5 G networks, essential for applications like smart cities and factories, must ensure high reliability, availability, and security. However, the safety of 5 G remains a matter of concern. Despite its enhanced security mechanisms compared to previous generations, our study identifies two novel denial-of-service (DoS) signaling attack types that can disrupt the 5 G control plane (CP), exposing weaknesses in its current security design. Traditional switches cannot prevent such attacks because they are unable to extract attacking user equipment (UE) information from encapsulated nonaccess stratum (NAS) data. While ensuring the availability of the 5 G CP is essential to protect 5 G services, this issue has emerged as a pressing challenge. To address this challenge, we propose a novel P4-based user equipment fingerprinting (P4USE) method to mitigate 5 G CP DoS signaling attacks. Leveraging P4's programmable packet processing capability, P4USE can parse encapsulated NAS information at line rate, identify UE fingerprints, and drop anomalous NAS messages originating from attackers. Experimental evaluations in Open5GS confirm both the existence of the identified vulnerabilities and the effectiveness of P4USE in mitigating them. Overall, our work reveals two novel vulnerabilities and presents a defense solution to enhance the availability and security of the 5 G CP.

Sports video annotation, an active research area in the field of multimedia content understanding, is an essential process in applications, such as summarization, highlight extraction, event detection, and retrieval. This paper considers the issue in relation to the annotation of baseball videos. Conventional baseball video annotation frameworks are based primarily on video content analysis, such as scoreboard recognition and machine learning techniques, which require a substantial amount of human input to collect and organize training data. The performance of such frameworks might become unstable if they encounter audiovisual patterns not included in the training data. To address the issue, we propose a novel framework for baseball video annotation that aligns high-level webcast text with low-level video content. Several cues, which are derived from the video content and webcast text, are utilized for alignment by leveraging hierarchical agglomerative clustering and genetic algorithm optimization. In addition, we develop an unsupervised method to learn the pitch segment properties of baseball videos by Markov random walk, and thereby reduce the need for human intervention substantially. Our experiments demonstrate that the proposed framework yields a robust result against a variety of video content and enhances the automaticity in baseball video annotation.