Security

This unlabeled dataset reflects the network activity of a real branch office with 29 active machines connected to the same broadcast domain for four hours. To achieve this, a Network Intrusion Detection System (NIDS) called BCAST IDS listened to network traffic every 10 seconds. During this time, various types of activities were carried out (browsing, emailing, file transfers, etc.) on each machine to ensure the dataset reflected a wide range of benign behavior.

Our dataset is constructed by leveraging existing malware samples and utilizing both UTRDCL and traditional DCL techniques to load the malicious components, thereby launching attacks. In addition to the malware samples themselves, we also provide online detection reports from reputable sources, including VirusTotal, MobSF, and Bazaar (Pithus). These reports offer a comprehensive analysis of the malware samples, enabling researchers to gain a deeper understanding of the attacks and their characteristics.

The softwarization and virtualization of the fifth-generation (5G) cellular networks bring about increased flexibility and faster deployment of new services. However, these advancements also introduce new vulnerabilities and unprecedented attack surfaces. The cloud-native nature of 5G networks mandates detecting and protecting against threats and intrusions in the cloud systems.

Common Randomness (CR) can be considered as a resource in our future communication systems that will assist in various operations, such as cryptographic encryption in wireless communication, improving identification capacity for identification codes. In wireless communication, CR can be conveniently generated by reading the reciprocal channel properties between two wireless terminals, and by sending pilot signals to each other using the time division duplexing (TDD)-based half-duplexing method. In the channel probing stage, reciprocal channel characteristics are measured.