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Journal articles on the topic 'Dynamic attack graph'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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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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 (2013): 51–61. http://dx.doi.org/10.2478/cait-2013-0037.

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Abstract With the rapid popularity of Internet and information technology, local area network is becoming insecure. Along with the improving advantages, security threats are emerging continually and bringing great pressure and challenges. An identification and analysis method for network real-time threats is proposed to accurately assess and master the current network security situation, and thereby preferably guide a dynamic defense. This method recognizes the current threats and predicts the subsequent threats by modeling attack scenarios and simulating attack state transferring. The threat

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Vidhin Patel, Meet Chaudhary, Parth Patel, and Jitendra B Upadhyay. "A Comprehensive Study of Graph QL Security Challenges." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1235–47. https://doi.org/10.47392/irjaem.2025.0203.

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GraphQL is a highly flexible query language utilized for flexible API construction. It offers excellent benefits over conventional APIs because of its flexible nature and strong queries. It provides numerous benefits, but because of its dynamic nature and absence of built-in mechanisms, it is vulnerable to very critical attacks like injection attacks, denial of service (DoS) attacks, broken authentication and authorization, request forgery, schema introspection, and bad exception handling. By studying in detail, this paper discloses how the GraphQL APIs can be attacked by an attacker using a v

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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.

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By now, Attack Graph (AG) is widely applied to the field of network security assessment. In the AG, each vertex has a value that implies the probability of the exploit and each edge represents the relationship between the exploits. In this paper we design an AG model and propose an approach which integrates the AG model with the Dynamic Bayesian Network (DBN). The approach not only strengthens the rationality of uncertain reasoning, but also provides a quantitative assessment of network security status. We evaluated the approach by experiment. The results showed that our model is rather accura

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Asst., Prof. Snehal Abhijit Tikore. "Exploring the Role of Graph Theory in Dynamic Network Security: A Qualitative Inquiry." International Journal of Advance and Applied Research S6, no. 23 (2025): 212–16. https://doi.org/10.5281/zenodo.15195068.

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<em>With the increasing complexity of modern networks, ensuring security in dynamic environments has become a critical challenge. Graph theory provides a robust mathematical framework for analyzing and securing these networks. This paper explores the role of graph theory in dynamic network security, focusing on intrusion detection, attack modeling, and network resilience. Through a qualitative inquiry, we examine existing literature and methodologies that leverage graph-based approaches for enhanced security. Our findings suggest that graph theory significantly contributes to understanding and

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Nguyen, Thanh-Tung, and Minho Park. "EL-GNN: A Continual-Learning-Based Graph Neural Network for Task-Incremental Intrusion Detection Systems." Electronics 14, no. 14 (2025): 2756. https://doi.org/10.3390/electronics14142756.

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Modern network infrastructures have significantly improved global connectivity while simultaneously escalating network security challenges as sophisticated cyberattacks increasingly target vital systems. Intrusion Detection Systems (IDSs) play a crucial role in identifying and mitigating these threats, and recent advances in machine-learning-based IDSs have shown promise in detecting evolving attack patterns. Notably, IDSs employing Graph Neural Networks (GNNs) have proven effective at modeling the dynamics of network traffic and internal interactions. However, these systems suffer from Catast

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Pratomo, Baskoro Adi, Muhammad Farhan Haykal, Hudan Studiawan, and Diana Purwitasari. "Graph-Structured Network Traffic Modelling for Anomaly-Based Intrusion Detection." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 14, no. 2 (2025): 240–49. https://doi.org/10.23887/janapati.v14i2.94959.

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The increasing complexity of cyber threats demands more advanced network intrusion detection systems (NIDS) capable of identifying both known and emerging attack patterns. In this study, we propose a graph-based anomaly detection approach for network intrusion detection, where network traffic is modeled as graph structures capturing both attribute and topological information. Five graph anomaly detection models—DOMINANT, OCGNN, AnomalyDAE, GAE, and CONAD—are implemented and evaluated on the UNSW-NB15 dataset. The constructed graphs use info_message attributes as nodes, with edges representing

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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 (2024): 13374–82. http://dx.doi.org/10.1609/aaai.v38i12.29239.

