Engineering topics

Dawson, Joel

Publications and source records attributed to Dawson, Joel.

3 records

DOE OSTI2024

Detection of Control Injection Attacks using Energy Data Anomalies in CNC Machining

The widespread adoption of networked devices, sophisticated automation, and data-driven processes in the industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. With these benefits, however, comes a substantial increase in the attack surface of these systems. In addition to affecting the readiness and the quality of critical products, the attacks against manufacturing processes and systems carry the potential to have severe physical consequences, including human injury and death. In this paper we present the results of a remote network-based control injection attack on a CNC mill. Specifically, we focus on the impact of this type of the attack on the movement of CNC mill during operation. Evaluating the physical effect of these attacks on a workpiece, we provide machine agnostic, affordable, and scalable solution for their monitoring. We then demonstrate a simple threshold-based method for the detection of these attacks and evaluate the effectiveness of detection.

Taylor, Curtis↗

DOE OSTI2022

Stealthy Cyber Anomaly Detection On Large Noisy Multi-material 3D Printer Datasets Using Probabilistic Models

As Additive Layer Manufacturing (ALM) becomes pervasive in industry, its applications in safety critical component manufacturing are being explored and adopted. However, ALM's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of ALM devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is very challenging. The ideal algorithm for this context is simple, computationally efficient, minimizes false positives, and is accurate enough to resolve small deviations. In this paper, we present a probabilistic-model-based approach to address this challenge. To test our approach, we analyze current measurements from a polymer composite 3D printer during emulated tampering attacks. Our results show that our approach can consistently and efficiently locate small changes in the presence of substantial operational noise.

Yoginath, Srikanth↗

DOE OSTI2022

Heartbeat: Detecting Malware by Periodic Power Signal Injection and Monitoring

Rootkits and other stealthy malware attempt to conceal their presence on a computer by making changes to the host computer’s operating environment. ORNL’s Heartbeat technology detects these changes, and thus the malware itself. Heartbeat operates by directly monitoring the DC power consumption of the computer while a set of operations, the “heartbeat,” is executed periodically. These operations exercise parts of the operating system that are common targets of malware tampering. The power consumption during these heartbeat events is monitored and then compared to a previously learned baseline, with any significant deviation detected and analyzed. This technology has been tested and validated in a laboratory environment, and ORNL is currently seeking a deployment partner to allow for further in-context development and testing of this technology.

97 MATHEMATICS AND COMPUTING↗