Security

Unmanned aerial vehicles (UAVs) are being used for various applications, but the associated cyber risks are also increasing. Machine learning techniques have been successfully adopted to develop intrusion detection systems (IDSs). However, none of the existing works published the cyber or physical datasets that have been used to develop the IDS, which hinders further research in this field.
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Maximum capture length for interface 0: 65000
First timestamp: 1186262976.484933000
Last timestamp: 1186263276.484931000
Unknown encapsulation: 0
IPv4 bytes: 2768247216
IPv4 pkts: 45954067
IPv4 flows: 2860519
Unique IPv4 addresses: 7075
Unique IPv4 source addresses: 7065
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The high profitability of mining cryptocurrencies mining, a computationally intensive activity, forms a fertile ecosystem that is enticing not only legitimate investors but also cyber attackers who invest their illicit computational resources in this area. Cryptojacking refers to the surreptitious exploitation of a victim’s computing resources to mine cryptocurrencies on behalf of the cybercriminal. This malicious behavior is observed in executable files and browser executable codes, including JavaScript and Assembly modules, downloaded from websites to victims’ machines and executed.
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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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The dataset used in the study consists of different IoT network traffic data files each IoT traffic data has files containing benign, i.e. normal network traffic data, and malicious traffic data related to the most common IoT botnet attacks which are known as the Mirai botnet.
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We use industrial cameras to take images of steel wire ropes under different conditions, and use these wire rope images to train the U_Net network, and realize the semantic segmentation of the wire rope images by the U_Net network.
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each application has up to two files. One for memory dataset and another for control flow dataset. Each dataset is composed of JSON objects. Each instruction is a JSON object.
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A dataset of Global Positioning System (GPS) spoofing attacks is presented in this article. This dataset includes data extracted from authentic GPS signals collected from different locationsto emulate a moving and a static autonomous vehicle using a universal software radio peripheral unit configured as a GPS receiver. During the data collection, 13 features are extracted from eight-parallel channels at different receiver stages (i.e., acquisition, tracking, and navigation decoding).
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