A Comparative Study: Between Non Autoencoders IDS and Autoencoder Based IDS Approaches in Network Communication

Intrusion detection is an integrated security issue in the present digital environment. Malicious cyber-attackers can frequently linger in tremendous volumes of regular data in demented network traffic. It has a superior destruct of concealing and opacity in cyberspace, making it challenging for Network Intrusion Detection Systems (NIDS) to aver catching accuracy and timing. The false-positive issue is one of the underlying drawbacks of network intrusion detection systems (NIDS), which are widely engaged to discover threats and safeguard the networks. Imbalance classes and unwarranted material data a reminiscence failure skyway to make false positive, which are inferior in company in the preparation dataset. Feature engineering is also performed in this approach using the Recursive Property Excreting method. In this experiment NSL-KDD Dataset is used. The outcome shows that our approach is finer than different state-of-art approaches in terms of different metrics like Accuracy, Precision, Recall, and F1 Score.