Intrusion detection in WSN using Supervised Machine Learning Techniques
Keywords:
Wireless Sensor Network, Intrusion Detection, Machine Learning, Naïve Bayes, WEKA, IBK, One RAbstract
Wireless sensor network (WSN) is becoming increasingly one of the trendiest research areas in Computer Science applications. It finds wide applications department of Defence, banking, hospital, marketing, education, and all prioritized government sectors. Applications that have created many problems especially in security levels and hindrance caused due to the intrusion in WSN based communication. In proposed system depending upon the security and dependability of this article builds model on IoT is established using machine learning algorithms. This intrusion detection system is very compatible and characteristics of determining the interactions in any dataset have given an exemplary classification, performance level and receiver of operator characteristics. This paper uses specialized data set of WSN to detect and classify different class attributes like black hole flooding and scheduling attacks. This paper considers the use of novel Framework that is trained using a dataset to detect and classify different attacks. Output results of the model show that WSN has improved ability for the intrusion detection system using higher classification and accuracy rate of 99.45% for IBk classifier using Weka tool. The precision rate for the built model is 97% and the area under the curve is also gave an optimum result ranging from 0.77 to 0.985 for Naïve Bayes multinominal and IBk classifier. An optimum model is built using the Weka tool which is trained using the dataset different types of attacks using some selected classifiers. The attacks like black hole, flooding, scheduling, and grey hole were predicted in WSN.
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