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Volume 7 Issue 4

April 2021

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Intrusion Detection System with Machine Learning Approach: A Survey


Anish Fathima B


IDS, Data Mining, Analysis, classification


In recent decades, information networks' accelerated growth has culminated in a slew of protection concerns, including computer and network intrusions. Intrusion Detection Systems (IDS) are a form of intrusion detection system that IDSs provide tools for identifying and discriminating between disruptive and non-intrusive network packets. Most modern intrusion detection technologies depend heavily on human observers to distinguish between disruptive and non-intrusive network traffic by analyzing server logs or network traffic. Human presence in the identification mechanism has become a non-trivial concern as network traffic data has grown. The system's capacity to operate autonomously over rapidly increasing data in the network is limited by IDS' ability to perform, dependent on human expertise. On the other hand, soft-computing methods will effectively model human expertise and their capacity to interpret the device. Autonomous packet detections are possible thanks to intrusion detection strategies focused on machine learning and soft computing. They can analyze data packets on their own. These methods are heavily focused on mathematical data processing. The algorithms that deal with these datasets will make judgments based on previous data trends to deal with new emerging data patterns in network traffic. This paper provides a thorough survey of numerous soft-computing and machine learning strategies to develop autonomous IDSs.

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Published in: Volume : 7, Issue : 4
Publication Date: 4/1/2021

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