Security
Abstract
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This project aims to generate smart home IoT datasets (especially Zigbee traffic data) in order to support research on smart home IoT network and device profiling, behaviour modelling, characterization, and security analysis. The Zigbee traffic data is captured in a real house with two Zigbee networks containing over 25 Zigbee devices which monitor the daily activities inside the house. The captured Ethernet traffic data from Home Assistant also contains the status data of several non-Zigbee IoT devices such as printers, a smart thermostat, and entertainment devices.
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Smart grid, an application of Internet of Things (IoT) is modern power grid that encompasses power and communication network from generation to utilization. Home Area Network (HAN), Field or Neighborhood Area Network (FAN/NAN) and Wide Area network (NAN) using Wireless LAN and Wireless/Wired WAN protocols are employed from generation to utilization . Advanced Metering Infrastructure, a utilization side infrastructure facilitates communication between smart meters and the server where energy efficient protocols are mandate to support smart grid .
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A dataset related to a cable under anomalies conditions while a transmitter (AWG) sends a binary PAM signal to a receiver. The signals are acquired by an oscilloscope. The anomalies were manually forced on the cable under test: air-exposed, water-exposed conductors, and tapping. In the dataset, the signals are also available for normal cable.
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This dataset is generated for the purpose of developing and testing attestation techniques for IoT devices. The dataset consists of RAM traces for eight different firmwares including traces for running the legitimate firmware as well as tampered versions of the firmwares. we upload the firmware onto the IoT device and allow it to operate for a predefined time period of 300 seconds. Throughout the device's normal operation, we utilize the gateway node to collect numerous RAM trace samples, each comprising 2048 bytes, with randomized intervals between consecutive samples.
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Numerous studies have focused on exploring Android malware in recent years, covering areas such as malware detection and application analysis. As a result, there is a pressing need for a reliable and scalable malware dataset to support the development and evaluation of effective malware studies. Although several benchmarks for Android malware datasets are widely used in research, they have significant limitations. Firstly, many of these datasets are outdated and do not capture current malware trends. Additionally, some have become obsolete or inaccessible, limiting their usefulness.
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The optical chaos communication (OCC) can provide physical layer security for high-speed data transmission. In these OCC systems, the time delay signature (TDS) serves as a crucial encryption key. We propose a method based on reservoir computing (RC) network for TDS extraction of OCC systems. The mapping relationship between the system output time series and its delay variants is learned by the RC network. Then, the convergence performance of the RC network is measured and used for TDS extraction. The effectiveness is verified by extracting TDS of the two main types of optical time-delay fe
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The optical chaos communication (OCC) can provide physical layer security for high-speed data transmission. In these OCC systems, the time delay signature (TDS) serves as a crucial encryption key. We propose a method based on reservoir computing (RC) network for TDS extraction of OCC systems. The mapping relationship between the system output time series and its delay variants is learned by the RC network. Then, the convergence performance of the RC network is measured and used for TDS extraction. The effectiveness is verified by extracting TDS of the two main types of optical time-delay fe
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The optical chaos communication (OCC) can provide physical layer security for high-speed data transmission. In these OCC systems, the time delay signature (TDS) serves as a crucial encryption key. We propose a method based on reservoir computing (RC) network for TDS extraction of OCC systems. The mapping relationship between the system output time series and its delay variants is learned by the RC network. Then, the convergence performance of the RC network is measured and used for TDS extraction. The effectiveness is verified by extracting TDS of the two main types of optical time-delay fe
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29 Views