期刊
ELECTRONICS
卷 10, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/electronics10080918
关键词
deep learning; intrusion detection; Internet of Things (IoT); software-defined network (SDN); distributed denial of service attack (DDoS)
资金
- China Fundamental Research Funds for the Central 321 Universities [N2017003, N182808003]
The Internet of Things (IoT) is a billion-dollar industry, but its prevalence makes it vulnerable to cyber attacks. Software-defined networks provide a single effective security solution to combat these threats, and algorithms can efficiently detect cyber threats and attacks while ensuring no additional burden on resource-constrained IoT devices.
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.
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