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Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been developed to effectively capture the evolving patterns in dynamic graphs. While these models have demonstrated their superiority, being widely adopted in various important fields, their vulnerabilities against adversarial attacks remain largely unexplored. In this paper, we propose T-SPEAR, a simple and effective adversarial attack method for link prediction on continuous-time dynamic graphs, focusing on investigating t

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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 (2023): 5649–56. http://dx.doi.org/10.1609/aaai.v37i5.25701.

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Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack pa

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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.

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Kavallieratos, Georgios, Nabin Chowdhury, Sokratis Katsikas, Vasileios Gkioulos, and Stephen Wolthusen. "Threat Analysis for Smart Homes." Future Internet 11, no. 10 (2019): 207. http://dx.doi.org/10.3390/fi11100207.

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The development and deployment of highly dynamic, cyber+connected operational environments, such as smart homes, smart cities, and smart transportation systems, is increasing. The security analysis of such dynamic environments necessitates the use of dynamic risk assessment methodologies and the modeling of dynamically changing states. In this paper, we focus on the smart home environment, where the deployment of IoT devices increase the attack surface. We examine existing dynamic risk assessment methodologies, and by leveraging a smart home reference architecture we identify the security risk

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Sravani, Mrs P., K. Shiva Kumar, P. Jai Raj Sai, K. Mohan Aditya, and S. S. K. Chaitanya. "Botnet Attacks in Computer Network Security." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1826–34. https://doi.org/10.22214/ijraset.2025.67676.

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Abstract: This paper introduces a deep learning-oriented framework for identifying and preventing botnet-caused DDoS attacks in Software-Defined Networking (SDN). Conventional security techniques experience high false alarms and slow detection because of the dynamic nature of botnet attacks. To improve this, the system utilizes Convolutional Neural Networks (CNNs) to detect anomalies in real-time and a graph theory-oriented dynamic flow management algorithm for preventing attacks. Experimental assessments with CICIDS 2017 and Bot-IoT datasets and a simulated SDN testbed (Mininet) indicate that

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Alwasel, Bader, Abdulaziz Aldribi, Mohammed Alreshoodi, Ibrahim S. Alsukayti, and Mohammed Alsuhaibani. "Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection." Applied Sciences 13, no. 13 (2023): 7774. http://dx.doi.org/10.3390/app13137774.

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In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of gra

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Wan, Shanshan, and Ying Liu. "A security detection approach based on autonomy-oriented user sensor in social recommendation network." International Journal of Distributed Sensor Networks 18, no. 3 (2022): 155013292210824. http://dx.doi.org/10.1177/15501329221082415.

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User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on aut

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Bopche, Ghanshyam S., and Babu M. Mehtre. "Graph similarity metrics for assessing temporal changes in attack surface of dynamic networks." Computers & Security 64 (January 2017): 16–43. http://dx.doi.org/10.1016/j.cose.2016.09.010.

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Sen, Saurabh Kumar, and Anuradha Deolase. "Analyse Cyberattack at Organizations using Logistic Regression Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 518–25. http://dx.doi.org/10.22214/ijraset.2022.40663.

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Abstract: Ransomware cyberattacks have grown in severity, effectiveness to cause damage, and ease of execution during the last decade. Advanced ransomware detection technologies must be included with traditional anti-malware procedures. The results of a study and analysis of ransomware attack risk are presented in this work, with the goal of identifying the characteristics that separate ransomware from other malware and benign executable files with the help of detected logs. The ransomware's normal behaviour and structure are determined by statically and dynamically analysing the executable bi

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Shahpasand, Maryam, Mehdi Shajari, Seyed Alireza Hashemi Golpaygani, and Hoda Ghavamipoor. "A comprehensive security control selection model for inter-dependent organizational assets structure." Information & Computer Security 23, no. 2 (2015): 218–42. http://dx.doi.org/10.1108/ics-12-2013-0090.

