8 dataset results for Network Intrusion Detection

UNSW-NB15 is a network intrusion dataset. It contains nine different attacks, includes DoS, worms, Backdoors, and Fuzzers. The dataset contains raw network packets. The number of records in the training set is 175,341 records and the testing set is 82,332 records from the different types, attack and normal.

110 PAPERS • 2 BENCHMARKS

CICIDS2017

CICIDS2017 (Intrusion Detection Evaluation Dataset (CIC-IDS2017))

Intrusion Detection Evaluation Dataset (CIC-IDS2017) Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions.

13 PAPERS • 1 BENCHMARK

SIDD-Image (Segmented Intrusion Detection Dataset)

This is the first image-based network intrusion detection dataset. This large-scale dataset included network traffic protocol communication-based images from 15 different observation locations of different countries in Asia. This dataset is used to identify two different types of anomalies from benign network traffic. Each image with a size of 48 × 48 contains multi-protocol communications within 128 seconds. The SIDD dataset can be to applied to a broad range of tasks such as machine learning-based network intrusion detection, non-iid federated learning, and so forth.

3 PAPERS • 1 BENCHMARK

CIC IoT Dataset 2022

CIC IoT Dataset 2022 This project aims to generate a state-of-the-art dataset for profiling, behavioural analysis, and vulnerability testing of different IoT devices with different protocols such as IEEE 802.11, Zigbee-based and Z-Wave. The following illustrates the main objectives of the CIC-IoT dataset project:

1 PAPER • NO BENCHMARKS YET

IoT Benign and Attack Traces (IoT Benign and Attack Traces - Data Collected for ACM SOSR 2019)

IOT BENIGN AND ATTACK TRACES

1 PAPER • NO BENCHMARKS YET

UQ NIDS Datasets

UQ NIDS Datasets (Machine Learning-Based NIDS Datasets)

A comprehensive dataset, merging all the aforementioned datasets. The newly published dataset represents the benefits of shared dataset feature sets, where the merging of multiple smaller ones is possible. This will eventually lead to a bigger and more universal NIDS datasets containing flows from multiple network setups and different attack settings. An additional label feature identifying the original dataset of each flow. This can be used to compare the same attack scenarios conducted over two or more different test-bed networks. The attack categories have been modified to combine all parent categories. Attacks named DoS attacks-Hulk, DoS attacks-SlowHTTPTest, DoS attacks-GoldenEye and DoS attacks-Slowloris have been renamed to the parent DoS category. Attacks named DDOS attack-LOIC-UDP, DDOS attack-HOIC and DDoS attacks-LOIC-HTTP have been renamed to DDoS. Attacks named FTP-BruteForce, SSH-Bruteforce, Brute Force -Web and Brute Force -XSS have been combined as a brute-force categor

1 PAPER • NO BENCHMARKS YET

UQ NIDS Datasets (FlowMeter Format)

UQ NIDS Datasets (FlowMeter Format) (Machine Learning-Based NIDS Datasets)

CICFlowMeter format of the datasets are made up of 83 features.

1 PAPER • NO BENCHMARKS YET

UQ NetFlow NIDS v1

UQ NetFlow NIDS v1 (Machine Learning-Based NIDS Datasets)

1 PAPER • NO BENCHMARKS YET