Applications of Machine Learning in Cyber Security: A Review DOI Creative Commons
Ioannis Vourganas, Anna Lito Michala

Journal of Cybersecurity and Privacy, Journal Year: 2024, Volume and Issue: 4(4), P. 972 - 992

Published: Nov. 17, 2024

In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have been gaining ground in Cyber Security (CS) research an attempt to counter increasingly sophisticated attacks. However, this paper poses the question of qualitative quantitative data. This argues that scholarly domain is severely impacted by quality quantity available Datasets are disparate. There no uniformity (i) dataset features, (ii) methods collection, or (iii) preprocessing requirements enable good-quality analyzed data suitable for automated decision-making. review contributes existing literature providing a single summary wider field relation AI, evaluating most datasets, combining considerations ethical posing list open questions guide future endeavors. Thus, valuable insights cyber security field, fostering advancements application AI/ML.

Language: Английский

Generative AI Empowered Network Digital Twins: Architecture, Technologies, and Applications DOI Open Access
Tong Li, Qingyue Long, Haoye Chai

et al.

ACM Computing Surveys, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

The rapid advancement of mobile networks highlights the limitations traditional network planning and optimization methods, particularly in modeling, evaluation, application. Network Digital Twins, which simulate digital domain for offer a solution to these challenges. This concept is further enhanced by generative AI technology, promises more efficient accurate AI-driven data generation simulation optimization. survey provides insights into AI-empowered twins. We begin outlining architecture twin, encompasses both physical domains. involves four key steps: processing monitoring, replication simulation, designing training optimizers, Sim2Real control. Next, we systematically discuss related studies each step make detailed taxonomy problem studied, methods used, designs leveraged. Each examined with focus on role AI, from estimating missing simulating behaviors control strategies bridging gap between Finally, open issues challenges AI-based

Language: Английский

Citations

10
IDEA-6G: Revolutionizing 6G Networks with Integrated Digital Twin and Self-Healing Mechanisms DOI
Anil Audumbar Pise,

Yogesh Khandokar

JOURNAL OF HIGH-FREQUENCY COMMUNICATION TECHNOLOGIES, Journal Year: 2025, Volume and Issue: 03(01), P. 258 - 270

Published: Jan. 7, 2025

6G network is an innovative concept of connectivity, which offers unparalleled speeds, ultralow latency, and extensive device connectivity that surpass the capabilities current 5G networks. However, challenges such as congestion security threats pose significant hurdles to ensuring reliable stable performance. A novel Integrated Digital twin self-healing mechanisms for networks (IDEA-6G) approach has been proposed addressing these performance network. The method leverages Twin (DT) sub-layer bridge physical digital worlds, enabling real-time synchronization monitoring assets. Meticulous feature extraction using Term Frequency - Inverse Document (TF-IDF) techniques Generative Adversarial Networks Long Short-Term Memory (GAN-LSTM) model have helped in enhancement efficient detection cyber-attacks within virtual models. Additionally, Deep Neural (DNNs) facilitate informed decision-making effective actions response identified threats. effectiveness IDEA-6G compared with existing B5GEMINI, DTFV, DITEN techniques. Results technique indicate superior prediction accuracy, rates, load balancing, service delay reduction. 17.55%, 27.54%, 7.38% higher than respectively.

Language: Английский

Citations

0
Intelligent human activity recognition for healthcare digital twin DOI
Elif Bozkaya, Tolga Önel, Levent Erişkin

et al.

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101497 - 101497

Published: Jan. 1, 2025

Language: Английский

Citations

0
Applications of Machine Learning in Cyber Security: A Review DOI Creative Commons
Ioannis Vourganas, Anna Lito Michala

Journal of Cybersecurity and Privacy, Journal Year: 2024, Volume and Issue: 4(4), P. 972 - 992

Published: Nov. 17, 2024

In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have been gaining ground in Cyber Security (CS) research an attempt to counter increasingly sophisticated attacks. However, this paper poses the question of qualitative quantitative data. This argues that scholarly domain is severely impacted by quality quantity available Datasets are disparate. There no uniformity (i) dataset features, (ii) methods collection, or (iii) preprocessing requirements enable good-quality analyzed data suitable for automated decision-making. review contributes existing literature providing a single summary wider field relation AI, evaluating most datasets, combining considerations ethical posing list open questions guide future endeavors. Thus, valuable insights cyber security field, fostering advancements application AI/ML.

Language: Английский

Citations

2