Abstract
A Proposed Model for Dimensionality Reduction to Improve the Classification Capability of Intrusion Protection Systems
Highlights
Deep learning using multi-layer perceptron has been used in the third experiment using Waikato Environment For knowledge Analysis (Weka) as a simulation tool
If we used all dataset features without feature selection techniques, we prefer using SVM algorithm which had a higher accuracy than other machine learning algorithms with (83.44%)
Machine and Deep Learning algorithms have been used in this paper to improve the classifications of intrusion detection systems
Summary
Securing the network against all kinds of threats is an essential part of system security management. When the risks are increasingly increasing, safety systems need to be built to make them smarter than ever before Regular security measures such as firewalls and antivirus cannot stop the growing number of complex attacks which take place over a network connection to the Internet. An additional safety layer was introduced as a solution to improve network security by the protection levels using intrusion protection systems (IPS). These can be viewed as additional protection measures focused on a framework of intrusion detection to avoid malicious attacks [1]. One of the main advantages of this method is to contribute to identifying unknown attacks This method can detect data anomalies by detecting attack accurately through this mechanism with low false positives and negative warnings. One of the disadvantages of methods based on the detection of anomalies in the data is that its performance is affected negatively due to regularly changes that
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