A collection of flow features for detecting LDDoS attacks in IoT environments. The features are derived from the CICIoT2023 dataset using spatial, Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT) methods. The author, organization, and specific data volume are not provided.
Use Cases
- Train machine learning models for LDDoS attack detection based on spatial flow features.
- Benchmark signal processing techniques (FFT/DWT) for feature extraction in network security.
- Analyze IoT network traffic patterns for anomaly detection based on derived flow metrics.
Strengths
- Features are derived from the established CICIoT2023 IoT security dataset.
- Includes multiple signal processing techniques (spatial, FFT, DWT) for feature engineering.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- kaggle
- Collection Method
- Features derived from the CICIoT2023 dataset using spatial, FFT, and DWT processing.