Intrusion Detection System with Machine Learning and Multiple Datasets
Haiyan Xuan Mohith Manohar
As Artificial Intelligence (AI) technologies continue to gain traction in the
modern-day world, they ultimately pose an immediate threat to current
cybersecurity systems via exploitative methods. Prompt engineering is a
relatively new field that explores various prompt designs that can hijack large
language models (LLMs). If used by an unethical attacker, it can enable an AI
system to offer malicious insights and code to them. In this paper, an enhanced
intrusion detection system (IDS) that utilizes machine learning (ML) and
hyperparameter tuning is explored, which can improve a model's performance in
terms of accuracy and efficacy. Ultimately, this improved system can be used to
combat the attacks made by unethical hackers. A standard IDS is solely
configured with pre-configured rules and patterns; however, with the
utilization of machine learning, implicit and different patterns can be
generated through the models' hyperparameter settings and parameters. In
addition, the IDS will be equipped with multiple datasets so that the accuracy
of the models improves. We evaluate the performance of multiple ML models and
their respective hyperparameter settings through various metrics to compare
their results to other models and past research work. The results of the
proposed multi-dataset integration method yielded an accuracy score of 99.9%
when equipped with the XGBoost and random forest classifiers and
RandomizedSearchCV hyperparameter technique.