article · Journal of Computer Networks and Communications
Combining the Internet of Things with Software Defined Networking creates network architectures that offer improved flexibility, but guaranteeing service availability remains a primary security challenge. Denial of service attacks frequently disrupt these systems. To address this risk, a centralised signature-based intrusion detection system was developed using a Random Forest machine learning classifier. The system was trained and evaluated on the benchmark CICIDS2017 dataset using only twelve selected features from the Wednesday data release. It attained an accuracy of 99.968 per cent, surpassing previous results achieved on the original dataset, alongside a cross-validated accuracy of 99.713 per cent. The resulting model meets the operational requirements of supervised intrusion detection for smart environments and can be deployed across various connected service settings.
Connected devices are increasingly vital to modern infrastructure, yet they are vulnerable to denial of service attacks that take critical services offline. Pairing these devices with software-defined networks helps manage them efficiently, but securing them is crucial. High-accuracy intrusion detection systems help ensure that networks running smart environments stay operational and resistant to disruptive cyber attacks.
This intrusion detection approach is suited for operators of smart environments and software-defined networks seeking to protect connected services from denial of service attacks. Based on the evaluation against standard benchmark datasets, the model is at the applied research and testing stage, demonstrating high classification accuracy with low feature overhead before integration into live network controllers.
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Internet of Things (IoT) can simply be defined as an extension of the current Internet system. It extends the human to human interconnection and intercommunication scenario of the Internet by including things, to bring anytime, anywhere, and anything communication. A discipline in networking evolving in parallel with IoT is Software Defined Networking (SDN). It is an important technology that is aimed to solve the different problems existing in the traditional network systems. It provides a new convenient home to address the different challenges existing in different network-based systems including IoT. One important security challenge prevailing in such SDN-based IoT (SDIoT) systems is guarantying service availability. The ever-increasing denial of service (DoS) attacks are responsible for such service denials. A centralized signature-based intrusion detection system (IDS) is proposed and developed in this work. Random Forest (RF) classifier is used for training the model. A very popular and recent benchmark dataset, CICIDS2017, has been used for training and validating the machine learning (ML) models. An accuracy result of 99.968% has been achieved by using only 12 features on Wednesday’s release of the dataset. This result is higher than the achieved accuracy results of related works considering the original CICIDS2017 dataset. A maximum cross-validated accuracy result of 99.713% has been achieved on the same release of the dataset. These developed models meet the basic requirement of a supervised IDS system developed for smart environments and can effectively be used in different IoT service scenarios.
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DOI: 10.1155/2021/5593214
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