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SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT
| Content Provider | MDPI |
|---|---|
| Author | Javeed, Danish Gao, Tianhan Khan, Muhammad |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 918 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10080918 |
| Journal | Electronics |
| Issue Number | 8 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-04-12 |
| Access Restriction | Open |
| Subject Keyword | Electronics Telecommunications Deep Learning Intrusion Detection Internet of Things (iot) Software-defined Network (sdn) Distributed Denial of Service Attack (ddos) |
| Content Type | Text |
| Resource Type | Article |
