@@ -287,7 +287,7 @@ Borchani (2020) proposed an advanced approach to malicious beaconing detection u
Enright et al. (2022) introduced a learning-based zero-trust architecture for 6G and future networks \cite{enright2022learning}. The paper explores the integration of machine learning with zero-trust security models to address the evolving security challenges in next-generation networks, particularly 6G. The authors propose a framework that combines learning-based techniques with zero-trust principles to enhance the detection of malicious activity and improve overall network security. The study contributes to the development of more adaptive and robust security architectures for future networks, offering a promising solution to the emerging threats in 6G environments.
Van Ede et al. (2022) introduced Deepcase, a semi-supervised contextual analysis method for security events \cite{van2022deepcase}. The paper presents a novel approach that combines semi-supervised learning techniques with contextual analysis to enhance the detection of security events. By leveraging contextual information, Deepcase can identify complex patterns and relationships in security data, improving the accuracy of event classification and anomaly detection. The authors demonstrate the effectiveness of their approach in real-world security environments, showing its potential to enhance the detection and response capabilities of security systems in large-scale networks.
Van Ede et al. (2022) introduced Deepcase, a semi-supervised contextual analysis method for security events \cite{van2022deepcase}. The paper presents a novel approach that combines semi-supervised learning techniques with contextual analysis to enhance the detection of security events. By leveraging contextual information, Deepcase can identify complex patterns and relationships in security data, improving the accuracy of event anomaly detection. The authors demonstrate the effectiveness of their approach in real-world security environments, showing its potential to enhance the detection and response capabilities of security systems in large-scale networks.
Ongun et al. (2021) introduced PORTFILER, a port-level network profiling approach for detecting self-propagating malware \cite{ongun2021portfiler}. The paper presents a novel method that profiles network traffic at the port level to identify self-propagating malware, which often uses specific ports for communication and propagation. PORTFILER analyzes network behavior to detect irregularities and patterns associated with malware activity. By focusing on port-level communication, the approach improves malware detection, providing more accurate identification of self-propagating threats in real-time. The authors demonstrate the effectiveness of their method through experiments, showing its potential to enhance network security against rapidly spreading malware.
