• Amaresh Kumar
• Dr. Ashish Kumar Khare

Intrusion detection is one of the important security problems in today’s cyber world. A significant number of techniques have been developed which are based on machine learning approaches. So for identifying the intrusion we have designed the machine learning algorithms. By using the algorithm we find out intrusion and we can identify the attacker’s details also. IDS are mainly two types: Host based and Network based. A Network based Intrusion Detection System (NIDS) is usually placed at network points such as a gateway and routers to check for intrusions in the network traffic. This paper presents the C4.5 decision tree algorithm for classification. The C4.5 algorithm is used in Data Mining as a Decision Tree Classifier which can be employed to generate a decision, based on a certain sample of data. The simulation results shows that the proposed approach gives the significant good results in term of the precision, recall, F1-Score, Error Rate and accuracy. The overall achieved accuracy is 96.3% or approx 97% with the 3% error rate.

NIDS, C4.5, Decision Tree, accuracy, IoT, IDS, Cyber, Attack, Security

"Decision Tree C4.5 Machine Learning Approach based Network Intrusion Detection for IoT Security Application", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.d479-d483, January-2022, Available :http://www.jetir.org/papers/JETIR2201361.pdf

"Decision Tree C4.5 Machine Learning Approach based Network Intrusion Detection for IoT Security Application", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppd479-d483, January-2022, Available at : http://www.jetir.org/papers/JETIR2201361.pdf