Anomaly Detection Model for Predicting Hard Disk Drive Failures

Đurašević, Slađana; Pesovic, Uros; Djordjevic, Borislav

doi:10.1080/08839514.2021.1922840

Cited by 9 publications

(1 citation statement)

References 14 publications

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“…Besides applications in CPS, anomaly detection techniques have been widely devised within other research areas as well. Specifically, in the information technology sector, Djurasevic et al [ 27 ] presented a failure detection method for hard disc drives that utilizes a specific set of monitoring and diagnostic parameters to train a support vector machine classifier. A recursive feature elimination approach is further employed to select the most important features from the overall set that contribute the most to the detection of the anomalies.…”

Section: Related Workmentioning

confidence: 99%

A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

Koutroulis

Mutlu

Kern

2022

Sensors

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Critical infrastructure, such as water treatment facilities, largely relies on the effective functioning of industrial control systems (ICSs). Due to the wide adoption of high-speed network and digital infrastructure technologies, these systems are now highly interconnected not only to corporate networks but also to the public Internet, mostly for remote control and monitoring purposes. Sophisticated cyber-attacks may take advantage the increased interconnectedness or other security gaps of an ICS and infiltrate the system with devastating consequences to the economy, national security, and even human life. Due to the paramount importance of detecting and isolating these attacks, we propose an unsupervised anomaly detection approach that employs causal inference to construct a robust anomaly score in two phases. First, minimal domain knowledge via causal models helps identify critical interdependencies in the system, while univariate models contribute to individually learn the normal behavior of the system’s components. In the final phase, we employ the extreme studentized deviate (ESD) on the computed score to detect attacks and to exclude any irrelevant sensor signals. Our approach is validated on the widely used Secure Water Treatment (SWaT) benchmark, and it exhibits the highest F1 score with zero false alarms, which is extremely important for real-world deployment.

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Section: Related Workmentioning

confidence: 99%