False Data Injection Attack Dataset for Industrial Internet of Things
- Citation Author(s):
-
AKM Ahasan Habib
(Universiti Kebangsaan Malaysia (UKM))
Mohammad Kamrul Hasan (Universiti Kebangsaan Malaysia (UKM)) - Submitted by:
- AKM Ahasan Habib
- Last updated:
- DOI:
- 10.21227/k89d-5s27
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Abstract
Training and testing the accuracy of machine learning or deep learning based on cybersecurity applications requires gathering and analyzing various sources of data including the Internet of Things (IoT), especially Industrial IoT (IIoT). Minimizing high-dimensional spaces and choosing significant features and assessments from various data sources remain significant challenges in the investigation of those data sources. The research study introduces an innovative IIoT system dataset called UKMNCT_IIoT_FDIA, that gathered network, operating system, and telemetry data. The datasets' initial statistical analysis shows that they can be used to assess cybersecurity applications like threat intelligence, intrusion detection, adversarial machine learning, deep learning, and privacy-preserving models.
Instructions:
False Data Injection Attack Dataset for Industrial Internet of Things
This dataset contains one .csv file. The dataset has 26 features that are “http_response_body_len”, “dst_port”, “dns_rcode”, “dns_qclass”, ”dns_qtype”, ”src_port”, “http_resp_mime_types”, ”http_request_body_len”, “conn_state”, “http_user_agent”, ”ssl_issuer”, ”ssl_subject”, ”http_orig_mime_types”, ”http_trans_depth”, “http_method”, “http_status_code”, ”http_version”, ”http_uri”, ”ssl_cipher”, ”ssl_version”, “ssl_resumed”, “ssl_established”, ”proto”, ”dns_rejected”, ”dns_RA”, ”dns_RD”, “dns_AA”, “service”, ”dns_query”, ”dst_ip_bytes”.
During the training process dataset head is present like Fig.1.
The total number of data scenarios is presented in Fig. 2.
To validate cybersecurity applications especially false data injection (FDI) attack, this study introduces IIoT datasets and their analysis.
Using this dataset, the manuscript was submitted to a different journal.