An Intelligent Approach to Improving the Performance of Threat Detection in IoT (KEY IJP************664)

• V. Lakshmi Chaitanya,Shaik Afrin,Bekkem Nikhila,Kummara Sreevani,Nakka Manisha,Donthu Lahari

Abstract

Internet of Things (IoT) systems are useful in our everyday lives and have grown in importance. A complete IoT system contains all of the devices, sensors, networks, software, and other components required for operation and interconnection. However, these gadgets and sensors often have minimal resource needs and many security flaws from manufacturers. Furthermore, the edge network locations of IoT devices have various security flaws. As a result, illegal sensor hijacking or denial-of-service attacks on edge network locations might have significant consequences for the system's functionality. In this work, we propose a model that combines machine learning algorithms and principal component analysis techniques to train and predict Distributed Denial of Service (DDoS) attacks. Principal component analysis techniques were applied to reduce data dimensionality. We used accuracy, precision, recall, and F1-Score as the evaluation metrics. We explain the True Positive, False Positive, True Negative, and False Negative measures as basic parts of the above evaluation metrics. Unlike previous studies, we used the Training Time to evaluate the training time of each model. We employed two datasets, CICIDS 2017 and CSE-CIC-IDS 2018, to evaluate our proposed model. In general, the proposed models exhibited the best performance and improved training time.