Evaluation of Machine Learning Algorithms for Intrusion Detection System in WSN

Technology has revolutionized into connecting “things” together with the rebirth of global network called Internet Things (IoT). This is achieved through Wireless Sensor Network (WSN) which introduces new security challenges for Information (IT) scientists and researchers. paper addresses issues in WSN by establishing potential automated solutions identifying associated risks. It also evaluates effectiveness various machine learning algorithms on two types datasets, mainly, KDD99 datasets. The aim to analyze protect networks combination Firewalls, Deep Packet Inspection (DPI), Intrusion Prevention Systems (IPS) all specialized overall protection networks. Multiple testing options were investigated such as cross validation percentage split. Based finding, most accurate algorithm least time processing suggested both