Abstract
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.
Highlights
With the growth of the Internet and the interconnectedness between each networking device, there is a dire need for security
1 2 3 4 5 6 7 8 9 10 positive, true negative, false positive, Matthews correlation coefficient (MCC) and false negative values were calculated with a confusion matrix
The software-defined networks (SDNs) paradigm is a promising solution to the securing of Internet of Things (IoT) infrastructure
Summary
With the growth of the Internet and the interconnectedness between each networking device, there is a dire need for security. It is a paradigm that links millions of smart devices, leading to the creation of a smart environment, such as smart health systems, smart cities, smart factories, intelligent vehicular networks and smart ecosystems [1] This means that the lack of security can pose a serious risk to the devices and the entire system. The traditional intrusion detection schemes are deployed, working at the infrastructure level, with firewalls or with intrusion detection and prevention systems to protect devices from attacks These security measures are not sufficient when it comes to the seamless nature of IoT devices. The Internet of Things is defined as an environment in which physical devices are integrated into the network in such a way that these objects become active participants of a business process These objects can vary from network devices to sensors to household and health care items. Scanning the IoT devices in real-time for threat detection may result in a very unaffordable overhead
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