack graph"

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Chen, Xinhao, Houding Zhang, Dongying Gao, Jie Fu, Zhilei Lv, and Zheng Li. "Research on Dynamic Anomaly Detection Method of Edge Devices Based on Attack Chain Knowledge Graph." In 2025 IEEE 8th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2025. https://doi.org/10.1109/itoec63606.2025.10968854.

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Yang, Wei, Fangyuan Hou, Zhuoyue Jia, et al. "A Dynamic Risk Assessment Method for Industrial Control Network Security Based on Bayesian Attack Graph." In 2025 5th International Conference on Computer, Control and Robotics (ICCCR). IEEE, 2025. https://doi.org/10.1109/icccr65461.2025.11072640.

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Tan, Mengmi, Jianyi Liu, and Ru Zhang. "DI-GAE: A Dynamic and Resource-Efficient Attack Detection Framework with Incremental Learning and Graph Autoencoders." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00314.

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Satpathy, Utkalika, Harsh Borse, and Sandip Chakraborty. "Towards Generating a Robust, Scalable and Dynamic Provenance Graph for Attack Investigation over Distributed Microservice Architecture." In 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). IEEE, 2025. https://doi.org/10.1109/comsnets63942.2025.10885639.

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Jiang, Mingsi, Mi Wen, Yun Xiong, and Weiwei Li. "An Approach for APT Attack Scenario Construction Based on Dynamic Attack Graphs." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901487.

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Ngo, Huy Q., Mingyu Guo, and Hung Nguyen. "Catch Me if You Can: Effective Honeypot Placement in Dynamic AD Attack Graphs." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications. IEEE, 2024. http://dx.doi.org/10.1109/infocom52122.2024.10621210.

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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.

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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.

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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.

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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. ACM, 2020. http://dx.doi.org/10.1145/3403746.3403897.

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