  An Intrusion detection system (IDS) is essential for avoiding malicious
activity. Mostly, IDS will be improved by machine learning approaches, but the
model efficiency is degrading because of more headers (or features) present in
the packet (each record). The proposed model extracts practical features using
Non-negative matrix factorization and chi-square analysis. The more number of
features increases the exponential time and risk of overfitting the model.
Using both techniques, the proposed model makes a hierarchical approach that
will reduce the features quadratic error and noise. The proposed model is
implemented on three publicly available datasets, which gives significant
improvement. According to recent research, the proposed model has improved
performance by 4.66% and 0.39% with respective NSL-KDD and CICD 2017.