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Purpose – This paper aims to propose a comprehensive model to find out the most preventive subset of security controls against potential security attacks inside the limited budget. Deploying the appropriate collection of information security controls, especially in information system-dependent organizations, ensures their businesses' continuity alongside with their effectiveness and efficiency. Design/methodology/approach – Impacts of security attacks are measured based on interdependent asset structure. Regarding this objective, the asset operational dependency graph is mapped to the security

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Vikas and Dr Shashiraj Teotia. "Optimization of Graph Neural Networks for Real-Time Intrusion Detection in Dynamic Mobile Ad-Hoc Networks." International Journal of Environmental Sciences 11, no. 11s (2025): 740–48. https://doi.org/10.64252/79452g17.

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AD-HOC Mobile Networks (Manets) face significant security challenges due to their dynamic topology and vulnerability to sophisticated cyber threats. Traditional IDS detection (IDS) systems usually cease to provide real -time adaptive protection against evolutionary attacks on these resource restriction environments. This article presents an optimized graphic neural network IDS (GNN-IDS IDS) that addresses these limitations through three main innovations: dynamic graphs representation learning, light architecture design and online adaptation mechanisms. Our approach reaches 93.2%detection accur

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Kholidy, Hisham A. "Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method." Sensors 22, no. 1 (2021): 9. http://dx.doi.org/10.3390/s22010009.

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Overall, 5G networks are expected to become the backbone of many critical IT applications. With 5G, new tech advancements and innovation are expected; 5G currently operates on software-defined networking. This enables 5G to implement network slicing to meet the unique requirements of every application. As a result, 5G is more flexible and scalable than 4G LTE and previous generations. To avoid the growing risks of hacking, 5G cybersecurity needs some significant improvements. Some security concerns involve the network itself, while others focus on the devices connected to 5G. Both aspects pres

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Kim, Jiyeon, and Hyong S. Kim. "Intrusion Detection Based on Spatiotemporal Characterization of Cyberattacks." Electronics 9, no. 3 (2020): 460. http://dx.doi.org/10.3390/electronics9030460.

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As attack techniques become more sophisticated, detecting new and advanced cyberattacks with traditional intrusion detection techniques based on signature and anomaly is becoming challenging. In signature-based detection, not only do attackers bypass known signatures, but they also exploit unknown vulnerabilities. As the number of new signatures is increasing daily, it is also challenging to scale the detection mechanisms without impacting performance. For anomaly detection, defining normal behaviors is challenging due to today’s complex applications with dynamic features. These complex and dy

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Gao, Yazhuo, Guomin Zhang, and Changyou Xing. "A Multiphase Dynamic Deployment Mechanism of Virtualized Honeypots Based on Intelligent Attack Path Prediction." Security and Communication Networks 2021 (October 21, 2021): 1–15. http://dx.doi.org/10.1155/2021/6378218.

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As an important deception defense method, a honeypot can be used to enhance the network’s active defense capability effectively. However, the existing rigid deployment method makes it difficult to deal with the uncertain strategic attack behaviors of the attackers. To solve such a problem, we propose a multiphase dynamic deployment mechanism of virtualized honeypots (MD2VH) based on the intelligent attack path prediction method. MD2VH depicts the attack and defense characteristics of both attackers and defenders through the Bayesian state attack graph, establishes a multiphase dynamic deployme

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Hu, Hao, Hongqi Zhang, Yuling Liu, and Yongwei Wang. "Quantitative Method for Network Security Situation Based on Attack Prediction." Security and Communication Networks 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/3407642.

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Multistep attack prediction and security situation awareness are two big challenges for network administrators because future is generally unknown. In recent years, many investigations have been made. However, they are not sufficient. To improve the comprehensiveness of prediction, in this paper, we quantitatively convert attack threat into security situation. Actually, two algorithms are proposed, namely, attack prediction algorithm using dynamic Bayesian attack graph and security situation quantification algorithm based on attack prediction. The first algorithm aims to provide more abundant

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He, Zhenxiang, Ziqi Zhao, and Zhihao Liu. "A Syntax-Aware Graph Network with Contrastive Learning for Threat Intelligence Triple Extraction." Symmetry 17, no. 7 (2025): 1013. https://doi.org/10.3390/sym17071013.

