Relevant bibliographies by topics / Cyberattack prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Cyberattack prediction'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Cyberattack prediction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Cyberattack prediction"

1

Dong, Jiping, Mengmeng Hao, Fangyu Ding, et al. "A Novel Multimodal Data Fusion Framework: Enhancing Prediction and Understanding of Inter-State Cyberattacks." Big Data and Cognitive Computing 9, no. 3 (2025): 63. https://doi.org/10.3390/bdcc9030063.

Full text
Abstract:
Inter-state cyberattacks are increasingly becoming a major hidden threat to national security and global order. However, current prediction models are often constrained by single-source data due to insufficient consideration of complex influencing factors, resulting in limitations in understanding and predicting cyberattacks. To address this issue, we comprehensively consider multiple data sources including cyberattacks, bilateral interactions, armed conflicts, international trade, and national attributes, and propose an interpretable multimodal data fusion framework for predicting cyberattack
APA, Harvard, Vancouver, ISO, and other styles
2

Yermalovich, Pavel. "Determining the Probability of Cyberattacks." European Journal of Engineering and Formal Sciences 4, no. 1 (2020): 46. http://dx.doi.org/10.26417/ejef.v4i1.p46-63.

Full text
Abstract:
The use of information is inextricably linked with its security. The presence of vulnerabilities enables a third party to breach the security of information. Threat modeling helps to identify those infrastructures, which would be most likely exposed to cyberattacks. In some cases, however, threat modeling can not be classified as sufficient method of protection. This paper entitled “Determining the probability of cyberattacks” presents an analysis of different techniques with an attempt to identify the most informative parameters and cyberattack prediction markers, which would lay the foundati
APA, Harvard, Vancouver, ISO, and other styles
3

Ryu, Sungwook, Jinsu Kim, Namje Park, and Yongseok Seo. "Preemptive Prediction-Based Automated Cyberattack Framework Modeling." Symmetry 13, no. 5 (2021): 793. http://dx.doi.org/10.3390/sym13050793.

Full text
Abstract:
As the development of technology accelerates, the Fourth Industrial Revolution, which combines various technologies and provides them as one service, has been in the spotlight, and services using big data, Artificial Intelligence (AI) and Internet of Things (IoT) are becoming more intelligent and helpful to users. As these services are used in various fields, attacks by attackers also occur in various areas and ways. However, cyberattacks by attackers may vary depending on the attacking pattern of the attacker, and the same vulnerability can be attacked from different perspectives. Therefore,
APA, Harvard, Vancouver, ISO, and other styles
4

Lee, Eungyu, Yongsoo Lee, and Teajin Lee. "Automatic False Alarm Detection Based on XAI and Reliability Analysis." Applied Sciences 12, no. 13 (2022): 6761. http://dx.doi.org/10.3390/app12136761.

Full text
Abstract:
Many studies attempt to apply artificial intelligence (AI) to cyber security to effectively cope with the increasing number of cyber threats. However, there is a black box problem such that it is difficult to understand the basis for AI prediction. False alarms for malware or cyberattacks can cause serious side effects. Due to this limitation, all AI predictions must be confirmed by an expert, which is a considerable obstacle to AI expansion. Compared to the increasing number of cyberattack alerts, the number of alerts that can be analyzed by experts is limited. This paper provides explainabil
APA, Harvard, Vancouver, ISO, and other styles
5

Pavel, Yermalovich, and Mejri Mohamed. "ONTOLOGY-BASED MODEL FOR SECURITY ASSESSMENT: PREDICTING CYBERATTACKS THROUGH THREAT ACTIVITY ANALYSIS." International Journal of Network Security & Its Applications (IJNSA) 12, no. 03 (2020): 01–22. https://doi.org/10.5281/zenodo.3887536.

Full text
Abstract:
The prediction of attacks is essential for the prevention of potential risk. Therefore, risk forecasting contributes a lot to the optimization of the information security budget. This article focuses on the ontology and stages of a cyberattack. It introduces the main representatives of the attacking side and describes their motivation.
APA, Harvard, Vancouver, ISO, and other styles
6

Maghrabi, Louai A., Ibrahim R. Alzahrani, Dheyaaldin Alsalman, Zenah Mahmoud AlKubaisy, Diaa Hamed, and Mahmoud Ragab. "Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems." Electronics 12, no. 19 (2023): 4091. http://dx.doi.org/10.3390/electronics12194091.

