62 research outputs found
Jatkuvaa yhdistettyä oppimista verkkopoikkeamien havaitsemiseen 5G Open-RANissa
No full textThis dissertation offers a unique federated continual learning setup for anomaly detection in the fast growing 5G Open Radio Access Network (O-RAN) environment. Conventional AI techniques frequently fall short of meeting the security automation needs of 5G networks, owing to their outstanding latency, dependability, and bandwidth demands. As a result, the thesis provides an anomaly detection system that does not only use federated learning (FL) to solve inherent privacy problems and resource constraints but also incorporates replay buffer concept in the training phase of the model to eradicate catastrophic forgetting. To allow the intended federated learning architecture, anomaly detectors are incorporated into the Near-real time RIC, while aggregation servers are installed within the Non-real time RIC. The configuration was carefully tested using the 5G NIDD Dataset, revealing a considerable boost in detection accuracy by reaching close to 99% for almost all datasets after including the continual learning process. The thesis also investigates the notion of transfer learning, in which pre-trained local models are evaluated against a hybrid Application layer DDoS dataset that includes benign samples from the CICIDS 2017 dataset and attack flows generated in proprietary SDN environment. The captured results show almost over 99% of accuracy, confirming the suggested system’s efficacy and flexibility. The study represents a significant step forward in the development of a more secure, efficient, and privacy-protecting 5G network architecture
Cybercrime Profiling: Decision-Tree Induction, Examining Perceptions of Internet Risk and Cybercrime Victimisation
No full textCMTS: Consensus-based Multi-hop Time Synchronization protocol in wireless sensor networks
No full text
A Novel Buffered Federated Learning Framework for Privacy-Driven Anomaly Detection in IIoT
No full textIndustrial Internet of Things (IIoT) is highly sensitive to data privacy and cybersecurity threats. Federated Learning (FL) has emerged as a solution for preserving privacy, enabling private data to remain on local IIoT clients while cooperatively training models to detect network anomalies. However, both synchronous and asynchronous FL architectures exhibit limitations, particularly when dealing with clients with varying speeds due to data heterogeneity and resource constraints. Synchronous architecture suffers from straggler effects, while asynchronous methods encounter communication bottlenecks. Additionally, FL models are prone to adversarial inference attacks aimed at disclosing private training data. To address these challenges, we propose a Buffered FL (BFL) framework empowered by homomorphic encryption for anomaly detection in heterogeneous IIoT environments. BFL utilizes a novel weighted average time approach to mitigate both straggler effects and communication bottlenecks, ensuring fairness between clients with varying processing speeds through collaboration with a buffer-based server. The performance results, derived from two datasets, show the superiority of BFL compared to state-of-the-art FL methods, demonstrating improved accuracy and convergence speed while enhancing privacy preservation
ZSM security:threat surface and best practices
No full textAbstract
The ETSI’s Zero touch network and Service Management (ZSM) framework is a prominent initiative to tame the envisioned complexity in operating and managing 5G and beyond networks. To this end, the ZSM framework promotes the shift toward full Automation of Network and Service Management and Operation (ANSMO) by leveraging the flexibility of SDN/NFV technologies along with Artificial Intelligence, combined with the portability and reusability of model-driven, open interfaces. Besides its benefits, each leveraged enabler will bring its own security threats, which should be carefully tackled to make the ANSMO vision a reality. This paper introduces the ZSM’s potential attack surface and recommends possible mitigation measures along with some research directions to safeguard ZSM system security
AI-driven zero touch network and service management in 5G and beyond:challenges and research directions
No full textAbstract
The foreseen complexity in operating and managing 5G and beyond networks has propelled the trend toward closed-loop automation of network and service management operations. To this end, the ETSI Zero-touch network and Service Management (ZSM) framework is envisaged as a next-generation management system that aims to have all operational processes and tasks executed automatically, ideally with 100 percent automation. Artificial Intelligence (AI) is envisioned as a key enabler of self-managing capabilities, resulting in lower operational costs, accelerated time-tovalue and reduced risk of human error. Nevertheless, the growing enthusiasm for leveraging AI in a ZSM system should not overlook the potential limitations and risks of using AI techniques. The current paper aims to introduce the ZSM concept and point out the AI-based limitations and risks that need to be addressed in order to make ZSM a reality
- …
