A Distributed Mitigation Strategy against DoS attacks in Edge Computing

Internet of Things (IoT) is a platform where every day devices become smarter, every day processing becomes intelligent, and every day communication becomes informative. Numerous challenges prevent to secure IoT devices and their end-to-end communication in an IoT environment. In fact, the IoT security is still an open challenge. The purpose of this work is to examine a distributed strategy for mitigating Denial of Service (DoS) attacks against the fog node in an edge computing context in which the nodes exchange messages through Message Queue Telemetry Transport (MQTT) protocol. The proposed strategy is based on a dynamic message sending frequency of the lightweight nodes. It is also mitigated data tampering and eavesdropping by using Elliptic Curve Cryptography (ECC)

Advanced detection Denial of Service attack in the Internet of Things network based on MQTT protocol using fuzzy logic

Message Queuing Telemetry Transport (MQTT) is one of the popular protocols used on the Internet of Things (IoT) networks because of its lightweight nature. With the increasing number of devices connected to the internet, the number of cybercrimes on IoT networks will increase. One of the most popular attacks is the Denial of Service (DoS) attack. Standard security on MQTT uses SSL/TLS, but SSL/TLS is computationally wasteful for low-powered devices. The use of fuzzy logic algorithms with the Intrusion Detection System (IDS) scheme is suitable for detecting DoS because of its simple nature. This paper uses a fuzzy logic algorithm embedded in a node to detect DoS in the MQTT protocol with feature selection nodes. This paper's contribution is that the nodes feature selection used will monitor SUBSCRIBE and SUBACK traffic and provide this information to fuzzy input nodes to detect DoS attacks. Fuzzy performance evaluation is measured against changes in the number of nodes and attack intervals. The results obtained are that the more the number of nodes and the higher the traffic intensity, the fuzzy performance will decrease, and vice versa. However, the number of nodes and traffic intensity will affect fuzzy performance