Machine Learning in Network Security Using KNIME Analytics
This work applies existing methods to a standard cybersecurity dataset, which is incremental for researchers or practitioners in network security.
The paper tested various machine learning algorithms on the NSL-KDD dataset using KNIME analytics to analyze intrusion detection systems in cybersecurity, but no specific results or numbers were reported.
Machine learning has more and more effect on our every day's life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.