![]() The dataset includes 200K benign and 200K malware samples totalling to 400K android apps with 14 prominent malware categories and 191 eminent malware families. This research work proposes a new comprehensive and huge android malware dataset, named CCCS-CIC-AndMal-2020. Mohammadreza MontazeriShatoori, Logan Davidson, Gurdip Kaur and Arash Habibi Lashkari, "Detection of DoH Tunnels using Time-series Classification of Encrypted Traffic", The 5th Cyber Science and Technology Congress (2020) (CyberSciTech 2020), Vancouver, Canada, August 2020įor more information and download this dataset, visit this page. The full research paper outlining the details of the dataset and its underlying principles: We developed a traffic analyzer namely DoHLyzer to extrac features from the captured traffic. The browsers and tools used to capture traffic include Google Chrome, Mozilla Firefox, dns2tcp, DNSCat2, and Iodine while the servers used to respond to DoH requests are AdGuard, Cloudflare, Google DNS, and Quad9. Layer 1 of the proposed two-layered approach is used to classify DoH traffic from non-DoH traffic and layer 2 is used to characterize Benign-Doh from Malicious-DoH traffic. The final dataset includes implementing DoH protocol within an application using five different browsers and tools and four servers to capture Benign-DoH, Malicious-DoH and non-DoH traffic. The main objective of this project is to deploy DoH within an application and capture benign as well as malicious DoH traffic as a two-layered approach to detect and characterize DoH traffic using time-series classifier. ![]() This research work proposes a systematic approach to generate a typical dataset to analyze, test, and evaluate DoH traffic in covert channels and tunnels.
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