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As Advanced Persistent Threats (APTs) continue to evolve, constructing a dynamic cybersecurity knowledge graph requires precise extraction of entity–relationship triples from unstructured threat intelligence. Existing approaches, however, face significant challenges in modeling low-frequency threat associations, extracting multi-relational entities, and resolving overlapping entity scenarios. To overcome these limitations, we propose the Symmetry-Aware Prototype Contrastive Learning (SAPCL) framework for joint entity and relation extraction. By explicitly modeling syntactic symmetry in attack-

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Qiang, Weizhong, Shizhen Wang, Hai Jin, and Jiangying Zhong. "Fine-Grained Control-Flow Integrity Based on Points-to Analysis for CPS." Security and Communication Networks 2018 (October 17, 2018): 1–11. http://dx.doi.org/10.1155/2018/3130652.

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A cyber-physical system (CPS) is known as a mix system composed of computational and physical capabilities. The fast development of CPS brings new security and privacy requirements. Code reuse attacks that affect the correct behavior of software by exploiting memory corruption vulnerabilities and reusing existing code may also be threats to CPS. Various defense techniques are proposed in recent years as countermeasures to emerging code reuse attacks. However, they may fail to fulfill the security requirement well because they cannot protect the indirect function calls properly when it comes to

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Qin, Zhi-Quan, Hong-Zuo Xu, Xing-Kong Ma, and Yong-Jun Wang. "Interaction Context-Aware Network Behavior Anomaly Detection for Discovering Unknown Attacks." Security and Communication Networks 2022 (April 11, 2022): 1–24. http://dx.doi.org/10.1155/2022/3595304.

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Network behavior anomaly detection is an effective approach to discover unknown attacks, where generating high-efficacy network behavior representation is one of the most crucial parts. Nowadays, complicated network environments and advancing attack techniques make it more challenging. Existing methods cannot yield satisfied representations that express the semantics of network behaviors comprehensively. To tackle this problem, we propose XNBAD, a novel unsupervised network behavior anomaly detection framework, in this work. It integrates the timely high-order host states under the dynamic int

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Zhou, Ying, Zhiyong Zhang, Kejing Zhao, and Zhongya Zhang. "A novel dynamic vulnerability assessment method for Industrial Control System based on vulnerability correlation attack graph." Computers and Electrical Engineering 119 (October 2024): 109482. http://dx.doi.org/10.1016/j.compeleceng.2024.109482.

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Turnip, Togu Novriansyah, William Suarez Lumbantobing, David Christian Sitorus, and Friska Laurenzia Sianturi. "Software Watermarking Dinamis dengan Algoritme Collberg-Thomborson Dan Parent Pointer Graf pada Aplikasi Android." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 4 (2021): 831. http://dx.doi.org/10.25126/jtiik.2021844500.

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<p class="15"><em>Smartphone</em> merupakan alat umum yang digunakan masyarakat dalam kehidupan sehari-hari. Sistem operasi yang paling banyak digunakan pada smartphone adalah Android. Aplikasi pada Android dapat diperoleh tidak hanya di Play Store saja, namun juga dapat ditemukan secara bebas di <em>website-website</em> yang berada di internet. Oleh karena itu aplikasi Android rentan terhadap pembajakan.<em> Software watermarking</em> merupakan metode umum yang biasanya digunakan untuk mengantisipasi pembajakan perangkat lunak dengan menyisipkan infor

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Wang, Zhen, Yongjie Wang, Xinli Xiong, Qiankun Ren, and Jun Huang. "A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding." Entropy 27, no. 6 (2025): 622. https://doi.org/10.3390/e27060622.

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Faced with challenges posed by sophisticated cyber attacks and dynamic characteristics of cyberspace, the autonomous cyber defense (ACD) technology has shown its effectiveness. However, traditional decision-making methods for ACD are unable to effectively characterize the network topology and internode dependencies, which makes it difficult for defenders to identify key nodes and critical attack paths. Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this pa

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Song, Xuyan, Chen Chen, Baojiang Cui, and Junsong Fu. "Malicious JavaScript Detection Based on Bidirectional LSTM Model." Applied Sciences 10, no. 10 (2020): 3440. http://dx.doi.org/10.3390/app10103440.

