Research Area: Internet of Things
The Internet-of-Things (IoT) network is growing big owing to its utility in smart applications. An IoT network is susceptible to security breaches, in majority due to the resource-constrained nature of IoT. Of the various breaches, the Distributed Denial-of-Service (DDoS) attack can snip off the network service to the users in various ways, such as consumption of servers resources, saturating link bandwidth, etc. These types of DDoS breaches can turn out to be a catastrophe in critical IoT use cases. This article delves into tackling the DDoS attack triggered by malicious wireless IoT on IoT servers. Our security scheme leverages the cloud and software-defined network (SDN) paradigm to mitigate the DDoS attack on IoT servers. We have proposed a novel mechanism named learning-driven detection mitigation (LEDEM) that detects DDoS using a semisupervised machine-learning algorithm and mitigates DDoS. We tested LEDEM in the testbed and emulated topology, and compared the results with state-of-the-art solutions. We achieved an improved accuracy rate of 96.28% in detecting DDoS attack.
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Author(s) Name: Nagarathna Ravi; S. Mercy Shalinie
Journal name: IEEE Internet of Things Journal
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Publisher name: IEEE
DOI: 10.1109/JIOT.2020.2973176
Volume Information: Volume: 7, Issue: 4, April 2020, Page(s): 3559 - 3570