Full text
Abstract:
Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Gol
APA, Harvard, Vancouver, ISO, and other styles
7

Abbas, Sidra, Imen Bouazzi, Stephen Ojo, et al. "Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks." PeerJ Computer Science 10 (January 16, 2024): e1793. http://dx.doi.org/10.7717/peerj-cs.1793.

Full text
Abstract:
The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attackers/hackers as additional features are accessible online. The complexity of cyberattacks has grown to pose a bigger threat to public and private sector organizations. They undermine Internet businesses, tarnish company branding, and restrict access
APA, Harvard, Vancouver, ISO, and other styles
8

Ali, Mohanad Faeq, Mohammed Shakir Mohmood, Ban Salman Shukur, et al. "HCAP: Hybrid cyber attack prediction model for securing healthcare applications." PLOS One 20, no. 5 (2025): e0321941. https://doi.org/10.1371/journal.pone.0321941.

Full text
Abstract:
The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of cybersecurity attacks. These attacks threaten the confidentiality, integrity, and availability of sensitive healthcare data, raising concerns about the reliability of IoMT infrastructure. Addressing these challenges requires advanced cybersecurity measures to protect the dynamic IoMT ecosystem from evolving threats. T
APA, Harvard, Vancouver, ISO, and other styles
9

Bordel, Borja, Ramón Alcarria, and Tomás Robles. "Denial of Chain: Evaluation and prediction of a novel cyberattack in Blockchain-supported systems." Future Generation Computer Systems 116 (March 2021): 426–39. http://dx.doi.org/10.1016/j.future.2020.11.013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Ahmedabad, Akhil. "AI-Powered Cyber-Physical Systems: A New Frontier in Securing Healthcare from Cyber Attacks." Stallion Journal for Multidisciplinary Associated Research Studies 2, no. 4 (2023): 28–40. https://doi.org/10.55544/sjmars.2.4.4.

Full text
Abstract:
There is a rising need for adequate cybersecurity safeguards to protect patient data, medical equipment, and crucial infrastructure as healthcare systems become more digitized. Effective security solutions are required for these intricate settings because of the range of medical equipment used within this system, i.e., Mobile Devices (MD) and Body Sensor Nodes (BSN). Healthcare facilities may utilize artificial intelligence (AI) techniques and cyber-physical systems (CPS) to identify and thwart cyberattacks. A novel machine learning threat detection framework for safe healthcare data transfer
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Cyberattack prediction"

1

Bača, Jonatán. "Informační a kybernetické hrozby v roce 2019." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2020. http://www.nusl.cz/ntk/nusl-417712.

Full text
Abstract:
Diploma thesis focuses on information and cyber threats in 2019. It comprises theoretical basis for better understanding of the issue. Afterward the thesis describes the analysis of the current situation which combined several analyses primarily aimed on Czech companies. In the last part draft measures is created which contain predictions and preventive actions and recommendations for companies.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Cyberattack prediction"

1

Sathish Kumar, P., M. Karthiga, and E. Balamurugan. "Cyber ML-Based Cyberattack Prediction Framework in Healthcare Cyber-Physical Systems." In Computational Intelligence in Robotics and Automation. CRC Press, 2022. http://dx.doi.org/10.1201/9781003181668-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Khanmohammadi, Kobra, Zakeya Namrud, François Labrèche, and Raphaël Khoury. "ExploitabilityBirthMark: An Early Predictor of the Likelihood of Exploitation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87496-3_13.

Full text
Abstract:
Abstract In recent years, there has been a steady increase in the number of reported vulnerabilities (CVEs), increasing the workload of organizations trying to update their systems promptly. This underscores the need to prioritize specific critical vulnerabilities over others to effectively prevent cyberattacks. Unfortunately, the current methods available for assessing the exploitability of vulnerabilities have substantial shortcomings. In particular, they often consist in prediction models that encode data that may not be immediately available at the time a vulnerability is first reported. I
APA, Harvard, Vancouver, ISO, and other styles
3

Ryjov, Alexander P., and Igor F. Mikhalevich. "Hybrid Intelligence Framework for Improvement of Information Security of Critical Infrastructures." In Handbook of Research on Cyber Crime and Information Privacy. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5728-0.ch016.

