IoT Telemetry Dataset and UNSW-NB15 Dataset

IoT Telemetry Intrusion Detection Dataset: This dataset is a synthetically labelled IoT telemetry dataset derived from the original IoT telemetry data (iot_telemetry_data.csv). It contains 405,184 records and extends the original telemetry attributes with synthetic device identifiers, timestamps, binary attack labels, and attack categories for intrusion detection research. The dataset contains IoT sensor attributes including CO, humidity, light, LPG, motion, smoke, and temperature, together with device information. Based on the observed device and attack-type distributions in the original data, synthetic device_id, label, and attack_type attributes were generated to support controlled IDS experiments. The dataset contains 202,592 Normal records and 202,592 Attack records. The attack records are distributed across five categories: Data Injection, DoS, Malware, Replay, and Spoofing, resulting in six classes when Normal traffic is included. The synthetic dataset is intended for academic research, machine learning, deep learning, GAN-based intrusion detection, binary and multiclass classification, and reproducibility studies, and is also meant to assess the model's generalisability. It should be considered a synthetic, labelled extension of the original IoT telemetry dataset, not a replacement for the original data. The dataset can be used for developing and evaluating IoT intrusion detection models, including machine learning, deep learning, ensemble learning, and generative adversarial network (GAN)-based approaches. It supports binary and multiclass classification and evaluation using metrics such as accuracy, precision, recall, F1-score, false-positive rate, false-negative rate, and confusion matrices. The dataset is provided in CSV format and is intended for academic and research purposes. UNSW-NB15: The UNSW-NB15 dataset is a network intrusion detection dataset developed for evaluating machine learning and deep learning-based cybersecurity models. It contains normal network traffic as well as multiple categories of malicious activities, making it suitable for both binary and multiclass intrusion detection research. The dataset includes 49 features representing network flow, packet, protocol, and connection characteristics, along with the corresponding attack labels. The attack categories include Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms. UNSW-NB15 can be used to develop and evaluate Intrusion Detection Systems (IDS), particularly for network attack classification, anomaly detection, feature analysis, and machine learning model comparison. Common evaluation measures include accuracy, precision, recall, F1-score, false-positive rate, false-negative rate, and confusion matrices. The dataset is widely used in cybersecurity research and provides a challenging benchmark containing diverse modern network attack behaviours. It is provided in CSV format for research and experimental use.