Network Anomaly Detection for NSL-KDD Dataset Using Deep Learning

Deep learning based intrusion detection cyber security methods gained increased popularity. The essential element to provide protection the ICT infrastructure is systems (IDSs). Intelligent solutions are necessary control complexity and increase in new attack types. intelligent system (DL/ML) has been widely used with its benefits effectively deal complex great dimensional data. IDS various types like known, unknown, zero day attacks attractive detected using unsupervised machine techniques. A novel methodology proposed that combines of Isolation forest (One Class) Support Vector Machine (OCSVM) active method detect threats without any prior knowledge. NSL-KDD dataset evaluate DL method. results show this performs better than other design inspires efforts emerging anomaly detection.