Full text
Abstract:
Information infrastructures for corporations and governments (information and automated systems, telecommunication networks, and other elements) have dramatically changed in the last decades due to the broad usage of IoT, AI, mobile internet, and other advanced technologies. Protection against cyberattacks requires new solutions that correspond to an increased level of complexity for these infrastructures. Important tasks for these new tools are forecasting cyberattacks, developing and applying preventive protective measures to reduce the risk of information security incidents. For the predict
APA, Harvard, Vancouver, ISO, and other styles
4

Berki, Eleni, Juri Valtanen, Sunil Chaudhary, and Linfeng Li. "The Need for Multi-Disciplinary Approaches and Multi-Level Knowledge for Cybersecurity Professionals." In Multidisciplinary Perspectives on Human Capital and Information Technology Professionals. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5297-0.ch005.

Full text
Abstract:
Cybersecurity professionals face increased demand to acquire the knowledge and develop the skills required to keep citizens safe from cyberattacks, predict the latter with scientific methods, and advance citizens' social awareness. A proactive multidisciplinary approach against cyberattacks is effective via the combination of multidisciplinary and multi-professional knowledge. Increased public awareness with total quality multi-domain knowledge and social computing skills is likely to decrease cyberattacks' victims and improve cyber systems quality in general. This chapter 1) outlines the basi
APA, Harvard, Vancouver, ISO, and other styles
5

Singh, Bhupinder, Christian Kaunert, and Saurabh Chandra. "Relishing Machine Learning Intelligence Combating Cyber Threats." In Navigating Cyber Threats and Cybersecurity in the Software Industry. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6250-1.ch007.

Full text
Abstract:
Machine learning in cybersecurity involves leveraging artificial intelligence technologies to bolster the defense of digital systems and data against cyber threats. This approach employs machine learning, neural networks, and other AI methods to enhance the efficiency and effectiveness of detecting, preventing, and responding to cyberattacks. AI helps identify anomalies by learning typical network behavior, performs behavioral analysis to spot suspicious activities, and facilitates real-time monitoring for instant threat detection. The role of AI in cybersecurity includes anticipating potentia
APA, Harvard, Vancouver, ISO, and other styles
6

Singh, Bhupinder. "Appreciating Machine Learning Intelligence Combating Cyber Threats." In Democracy and Democratization in the Age of AI. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8749-8.ch014.

Full text
Abstract:
Machine learning in cybersecurity involves leveraging artificial intelligence technologies to bolster the defense of digital systems and data against cyber threats. This approach employs machine learning, neural networks, and other AI methods to enhance the efficiency and effectiveness of detecting, preventing, and responding to cyberattacks. AI helps identify anomalies by learning typical network behavior, performs behavioral analysis to spot suspicious activities, and facilitates real-time monitoring for instant threat detection. The role of AI in cybersecurity includes anticipating potentia
APA, Harvard, Vancouver, ISO, and other styles
7

Voloshin, Misha. "Predictive Network Defense." In Advances in Data Mining and Database Management. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5063-3.ch007.

Full text
Abstract:
Maintaining electronic devices in today's networked world is not for the faint of heart. The modern network administrator is tasked not only with keeping machines running but also with standing a constant and unerring vigil against cyberattack. A skilled admin learns to identify telltale signs that the network is in trouble, and to quickly evict intruders, repair damage, and reinforce the network's fortifications. No software today can replicate a trained admin's experience and talent. However, just as viruses and rootkits grow progressively more sophisticated with each passing year(Parikka, 2
APA, Harvard, Vancouver, ISO, and other styles
8

Tariq, Muhammad Usman. "AI-Powered Cybersecurity." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8034-5.ch007.

Full text
Abstract:
In this chapter the critical role that artificial intelligence (AI) plays in improving cybersecurity in environmentally conscious sustainable IT systems is examined. Green IT initiatives are becoming essential for minimizing environmental impact but safeguarding these systems from cyberattacks has become more difficult. This chapter extensively looks at how AI helps build robust flexible defenses for green technology infrastructures like smart grids, renewable energy systems, and sustainable data storage. AI-driven cybersecurity offers a customized strategy for each system's particular vulnera
APA, Harvard, Vancouver, ISO, and other styles
9

Pasupuleti, Murali Krishna. "AI’s Role in Global Stability, Diplomacy, and Peacebuilding." In AI-Powered Diplomacy and Conflict Resolutions. National Education Services, 2025. https://doi.org/10.62311/nesx/32268.

