Security

With the wide adoption, Linux-based IoT devices have emerged as one primary target of today’s cyber attacks. While traditional malware-based attacks (e.g., Mirai) can quickly spread across these devices, they are well-understood threats with defense techniques such as malware fingerprinting coupled with community-based fingerprint sharing. Recently, fileless attacks—attacks that do not rely on malware files—have been increasingly occurring on Linux-based IoT devices.

This 5G dataset that can be used to advance research in attack detection in 5G service based architecture was generated using the open-source Free5GC testbed and UERANSIM, a UE/RAN simulator. The dataset include benign traffic featuring 5G procedures (e.g., registration, deregistration, PDU session establishment/release, uplink, downlink) along with 6 different HTTP/2 attack simulated between different 5G network functions. including:

There are parts of datasets used in paper <ADFLOW: Integrated and Comprehensive Ad Detection Considering Relationships Among Webpage Elements>, including SITE-D/IMG-D/TEXT-D and some PageGraphs extracted from websites in SITE-D.

The IMG-D dataset is large, and we have not yet finished organizing it, so it only includes a portion. Similarly, due to the extensive size of the entire PageGraph dataset used in our experiments, we have only uploaded the PageGraph of a few hundred websites.

This dataset is generated for the purpose of developing and testing attestation techniques for IoT devices. The dataset consists of RAM traces for eight different firmwares including traces for running the legitimate firmware as well as tampered versions of the firmwares. we upload the firmware onto the IoT device and allow it to operate for a predefined time period of 300 seconds. Throughout the device's normal operation, we utilize the gateway node to collect numerous RAM trace samples, each comprising 2048 bytes, with randomized intervals between consecutive samples.