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3 results for “botnet”
DCNDS Project Dataset - P2P Botnet Detection Using Enhanced Peer Hunter
<p>This dataset represents the Enhanced Peer Hunter implementation that was deployed on two NREN within the AsiaConnect project in Malaysia and Indonesia. Based on a 30 days measurement from Saturday, June 19, 2021 4:00:00 PM GMT - Monday, July 19, 2021 3:59:55 PM GMT, the list of detections and all flows associated with the detected machines are listed in the dataset (2 files per country). The dataset would allow any future p2p botnet detection mechanisms to leverage this (flow) data to train and evaluate their detection mechanisms.</p> <p> Take note that all IP addresses have been anonymized using a hash function with salt to avoid leakage of information.</p> <p> </p>
Topological Botnet Detection
<p>Large-scale topological botnet detection datasets for graph machine learning and network security, containing 4 different types of synthetic botnet topologies and 2 different real botnet topologies overlaid onto large real-world background traffic communication networks.</p> <p>Each dataset contains a specific botnet topology, with 960 graphs in total, randomly split to train/val/test sets. There are labels on both nodes and edges indicating whether they were in the botnet (evil) community. Learning tasks could target at predicting on nodes to detect whether they are botnet nodes, or recovering the whole botnet community by also predicting on edges as whether they belong to the original botnet.</p> <p>There are no unique features on nodes or edges, as the detection is purely based on topological properties within the graph (topological discovery).</p> <p> </p>
Botnet Data - Computer Networks Lab 2022/2
<p>Dataset created for the Computer Network Laboratory class at UFES.</p> <p>Reproduction in a different experimental setup of the botnet attack presented in:</p> <p>"An empirical comparison of botnet detection methods" Sebastian Garcia, Martin Grill, Jan Stiborek and Alejandro Zunino. Computers and Security Journal, Elsevier. 2014. Vol 45, pp 100-123. http://dx.doi.org/10.1016/j.cose.2014.05.011</p> <p>More specifically, the scenario https://mcfp.felk.cvut.cz/publicDatasets/CTU-Malware-Capture-Botnet-48/</p> <p>For more information, https://github.com/VitorSpa/LabRedes-2023</p> <p> </p>
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