Intrusion Detection System
This dataset consists of .csv files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack and Decreased Rank Attack) targeting the RPL protocol and these files are taken from Cooja (Contiki network simulator). It gives researchers the opportunity to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks is an important step towards developing and testing protection mechanisms against such attacks by mimicking real-world attack scenarios.
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885 Views
Slow-rate DDoS attacks are recent threats targeting next-generation networks such as IoT, 5G, etc. Unlike conventional high-rate DDoS, slow-rate DDoS have not been deeply studied, mainly due to the limited number of existing datasets with real traces.
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703 Views
For academic purposes, we are happy to release our datasets. This dataset is in support of my research paper 'TOW-IDS: Intrusion Detection System based on Three Overlapped Wavelets in Automotive Ethernet'. If you want to use our dataset for your experiment, please cite our paper.
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1792 Views
Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks.
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2631 Views
This dataset supports researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To that aim, data have been acquired from a water distribution hardware-in-the-loop testbed which emulates water passage between nine tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed by a real partition which is virtually connected to a simulated one.
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2356 Views
Datasets as described in the research paper "Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT Applications".
There are two main dataset provided here, firstly is the data relating to the initial training of the machine learning module for both normal and malicious traffic, these are in binary visulisation format, compresed into the document traffic-dataset.zip.
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4501 Views
Training, Test, and Validation data pertaining to the real-time packet data captured in Sonic Firewall is attached herewith.
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164 Views
Intrusion Detection System can be build for private cloud using OpenNebula. OpenNebula is a cloud computing platform for managing heterogenous distributed data center infrastructure. The database is generated using a private cloud setup using KVM and OpenNebula. OpenNebula provides API to monitor Virtual Machines (VMs) running on the infrastructure. Total 6 VMs were deployed on the infrastructure. The monitoring data was collected over 63 Hours. Attacks were simulated on few of the VMs for variable time duration.
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739 Views