By Sarfraz Brohi, Senior Lecturer Cyber Security
Connected places such as smart cities have enabled urban planners to improve citizens’ quality of life by collecting, storing, processing and analysing data. Internet of Things (IoT) is one of the driving technologies of connected places. It integrates different city functions such as parking systems, mobility services, waste management, healthcare and emergency services. Unfortunately, IoT has vulnerabilities that attackers could exploit due to the massive processing of sensitive data. Cyber security breaches in IoT-powered connected places could violate citizens’ privacy, endanger life and cause economic disaster.
IoT security encompasses a massive area of research with a wide array of open challenges. Dr Sarfraz Brohi (Senior Lecturer in Cyber Security at CSCT-UWE, Bristol) collaborated with the researchers from Taylor’s University, Malaysia (Dr Noor Zaman: Cluster head for cyber security research, Ms Fatima Zahra and Dr Navid Khan) and Taif University, Saudi Arabia (Dr Mehedi Masud and Dr Mohammed A. AlZain) to address crucial IoT-specific rank and wormhole attacks by creating a machine learning model.
The fundamental components of an IoT-enabled infrastructure usually include sensors, RFIDs, microcontrollers and digital devices. These components are low power and lossy due to their small size and simple architecture. Therefore, they use lightweight routing standards and protocols for data transmission. RPL is one such protocol used in IoT networks. RPL-based IoT networks are vulnerable to two types of attacks: WSN-inherited attacks and RPL-specific attacks. Rank and wormhole attacks are examples of high-impact attacks from these categories where attackers target the protocol and sensor network vulnerabilities to disrupt network functionalities and compromise resources.
F. Zahra, NZ. Jhanjhi, SN. Brohi, NA. Khan, M. Masud, and MA. AlZain, generated a dataset and developed a model for detecting RPL-specific and WSN-inherited attacks in RPL-based IoT: LIoTN-RPL dataset and MC-MLGBM model. The LIoTN-RPL data pool consists of network traffic data extracted from various network models. These network models have been designed considering three scenarios – one benign and two attack scenarios – and simulated based on the number of IoT nodes and state of nodes. The MC-MLGBM classifies three target classes and addresses two attacks. In this research, they have used accuracy, precision and recall to evaluate the proposed model. To avoid accuracy bias, they have also used cross entropy, Cohen’s Kappa, and MCC as performance evaluation metrics. The existing models usually focus on one category of attacks. The proposed model provides a conceptual framework for aggregately addressing both in RPL-based IoT networks.
The results of this research are discussed in the paper “Rank and Wormhole Attack Detection Model for RPL-based Internet of Things using Machine Learning”, published in the MDPI Sensors special issue on Advances in IoT Privacy, Security and Applications. Authors have reviewed recent methodologies for addressing security issues in IoT and techniques used to detect the attacks. Furthermore, they have analysed the data collection methods in the research domain. This research observed the scarcity of publicly available RPL attack datasets and the prevalence of self-generated datasets using simulators like Cooja. The future direction of this research focuses on more experiments by designing and simulating other RPL-specific and WSN-inherited attack models. LIoTN-RPL will be released as an open-source dataset to the research community to facilitate the development of ML models for attack detection in RPL-based IoT networks.
