Anomaly Detection System Through Numerical Simulations

Abstract

The essence of machine learning is to compile the data we observe with the experience that the
program learns to generate the information that we can make use of. For example, the process of
differentiating valid emails from spam emails. The input will be some documents or words which are
included in the emails and the output should be yes or no that indicating the email is spam or not-spam
respectively, but we do not have an algorithm to accurately identify the spam emails. In this regard we
calculate the errors as absolute relative approximation error during this interpolation process.
By contrasting the model with current IDS techniques, we will assess its efficacy using metrics like
accuracy, detection rate, and computational efficiency. Lastly, to make sure the system is both efficient
and comprehensible, we will employ attention mechanisms to interpret the model's decisions and offer
insights into the detection process. A study comparing different supervised machine learning classifiers for
intrusion detection was carried out by. Despite conventional defenses like firewalls and antivirus software,
the internet's and computer networks' explosive growth has raised security concerns.
The pre-trained Transformer model algorithm is used to identify anomalies after the data points have been
clustered using the K-Means algorithm. The goal of this research is to enhance anomaly detection in
Network Intrusion Detection Systems by integrating the advantages of both algorithms.