Security Threat Detection In Telecommunication Network In Compromised IoT Devices By Using Trustworthy Machine Learning

Abstract

Currently, Information Communication Technology (ICT) holds a significant part in the sphere. In IT, Cyber Security carry a massive position. Internet of Things (IoT) indicates to the vast number of tangible bodies which are affixed to the internet, by gathering and switching information with other apparatus and systems with the help of the internet. By using Machine Learning technique, the security threat detection is identified over the telecommunication network in compromised IoT devices. The Driver Anomaly Detection (DAD) Dataset is used for anomaly detection in IoT networks. Message Queue Telemetry Transport protocol (MQTT) is a messaging protocol which is based on Transmission Control Protocol (TCP) and utilized for to create communication between multiple devices. It is required to identify and distinguish the available threats presented in telecommunication network. This thesis gives an understanding about different security threats detection in telecommunication network using Machine Learning technique and explain about security constraints, issues presented. By implementing Security Threat Detection System in an institute, it helps to assists analytical output concerning the imminent threats. Similarly, it aids to guarantee the fame of an association by launching faith among the workers. The above are the benefits obtained by a specific institution by consisting a Threat Detection System. Although there are existing Threat Detection Systems presented in the trade, but they are lacked in some instances like real time. So, in order to resolve all these problems, in this research as a result, ended up with a cost effective and ease of use comprehensive Threat Detection System in a telecommunication network in compromised IoT devices by using trustworthy machine learning