Intrusion Detection System with Machine Learning and Multiple Datasets
This work addresses cybersecurity threats from unethical hackers using AI, but it is incremental as it builds on existing ML methods for intrusion detection.
The paper tackles the problem of improving intrusion detection systems to combat AI-driven cybersecurity threats by proposing an enhanced system using machine learning and multiple datasets, achieving an accuracy of 99.9% with XGBoost and random forest classifiers.
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.