No abstract

No abstract

Although the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks. The potential threats to IoT applications and the need to reduce risk have recently become an interesting research topic. It is crucial that effective Intrusion Detection Systems (IDSs) tailored to IoT applications be developed. Such IDSs require an updated and representative IoT dataset for training and evaluation. However, there is a lack of benchmark IoT and IIoT datasets for assessing IDSs-enabled IoT systems. This paper addresses this issue and proposes a new data-driven IoT/IIoT dataset with the ground truth that incorporates a label feature indicating normal and attack classes, as well as a type feature indicating the sub-classes of attacks targeting IoT/IIoT applications for multi-classification problems. The proposed dataset, which is named TON_IoT, includes Telemetry data of IoT/IIoT services, as well as Operating Systems logs and Network traffic of IoT network, collected from a realistic representation of a medium-scale network at the Cyber Range and IoT Labs at the UNSW Canberra (Australia). This paper also describes the proposed dataset of the Telemetry data of IoT/IIoT services and their characteristics. TON_IoT has various advantages that are currently lacking in the state-of-the-art datasets: i) it has various normal and attack events for different IoT/IIoT services, and ii) it includes heterogeneous data sources. We evaluated the performance of several popular Machine Learning (ML) methods and a Deep Learning model in both binary and multi-class classification problems for intrusion detection purposes using the proposed Telemetry dataset.

No abstract

The resource-constrained nature and large-scale adoption of Internet of Things (IoT) have a significant challenge for securing IoT applications. This necessitates a robust and lightweight security architecture and schemes as the existing traditional Internet security architecture and protocols require huge resources and lack of end-to-end security mechanism. In this research, a resource efficient end-to-end security scheme has been proposed by offloading computations and storage of security parameters to fog nodes in the vicinity. In addition, a symmetric-key payload encryption has been used to minimize the overhead of message communication in the resource-contested IoT environment. The analysis shows that the proposed scheme outperforms Transport Layer Security (TLS) in resource usage while it maintains equivalent authenticated end-to-end communication between communicating IoT nodes. The proposed end-to-end security scheme saves more communication bandwidth and incurs less overhead as compared to existing TLS-based security schemes. In particular, the proposed system uses less number of handshakes and achieves a decrease in the number of transmitted messages (approximately 184 bytes as compared to compared TSL message size of 332 bytes) for every handshake. Further, it has been demonstrated through experiments that the proposed security method incurs less overheads as compared to the TLS bandwidth consumption considering a single connection session during message subscription.

The false data injection (FDI) attack cannot be detected by the traditional anomaly detection techniques used in the energy system state estimators. In this paper, we demonstrate how FDI attacks can be constructed blindly, i.e., without system knowledge; including topological connectivity and line reactance information. Our analysis reveal that existing FDI attacks become detectable (consequently unsuccessful) by the state estimator if the data contains grossly corrupted measurements such as device malfunction and communication errors. The proposed sparse optimization based stealthy attacks construction strategy overcomes this limitation by separating the gross errors from the measurement matrix. Extensive theoretical modelling and experimental evaluation show that the proposed technique performs more stealthily (has less relative error) and efficiently (fast enough to maintain time requirement) compared to other methods on IEEE benchmark test systems.

No abstract

No abstract