Security

Data Format: The format of the dataset is CSV format.
- Size of Dataset: The size of the dataset is 3.84 Go with 7,068,103 rows and 18 columns.
- The summary of the attributes:
1) Date: Date of the event, in the format MM/DD/YYYY.
2) Time: Time of day, expressed in 24-hour format HH:MM.
3) Timezone: Time zone specified during processing.
4) MACB: Associated with actions performed on a file in a file system: M for modification, A for access, C for change, B for creation.
5) Source: Short name of the source.
6) Sourcetype: More detailed description of the source.

This dataset contains synthetically generated data representing security profiles of mobile applications, designed for training and evaluating machine learning models for vulnerability detection. It was created as part of the research described in the paper "Machine Learning-Based Vulnerability Detection in Mobile Applications" by Yanguema and Yin.

This dataset, denoted as 𝑾_data, represents a synthetic yet structurally authentic warehouse management dataset comprising 4,132 records and 11 well-defined attributes. It was generated using the Gretel.ai platform, following the structural standards provided by the TI Supply Chain API–Storage Locations specification. The dataset encapsulates essential operational and spatial parameters of warehouses, including unique identifiers, geospatial coordinates, storage capacities, and categorical capacity statuses.

The GestDoor dataset contains wearable sensor data collected to support research in biometric authentication through arm movements during door-opening interactions. Using two 6-degree-of-freedom (6-DOF) inertial measurement units (IMUs) worn on the wrist and upper arm, 11 participants performed four types of door-opening tasks—left-hand pull, left-hand push, right-hand pull, and right-hand push—across up to three sessions. The dataset includes 3,330 samples comprising accelerometer and gyroscope signals at 100 Hz, along with session metadata.

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.