The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate every day tasks, they also introduce tremendous security flaws. Current insufficient security measures employed to defend smart devices make IoT the ‘weakest’ link to breaking into a secure infrastructure, and therefore an attractive target to attackers. This paper proposes a three layer Intrusion Detection System (IDS) that uses a supervised approach to detect a range of popular network based cyber-attacks on IoT networks. The system consists of three main functions: 1) classify the type and profile the normal behaviour of each IoT device connected to the network, 2) identifies malicious packets on the network when an attack is occurring, and 3) classifies the type of the attack that has been deployed. The system is evaluated within a smart home testbed consisting of 8 popular commercially available devices. The effectiveness of the proposed IDS architecture is evaluated by deploying 12 attacks from 4 main network based attack categories such as: Denial of Service (DoS), Man-In-The-Middle (MITM)/Spoofing, Reconnaissance, and Replay. Additionally, the system is also evaluated against 4 scenarios of multi-stage attacks with complex chains of events. The performance of the system’s three core functions result in an F-measure of: 1) 96.2%, 2) 90.0%, and 3) 98.0%. This demonstrates that the proposed architecture can automatically distinguish between IoT devices on the network, whether network activity is malicious or benign, and detect which attack was deployed on which device connected to the network successfully.

There has been significant interest within the offshore oil and gas industry to utilise Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS) . There has also been a corresponding increase in cyberattacks targeted at oil and gas companies. Offshore oil production requires remote access to and control of large and complex hardware resources. This is achieved by integrating ICPS, Supervisory, Control and Data Acquisition (SCADA) systems, and IIoT technologies. A successful cyberattack against an oil and gas (O&G) offshore asset could have a major impact on the environment, marine ecosystem and safety of personnel. Any disruption to the world’s supply of O&G can also have an effect on oil prices and the global economy. We describe the cyberattack surface within the oil and gas industry, discussing emerging trends in the offshore sub-sector and provide a historical perspective of known cyberattacks. We also present a case study of a subsea control system architecture typically used in offshore O&G operations and highlight potential vulnerabilities affecting the components of the system. This study is the first to provide a detailed analysis of attack vectors in a subsea control system. The analysis provided can be used to understand key vulnerabilities in such systems and may be used to implement efficient mitigation methods.

This article presents three-tiered intrusion detection systems, which uses a supervised approach to detect cyber-attacks in industrial control systems networks. The proposed approach does not only aim to identify malicious packets on the network but also attempts to identify the general and finer grain attack type occurring on the network. This is key in the industrial control systems environment as the ability to identify exact attack types will lead to an increased response rate to the incident and the defence of the infrastructure. More specifically, the proposed system consists of three stages that aim to classify: (i) whether packets are malicious; (ii) the general attack type of malicious packets (e.g. Denial of Service); and (iii) finer-grained cyber-attacks (e.g. bad cyclic redundancy check, attack). The effectiveness of the proposed intrusion detection systems is evaluated on network data collected from a real industrial gas pipeline system. In addition, an insight is provided as to which features are most relevant in detecting such malicious behaviour. The performance of the system results in an F-measure of: (i) 87.4%, (ii) 74.5% and (iii) 41.2%, for each of the layers, respectively. This demonstrates that the proposed architecture can successfully distinguish whether network activity is malicious and detect which general attack was deployed.