An efficient intrusion detection system using unsupervised learning AutoEncoder

Key Points

• The aim is to develop an efficient intrusion detection system using unsupervised learning techniques to enhance security.
• Utilized an autoencoder model for anomaly detection in network traffic.
• Applied unsupervised learning to identify potential security breaches without labeled data.
• Analyzed performance metrics to evaluate detection accuracy and efficiency.
• Achieved improved detection rates compared to traditional methods.
• Demonstrated the capability to identify unknown threats effectively.
• Reduced false positive rates in intrusion alerts.