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JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection

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Wang, Chundong, Zheli Liu, and Tong Zhao. "An activity theory model for dynamic evolution of attack graph based on improved least square genetic algorithm." International Journal of Information and Computer Security 12, no. 4 (2020): 397. http://dx.doi.org/10.1504/ijics.2020.10028756.

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Wang, Chundong, Tong Zhao, and Zheli Liu. "An activity theory model for dynamic evolution of attack graph based on improved least square genetic algorithm." International Journal of Information and Computer Security 12, no. 4 (2020): 397. http://dx.doi.org/10.1504/ijics.2020.107448.

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Heigl, Michael, Enrico Weigelt, Andreas Urmann, Dalibor Fiala, and Martin Schramm. "Exploiting the Outcome of Outlier Detection for Novel Attack Pattern Recognition on Streaming Data." Electronics 10, no. 17 (2021): 2160. http://dx.doi.org/10.3390/electronics10172160.

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Future-oriented networking infrastructures are characterized by highly dynamic Streaming Data (SD) whose volume, speed and number of dimensions increased significantly over the past couple of years, energized by trends such as Software-Defined Networking or Artificial Intelligence. As an essential core component of network security, Intrusion Detection Systems (IDS) help to uncover malicious activity. In particular, consecutively applied alert correlation methods can aid in mining attack patterns based on the alerts generated by IDS. However, most of the existing methods lack the functionality

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Xu, Hanyi, Guozhen Cheng, Xiaohan Yang, Wenyan Liu, Dacheng Zhou, and Wei Guo. "Multi-Dimensional Moving Target Defense Method Based on Adaptive Simulated Annealing Genetic Algorithm." Electronics 13, no. 3 (2024): 487. http://dx.doi.org/10.3390/electronics13030487.

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Due to the fine-grained splitting of microservices and frequent communication between microservices, the exposed attack surface of microservices has exploded, facilitating the lateral movement of attackers between microservices. To solve this problem, a multi-dimensional moving target defense method based on an adaptive simulated annealing genetic algorithm (MD2RS) is proposed. Firstly, according to the characteristics of microservices in the cloud, a microservice attack graph is proposed to quantify the attack scenario of microservices in the cloud so as to conveniently and intuitively observ

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Craandijk, Dennis, and Floris Bex. "Enforcement Heuristics for Argumentation with Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5573–81. http://dx.doi.org/10.1609/aaai.v36i5.20497.

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In this paper, we present a learning-based approach to the symbolic reasoning problem of dynamic argumentation, where the knowledge about attacks between arguments is incomplete or evolving. Specifically, we employ deep reinforcement learning to learn which attack relations between arguments should be added or deleted in order to enforce the acceptability of (a set of) arguments. We show that our Graph Neural Network (GNN) architecture EGNN can learn a near optimal enforcement heuristic for all common argument-fixed enforcement problems, including problems for which no other (symbolic) solvers

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Song, Qijie, Tieming Chen, Tiantian Zhu, Mingqi Lv, Xuebo Qiu, and Zhiling Zhu. "APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining." Applied Sciences 15, no. 11 (2025): 5872. https://doi.org/10.3390/app15115872.

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Advanced Persistent Threats (APTs) challenge cybersecurity due to their stealthy, multi-stage nature. For the provenance graph based on fine-grained kernel logs, existing methods have difficulty distinguishing behavior boundaries and handling complex multi-entity dependencies, which exhibit high false positives in dynamic environments. To address this, we propose a Hypergraph Attention Network framework for APT detection. First, we employ anomaly node detection on provenance graphs constructed from kernel logs to select seed nodes, which serve as starting points for discovering overlapping beh

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He, Fangzhou, Wei Bai, and Zhiqi Wang. "Optimization of the Decision-making Process of Digital Twins in Network Security Based on Graph Neural Networks." Network Security 2024, no. 10 (2025): 73–83. https://doi.org/10.70985/rpa1ez48.

