A Novel Technique for Automized ML-Enabled IDS
Gurleen Kaur, Ankesh Gupta, Hemant Kumar Saini · 2023
In today's era with the emerging trends of Big Data and IoT the network traffic range of services derived for the users according to their needs. Mostly public users use the open channels for the transmission of the data which would be a lot о concern over its security. To sustain such security various researches developed many defensive approaches but those are no longer effective. Intrusion detection system (IDS) deployed to detect the various intrusion assaults but they are not up to mark. This paper explores the various classes of intrusions and methodologies to mitigate them. The overview gives the useful resource for naïve researchers, make them better learning of the emerging intrusions and invoke the potential measure involving the Machine learning techniques for future investigation. In particular various potential risks and rewards of intrusive activities are highlighted which will persuade researchers to implement the proactive approaches to address such challenges. Also trying to propose IDS where the detection paradigms has been improved by ensemble learner and advanced hyperprameter optimization which lessen the false alarms and identify accurately.