Context Aware Security for Industrial Control Systems
Research Programme

Context Aware Security for Industrial Control Systems

(2020-2023)
Anomaly Detection Bluetooth ZigBee Deep Learning Sensor Fusion
Internet of Things (IoT) Infrastructure / Systems (IS) Human Computer Interaction (HCI) Security (S)

Project Overview

Industrial Cyber-Physical Systems (ICPSs) manage critical manufacturing processes where digital controls and physical behaviour are tightly coupled. Sophisticated adversaries exploit this connection by manipulating sensor readings while controlling connected devices, evading traditional network traffic analysis. CASPER (Context Aware Security for cyber Physical Edge Resources) addresses this vulnerability by deploying a secondary, low-cost IoT sensor network that operates on separate communication protocols such as Bluetooth and ZigBee, creating an air-gapped layer of protection independent from primary control channels. The project pursues four main objectives: reviewing current ICPSs from cybersecurity perspectives, developing a reconfigurable IoT sensing infrastructure for analytics deployment, augmenting cyberattack detection through physical and behavioural monitoring, and evaluating the effectiveness of a context-aware, dynamically adaptive IoT edge network for protecting manufacturing environments.

Sensors positioned around industrial robotic arms observe temperature, vibration, light, and sound to detect anomalies. Edge analytics and state-of-the-art deep learning correlate these physical signatures with expected behaviour, enabling operators to receive early warnings before faults escalate, even when attackers spoof primary telemetry. This physical-layer monitoring complements traditional network defences by providing an independent source of ground truth.

The work has produced the CASPER platform, validated through experimental deployments. The project has contributed open datasets and toolkits for the broader research community, supporting reproducible experimentation in cyber-physical anomaly detection for industrial environments.

Team

Partners

Repositories

Outcomes

Journal

Real-Time Anomaly Detection for Industrial Robotic Arms Using Edge Computing

Hakan Kayan, Ryan Heartfield, Omer Rana, Pete Burnap, and Charith Perera,

IEEE Internet of Things Journal (IOTJ), Volume 12, Issue 15, pp 29696-29712. August 2025

Journal

CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic Arms

Hakan Kayan, Ryan Heartfield, Omer Rana, Pete Burnap, and Charith Perera,

ACM Transactions on Internet of Things (TIOT), Volume 5, Issue 3, Article No: 18 (August 2024), pp, 1 - 36

Journal

Exploiting User-Centred Design to Secure Industrial Control Systems

Matthew Nunes, Hakan Kayan, Pete Burnap, Charith Perera, Jason Dykes,

Frontiers in the Internet of Things, Volume 3, 2024, pp. 1-18

Conference

Artifact: CASPER: Context-Aware Anomaly Detection System for Industrial Robotic Arms

Hakan Kayan, Omer Rana, Pete Burnap, and Charith Perera,

In Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp 3-4, 2023

Conference

CASPER: Context-Aware Anomaly Detection System for Industrial Robotic Arms

Hakan Kayan, Omer Rana, Pete Burnap, and Charith Perera,

In Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp 282-284, 2023

Journal

Cybersecurity of Industrial Cyber-Physical Systems: A Review

Hakan Kayan, Matthew Nunes, Omer Rana, Pete Burnap, Charith Perera,

ACM Computing Surveys (CSUR), Volume 54, Issue 11s, January 2022, Article No.: 229, pp 1–35 (35)

Journal

AnoML-IoT: An end to end re-configurable multi-protocol anomaly detection pipeline for Internet of Things

Hakan Kayan, Yasar Majib, Wael Alsafery, Mahmoud Barhamgi, Charith Perera,

Elsevier Internet of Things, Volume 16, 100437, December 2021 (19)