Security

This research utilizes real-world malware samples that are reinforced with the latest VM-based packers and digitally signed to ensure runtime execution. For academic research purposes only, these packed malware samples are provided as running instances to facilitate behavioral, forensic and detection analysis. Users are forewarned on the potential risks of executing unknown malicious programs, and should refrain from installing or propagating these files outside of a controlled experimental environment.

In our Systematic Literature Review (SLR), we systematically conducted searches in various repositories, utilizing a search string, based on titles and abstracts of publications. Following the retrieval of results, we meticulously eliminated duplicate publications, ensuring that only one instance with identical title, abstract, author names, and year of publication was retained. Subsequently, we excluded books and thesis reports from our dataset. A comprehensive manual review of the titles and abstracts of the remaining articles was then undertaken to further refine our dataset.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. The receiver hardware impairments interfere with the feature extraction of transmitter impairments, but their effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system by employing adversarial training to learn the receiver-independent features.

This dataset results from a month-long cloud-based Internet Background Radiation observation conducted in May 2023.
A sensor fleet comprised of 26 EC2 compute instances was deployed within Amazon Web Services across their 26 commercially available regions, 1 sensor per region.

The dataset contains 21,856,713 incoming packets, out of which 17,008,753 are TCP datagrams, 3,076,855 are ICMP packets and the remainder, 1,770,418 are UDP messages.