Machine Learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis

The security of computer networks is
increasingly difficult to maintain due to the rising complexity
and frequency of cyber-attacks. Important tools for finding
and neutralizing these dangers are "intrusion detection"
systems. This study sets out to do a thorough examination and
comparison of the efficacy of several “machine learning
algorithms” for use in “intrusion detection”.We evaluate the
efficacy of several "machine learning algorithms" in correctly
categorizing instances of network traffic as normal or invasive
via extensive experiments performed on representative
datasets. Algorithms like random forests, decision trees, SVMs,
DL models and NNs are all being tested and rated.
Effectiveness is measured and compared using a variety of
performance indicators including “accuracy, recall, precision,
false positive rate, and F1-score”.The results of this study
emphasize the potential of deep learning models and Random
Forests for use in "intrusion detection" and add to the body of knowledge around machine learning methods for this task.
Professionals in the field of network security might use the
results to their advantage when building "intrusion detection" systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable "intrusion detection" systems.