Biblio

One of the most important issues in the development of the Internet of Things (IoT) is network security. The deep packet inspection (DPI) is a promising technology that helps to detection and protection against network attacks. The DPI software system for IoT is developed in this paper. The system for monitoring and analyzing IoT traffic to detect anomalies and identify attacks based on Hurst parameter is proposed. This system makes it possible to determine the Hurst flow parameter at different intervals of observation. This system can be installed on a network provider to use more effectively the bandwidth.

The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.