A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

Abstract

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 withouthuman intervention. However, the network may suffer from problems such as the low battery,heterogeneity, and connectivity issues due to the lack of standards. Even though these problemscan cause several performance hiccups, security issues need immediate attention because hackersaccess vital personal and financial information and then misuse it. These security issues can allowhackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial ofService (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secureauthentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of theIoT–blockchain network through automation. However, surveyed work shows that the blockchainand SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms andinitiate DDoS attacks. Thus, developers and security software engineers must be aware of differenttechniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detectDDoS attacks. The novelty of our work is to classify the DDoS detection techniques according toblockchain technology. As a result, researchers can enhance their systems by using blockchain-basedsupport for detecting threats. In addition, we provide general information about the studied systemsand their workings. However, we cannot neglect the recent surveys. To that end, we compare thestate-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoSattacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks alsoimpact other computing systems, such as SCs, networking devices, and power grids. Hence, ourwork briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance,due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financialand utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the contextof associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, andthe underlying technology of IoT systems so that the readers can grasp the detection techniquesand the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN)and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoTNetworks. Thus, we include a detailed discussion of future development encompassing all major IoTsubsystems.