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Malware can spread rapidly across Internet of Things (IoT) environments. Responding to this threat is challenging because IoT devices are numerous, heterogeneous, and often constrained in their processing power, memory, and energy. Read more
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Large Language Models (LLMs) are increasingly being used to write, complete, and debug software. These tools can improve developer productivity, but an important question remains: is the generated code secure enough to be used in real applications? Read more
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Industrial Control Systems (ICS) operate essential services such as water treatment, energy generation, manufacturing, and transportation. Detecting cyberattacks against these systems is therefore extremely important. However, selecting a suitable anomaly detection method is not straightforward, particularly when the system must detect previously unseen attacks. Read more
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Smart grids increasingly use artificial intelligence to predict system stability and support decisions about electricity distribution. However, an attacker may manipulate the measurements used by these models, causing an apparently accurate prediction system to produce unsafe results. Read more
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Advanced Persistent Threats, commonly known as APTs, can remain hidden inside a compromised organisation for extended periods. Once established, an attacker may gradually transfer sensitive information while attempting to make the malicious traffic appear normal. Read more
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Malware propagation presents a continuing threat to Internet of Things (IoT) systems. Defence is particularly challenging when attackers and defenders have different or incomplete perceptions of the system and cannot accurately observe each other’s intentions. Read more
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Malware infections in Internet of Things (IoT) systems can lead to both device compromise and the leakage of sensitive information. Protecting privacy in these environments is challenging because defenders often have incomplete information about which devices are infected and how the malware is behaving. Read more
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Malware propagation across Internet of Things (IoT) devices can lead to data leakage, financial loss, service disruption, and the compromise of wider cyber-physical environments. Defending these systems is difficult because malware and defensive actions continually alter the security state of the network. Read more
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Internet of Things (IoT) devices generate, store, and exchange large volumes of potentially sensitive information. This information may relate to personal behaviour, healthcare, transportation, industrial operations, energy consumption, or the activities taking place within homes and workplaces. Read more
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Internet of Things (IoT) environments may contain large numbers of interconnected and resource-constrained devices. Once malware compromises part of such an environment, it can propagate between devices and cause data leakage, service disruption, and wider system compromise. Read more
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Our AGRITRUST research has been featured by Electronics Weekly in an article titled Arduino-based Squirrel Box brings soil metrics to the IoT. Read more
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Smart agriculture increasingly relies on Internet of Things (IoT) sensors to monitor soil, weather, crops, and environmental conditions. The resulting data can support decisions about irrigation, fertilisation, crop management, and sustainable use of natural resources. Read more
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Internet of Things (IoT) deployments often contain battery-powered and resource-constrained devices. Information about a device’s remaining energy may influence routing, task allocation, network membership, and other operational decisions. Read more
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Two of our research papers on industrial control system security and sensor attack detection were cited in written evidence submitted to a UK Parliament inquiry into smart and connected technologies. Read more
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Cyber-physical systems combine computing, communication, sensing, and control with physical processes. They are used in water infrastructure, power systems, transportation, manufacturing, medical devices, and robotic platforms. Read more
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Evaluating the security of an Industrial Control System requires more than testing a small collection of previously observed cyberattacks. Read more
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Programmable Logic Controllers, commonly known as PLCs, are central to modern industrial control systems. They receive measurements from sensors, execute control logic, and issue commands to actuators controlling physical processes. Read more
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This blog post is our recent work that explains the challenges we have faced in adopting Machine Learning Algorithms for intrusion detection in Industrial Control Systems (ICS). This study is insightful with a summary of several projects both using examples from design and data-centric techniques. Each study is concluding with a lesson learned and some recommendations are made for future research. Read more
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This is a test blog post. This is unfinished page and will be updated in future with some interesting content related to security and privacy. Read more
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Noise in sensor measurements is generally treated as an unwanted disturbance. Engineers often use filters and estimation techniques to reduce its effect and obtain a clearer representation of the underlying physical process. Read more
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Sensor measurements in cyber-physical systems contain noise produced by sensing hardware and the underlying physical process. This noise is normally viewed as an obstacle to accurate measurement and control. Read more
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Water distribution is a critical public service. Modern distribution networks use sensors, communication systems, programmable controllers, pumps, and valves to monitor and control the movement of water across large geographical areas. Read more
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Smart water distribution systems use sensors, controllers, pumps, valves, and communication networks to deliver water efficiently and reliably. Read more
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Industrial control systems use sensors and state estimators to understand the condition of a physical process. If a measurement differs significantly from the estimated state, a failure detector may raise an alarm. Read more