Network Traffic Anomaly: Processed Intrusion Detection Data from CSE-CIC-IDS2018

A processed and curated dataset for network traffic anomaly detection, derived from the CSE-CIC-IDS2018 intrusion detection dataset. It is designed for machine learning and deep learning research on network security and intrusion detection. The dataset was uploaded by author 'abmallick' and was last updated on December 15, 2025.

Use Cases

• Training machine learning models for network intrusion detection based on processed traffic features.
• Benchmarking deep learning algorithms for anomaly detection in network security.
• Conducting research on the efficacy of different ML approaches using a curated version of a known benchmark dataset.

Strengths

• Derived from the CSE-CIC-IDS2018 dataset, a known benchmark for intrusion detection research.
• Described as 'processed and curated' to be easier to use for ML research.
• Last updated on 2025-12-15, suggesting recent maintenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Derived from the CSE-CIC-IDS2018 intrusion detection dataset.

Collection Method

Processed and restructured from the original source data.

Freshness

Last updated 2025-12-15 08:40:12.