Full text
Abstract:
Abstract Artificial Intelligence (AI) is transforming the landscape of global stability, diplomacy, and peacebuilding by enhancing conflict prediction, diplomatic negotiations, cybersecurity, and humanitarian interventions. AI-driven predictive analytics and machine learning models are being leveraged to forecast geopolitical tensions, detect misinformation, and assess economic and social indicators that contribute to instability. In diplomacy, AI-powered natural language processing (NLP) and decision-support systems are reshaping multilateral negotiations, treaty drafting, and international c
APA, Harvard, Vancouver, ISO, and other styles
10

Ayyaswamy, Kathirvel, N. Gobalakrishnan, and Naren Kathirvel. "Cyber Security in Industrial Automation Using AI." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-3241-3.ch024.

Full text
Abstract:
As industries adopt digital technologies like the Internet of Things (IoT) and Industrial Control Systems (ICS), the vulnerability of these systems to cyberattacks has increased, making traditional cybersecurity measures insufficient. AI enhances industrial cybersecurity by leveraging machine learning algorithms for anomaly detection, enabling predictive analytics to forecast potential vulnerabilities, and automating incident responses for faster mitigation of threats. Additionally, AI improves malware detection through behavioral analysis, complementing signature-based approaches, and streaml
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Cyberattack prediction"

1

Al-Rahman Hourani, Abd, Eslam Mahmoud, and Saïd Mammar. "Active CyberAttack Detection on Autonomous Vehicle Using Model Predictive Control." In 2024 International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2024. https://doi.org/10.1109/icnsc62968.2024.10760080.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Lee, Hyeonmin, Dongchan Kim, Donggyu Beom, Gyurin Byun, Van-Vi Vo, and Hyunseung Choo. "Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction." In 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2025. https://doi.org/10.1109/imcom64595.2025.10857558.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Berqia, Amine, Habiba Bouijij, Manar Chahbi, and Oussama Ismaili. "Predicting Cyberattacks on Connected Healthcare Devices Using LightGBM-based Approach." In 2025 13th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2025. https://doi.org/10.1109/isdfs65363.2025.11012067.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Perera, Ian, Jena Hwang, Kevin Bayas, Bonnie Dorr, and Yorick Wilks. "Cyberattack Prediction Through Public Text Analysis and Mini-Theories." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622106.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Perez, Carlos Eduardo Barrera, Jairo E. Serrano, and Juan Carlos Martinez-Santos. "Cyberattacks Predictions Workflow using Machine Learning." In 2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2021. http://dx.doi.org/10.1109/icmlant53170.2021.9690527.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Perry, Ian, Lutzu Li, Christopher Sweet, et al. "Differentiating and Predicting Cyberattack Behaviors Using LSTM." In 2018 IEEE Conference on Dependable and Secure Computing (DSC). IEEE, 2018. http://dx.doi.org/10.1109/desec.2018.8625145.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Chin, George, Sutanay Choudhury, John Feo, and Lawrence Holder. "Predicting and detecting emerging cyberattack patterns using StreamWorks." In the 9th Annual Cyber and Information Security Research Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2602087.2602111.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wang, Zhe, and Tingkai Yan. "Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks." In 2023 IEEE 29th International Symposium on Local and Metropolitan Area Networks (LANMAN). IEEE, 2023. http://dx.doi.org/10.1109/lanman58293.2023.10189424.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Durand, Helen, and Matthew Wegener. "Mitigating Cyberattack Impacts Using Lyapunov-Based Economic Model Predictive Control." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147650.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Luzzi, Juan, Ranesh Naha, Arunkumar Arulappan, and Aniket Mahanti. "SoK: A Holistic View of Cyberattacks Prediction with Digital Twins." In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE). IEEE, 2024. http://dx.doi.org/10.1109/ic-etite58242.2024.10493514.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Cyberattack prediction' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography