  A significant increase in the number of interconnected devices and data
communication through wireless networks has given rise to various threats,
risks and security concerns. Internet of Things (IoT) applications is deployed
in almost every field of daily life, including sensitive environments. The edge
computing paradigm has complemented IoT applications by moving the
computational processing near the data sources. Among various security models,
Machine Learning (ML) based intrusion detection is the most conceivable defense
mechanism to combat the anomalous behavior in edge-enabled IoT networks. The ML
algorithms are used to classify the network traffic into normal and malicious
attacks. Intrusion detection is one of the challenging issues in the area of
network security. The research community has proposed many intrusion detection
systems. However, the challenges involved in selecting suitable algorithm(s) to
provide security in edge-enabled IoT networks exist. In this paper, a
comparative analysis of conventional machine learning classification algorithms
has been performed to categorize the network traffic on NSL-KDD dataset using
Jupyter on Pycharm tool. It can be observed that Multi-Layer Perception (MLP)
has dependencies between input and output and relies more on network
configuration for intrusion detection. Therefore, MLP can be more appropriate
for edge-based IoT networks with a better training time of 1.2 seconds and
testing accuracy of 79%.
