The main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of Internet of Medical Things (IoMT) security solutions. To accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT and Bluetooth).
针对ML-KEM的选择密文攻击数据集
The dataset covers eight types of contract vulnerabilities that QUIVERIF is capable of detecting: 1) transaction order dependency (TOD); 2) timestamp dependency (TD); 3) reentrancy; 4) gasless send; 5) overflow; 6) transferMint [19]; 7) ether strict equality; 8) gas limit DoS
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT-based botnet attacks. In order to mitigate this new threat there is a need to develop new methods for detecting attacks launched from compromised IoT devices and differentiate between hour and millisecond long IoT-based attacks.
Many Intrusion Detection Systems (IDS) has been proposed in the current decade. To evaluate the effectiveness of the IDS Canadian Institute of Cybersecurity presented a state of art dataset named CICIDS2017, consisting of latest threats and features. The dataset draws attention of many researchers as it represents threats which were not addressed by the older datasets. While undertaking an experimental research on CICIDS2017, it has been found that the dataset has few major shortcomings. These issues are sufficient enough to biased the detection engine of any typical IDS.
Recent researches have shown that non-sequential tasks based on deep neural networks (DNN), such as image classification and object detection, are vulnerable to backdoor attacks, leading to incorrect model predictions. As a crucial task in computer vision, Scene Text Recognition (STR) is widely used in IoT fields such as intelligent transportation systems and intelligent surveillance. Therefore, a high degree of security is needed to ensure the accuracy of the system for text recognition. However, there are currently no studies on STR backdoor attacks.
This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.
The dataset includes experiments with 1, 2, and 3 people, covering various positions, walking trajectories, and intermediate locations to evaluate model generalization. Ground truth information is embedded in the folder names, indicating the true distance and angle of the targets.
This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.
The dataset includes experiments with 1, 2, and 3 people, covering various positions, walking trajectories, and intermediate locations to evaluate model generalization. Ground truth information is embedded in the folder names, indicating the true distance and angle of the targets.
This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.
The dataset includes experiments with 1, 2, and 3 people, covering various positions, walking trajectories, and intermediate locations to evaluate model generalization. Ground truth information is embedded in the folder names, indicating the true distance and angle of the targets.
