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.

It is expected to be pervasive sensors to integrate physical and digital world. Low-latency and wide-covering networks are hence urged, which lead to costly infrastructure. We are proposing ARCH based on smart contract and Self-sovereign Identity (SSI), to provide telecoms a trusted and transparent multilateral platform sharing their infrastructure to decrease the deployment and agency cost, and carbon emission.

Phishing has persistently posed threats to the World Wide Web as phishing websites evolve over these years. Many previous works were devoted to extracting useful features and focused on the essential components of phishing websites. One of the essential components is sensitive inputs which require sensitive information. Yet, due to a large variety of web designs, detecting the existence of sensitive inputs is not trivial. Some previous works have provided rule-based approaches to detect login forms, which contain sensitive inputs, using HTML codes. However, the novel phishing websites modify HTML codes against the detection rules, which causes less accurate detection.To overcome the limitation of previous works, we proposed SenseInput using hybrid deep learning models to detect the existence of sensitive inputs and sensitive information because phishing websites eventually present sensitive inputs in their visual content. SenseInput achieved 96.94% f1-score for sensitive input detection on our dataset and 96.73% f1-score on a public dataset, Phishpedia Phish30K. Next, we used 22 features involving the proposed seven statistical features and two sensitive input features for phishing detection. The experiment shows that our approach achieves 98.48% and 95.87% f1-score on our validation and Phishpedia datasets, outperforming previous approaches. Finally, we investigated the influence of sensitive input features. The result shows that our sensitive input features are more effective than the rule-based login form. Besides, the experiment also indicates that proposed sensitive input features can reduce the impact of bias between different datasets.