Security
This project builds a length-versatile and noise-robust LoRa radio frequency fingerprint identification (RFFI) system. The LoRa signals are collected from 10 commercial-off-the-shelf LoRa devices, with the spreading factor (SF) set to 7, 8, 9, respectively. The packet preamble part and device labels are provided.
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This paper presents a real-time reconfigurable Cyber-Power Grid Operation Testbed (CPGrid-OT) with multi-vendor, industry-grade hardware, and software.
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A comparative, empirical study of state-of-the-art contrastive and generative graph learning models applied to source and binary software fragments drawn from the National Vulnerability Database (NVD) reveals that Graph Masked Auto-Encoders show exceptional promise for detecting security vulnerabilities, outperforming all other baseline models in the study.
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The data is a truth table of a 22-variable 4-resilient Boolean function with nonlinearity 2095616.
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The dataset is available in GitHub and the link is given below:
https://github.com/sahilarora3117/capstone-19BCE1366-2023
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This is a data set for Radio Frequency fingerprinting, which is a kind of identification of wireless devices based on their intrinsic physical features. The data set is composed by GSM bursts collected from 12 GSM mobile phones while transmitting. The samples have been collected using a Software Defined Radio with a sample rate at 20 MS/s. The content information has been removed from the bursts to remove the risk of bias due to content. The data set is in MATLAB format.
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Future IPv6-based networks face a significant security challenge with ICMPv6 communications. An attacker uses ICMPV6 messages to saturate the target system and aims to implement a Denial of Service (DoS) or Distributed Denial of Service (DDoS) attack. A robust intrusion detection system is being developed by researchers to address these issues. Researchers have access to a restricted number of IPv6 datasets to construct a well-known intrusion detection system, however these datasets are not accessible to the public and only target on one kind of attack.
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The deployment of unmanned aerial vehicles (UAV) for logistics and other civil purposes is consistently disrupting airspace security. Consequently, there is a scarcity of robust datasets for the development of real-time systems that can checkmate the incessant deployment of UAVs in carrying out criminal or terrorist activities. VisioDECT is a robust vision-based drone dataset for classifying, detecting, and countering unauthorized drone deployment using visual and electro-optical infra-red detection technologies.
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