Serial Number: 99
Year: 2021
Kind of Traffic: Simulated
Publicly Available: No
Count of Records: 50GB
Features Count: 83
No. of citations: 45
Attack Type: MITM, DoS, DDoS.etc
Download Links: Not Available
Abstract: Liu and team demonstrated that existing intrusion detection technologies face challenges, such as their limited applicability in scalable and dynamic environments such as contemporary IoT, there exists a trade-off between the limitations in resources of IoT devices and the increasing volume of data traffic. To tackle these challenges, they introduced the CCDINID-V1 dataset and utilized it, alongside two additional datasets. The dataset CCD-INID-V1 has a real network traffic of smart labs and smart homes, which was produced in experimental setup. The experimental configuration involves four Raspberry Pis equipped with sensors, functioning as devices for IoT sensing. Temperature readings are gathered by the IoT devices in smart home Scenario, whereas in the smart lab scenario both temperature and pressure measurements are gathered by IoT devices. Through a Wi-Fi IP connection, using HTTPS the data recorded was sent to a cloud server. Authors mimicked five attacks, comprising UDP Flood, ARP Poisoning, Hydra Bruteforce, ARP DoS, with Asterisk protocol, and SlowLoris. There are categorized as either normal or malicious in dataset entries. Additionally, NFStream extracted 83 features, including various timestamps, packet and byte counts, MAC addresses, and destination and source IPs. Although interested individuals can acquire it by reaching out to the authors directly, as dataset is not accessible publicly.