yue123161/Paper_TNSM

Code for paper: Contrastive Learning Enhanced Intrusion Detection

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Experimental

This project offers an enhanced method for detecting network intrusions. It takes raw network packet sequences or traffic data from datasets like NSL-KDD and UNSW-NB15, and outputs more accurate classifications of network activities as either benign or malicious. Network security analysts and engineers can use this to improve their systems' ability to identify subtle threats.

No commits in the last 6 months.

Use this if you are a network security professional looking to improve the accuracy and detection rate of your intrusion detection systems, especially in scenarios where traditional methods struggle with ambiguous or diverse network traffic.

Not ideal if you are looking for a pre-built, out-of-the-box solution that doesn't require Python programming or fine-tuning of machine learning parameters.

network-security intrusion-detection cybersecurity-analytics threat-detection network-monitoring

No License Stale 6m No Package No Dependents

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