SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT

Electronics

MuhammadTaimoor Khan¹  Danish Javeed²  Tianhan Gao² 

[1] Riphah Institute of Science and Engineering, Islamabad 44000, Pakistan;Software College, Northeastern University, Shenyang 110169, China;

关键词: deep learning;    intrusion detection;    Internet of Things (IoT);    software-defined network (SDN);    distributed denial of service attack (DDoS);   

DOI  :  10.3390/electronics10080918

来源: DOAJ

【 摘 要 】

The Internet of Things (IoT) has proven to be a billion-dollar industry. Despite offering numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for the development of cyber-attacks. The diversity of the IoT, on the one hand, leads to the benefits of the integration of devices into a smart ecosystem, but the heterogeneous nature of the IoT makes it difficult to come up with a single security solution. However, the centralized intelligence and programmability of software-defined networks (SDNs) have made it possible to compose a single and effective security solution to cope with cyber threats and attacks. We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices so that no burden is placed on them. We use a state-of-the-art dataset, CICDDoS 2019, to train our algorithm. The results evaluated by this algorithm achieve high accuracy with a minimal false positive rate (FPR) and testing time. We also perform 10-fold cross-validation, proving our results to be unbiased, and compare our results with current benchmark algorithms.

【 授权许可】

Unknown