Serial Number: 111
Year: 2023
Kind of Traffic: Simulated + Real
Publicly Available: Yes
Count of Records: 17000 frames
Features Count:
No. of citations: 9
Attack Type: climbing, tapping, etc
Download Links: Not Available
Abstract: The authors meticulously curated a dataset to evaluate machine learning methodologies for detecting intrusions through the analysis of network traffic captures in perimeter intrusion detection systems (PIDS). Sourced from strategically placed security cameras capturing instances of intrusions in authentic attack scenarios, this dataset was used to validate an innovative machine learning-driven approach, aiming to enhance both the effectiveness and efficiency of PIDS for heightened security measures. It includes variations in zoom, roll, and yaw to replicate real-world conditions. Over a continuous 15-day recording period, videos covered day, night, rainy day, and night scenarios, with the camera consistently set to autofocus mode. The authors manually examined the entire 10 days' worth of footage, selecting a subset of 30 hours containing instances of intrusion. From this curated set, 17,000 frames were meticulously chosen for further analysis. The authors have openly made this dataset accessible to facilitate future research efforts in the field.