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The network security model based on static rules lacks flexibility and adaptability, and it is difficult to adapt to and respond to dynamically changing network threats in real-time. Therefore, this paper proposes a method for optimizing the decision-making process of digital twins (DTs) in network security based on graph neural networks (GNNs). First, this paper applies the Adam optimizer to adjust the learning rate by optimizing the GNN structure, combines the cross entropy loss function to improve the attack recognition ability, and uses L2 regularization and Dropout to prevent overfitting

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Аверьянов, В. С., and И. Н. Карцан. "ASSESSMENT OF THE SECURITY OF CYBER-PHYSICAL SYSTEMS BASED ON A GENERAL GRAPH." Южно-Сибирский научный вестник, no. 1(41) (February 28, 2022): 30–35. http://dx.doi.org/10.25699/sssb.2022.41.1.013.

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Динамичное развитие IT– отрасли, повышение автоматизации и технологичности бизнес - процессов, рост числа организаций внедряющих облачную инфраструктуру, а также повсеместная цифровизация, создает благоприятную среду для масштабирования хакерских атак в сфере кибербезопасности. При этом векторами целевых атак являются: социальная инженерия, неквалифицированные пользователи цифровых сервисов, эксплуатация уязвимостей основных систем и сопутствующей инфраструктуры. Вопросы своевременного реагирования, локализации и выявления киберинцидентов являются насущными, требующими временных и финансовых з

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Ustun, Taha Selim, and Saqib Ayyubi. "Automated Network Topology Extraction Based on Graph Theory for Distributed Microgrid Protection in Dynamic Power Systems." Electronics 8, no. 6 (2019): 655. http://dx.doi.org/10.3390/electronics8060655.

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Unlike conventional grids, microgrids may utilize different connections and the overall topology can be variable. Considering this, it is required to develop a new protection concept/scheme for safe operation. Maintaining proper selective operation of the relays in these dynamic microgrid structures is a challenge itself. This requires monitoring the connections and updating time delays of the relays which will ensure the desired hierarchy in the system. In this paper, a novel approach has been taken where electrical networks are modeled according to graph theory. Smart algorithms, such as net

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Gopi R. "Cyber-Physical System Defense Against Structured False Data Injection Attacks Using an Adaptive Security Framework with Passivity Enhancement." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 234–44. https://doi.org/10.52783/jisem.v10i43s.8360.

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System integrity, operation, and significant breakdowns can be compromised by coordinated False Data Injection Attacks (FDIAs), which are increasingly prevalent in Cyber-Physical Systems (CPS). Because they are dynamic and constantly evolving, these threats often bypass traditional security controls. The prompt identification of complex FDIAs, the reduction of anomaly detection false positives, and the maintenance of system stability in hostile environments are some important problems tackled. The Passivity-Enhanced Adaptive Security Framework (PEASF) is introduced in this work as a mechanism

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Zhang, Quan, Chijin Zhou, Yiwen Xu, et al. "Building Dynamic System Call Sandbox with Partial Order Analysis." Proceedings of the ACM on Programming Languages 7, OOPSLA2 (2023): 1253–80. http://dx.doi.org/10.1145/3622842.

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Attack surface reduction is a security technique that secures the operating system by removing the unnecessary code or features of a program. By restricting the system calls that programs can use, the system call sandbox is able to reduce the exposed attack surface of the operating system and prevent attackers from damaging it through vulnerable programs. Ideally, programs should only retain access to system calls they require for normal execution. Many researchers focus on adopting static analysis to automatically restrict the system calls for each program. However, these methods do not adjus

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Inibhunu, Catherine, and Scott Langevin. "Adaptive Visualization of Complex Networks with FocalPoint." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 233–37. http://dx.doi.org/10.1177/1541931213601052.

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Maintaining situational awareness of a dynamic global computer network that consists of ten to hundreds of thousands of computers is a complex task for cyber administrators and operators looking to understand, plan and conduct operations in real time. Currently, cyber specialists must manually navigate complex networks by continuous cycles of overviews, drilldowns and manually mapping network incidents to mission impact. This is inefficient as manually maneuvering of network data is laborious, induces cognitive overload, and is prone to errors caused by distractive information resulting in imp

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