Sensor Data Based System-Level Anomaly Prediction for Smart Manufacturing

Wang, Jianwu; Liu, Chen; Zhu, Meiling; Guo, Pei; Hu, Yapeng

doi:10.1109/bigdatacongress.2018.00028

Cited by 26 publications

(16 citation statements)

References 23 publications

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“…We present the schematic illustration process of the work proposed iDRP framework, including the realization of the universal approximations of Multilayer Perceptron (MLP) with multiple hidden layers by exploiting the capabilities of H-ELM and other related techniques [30]- [33], which have the preeminent underexplored potentials for accelerated speed, rapid feature learning, and improved classification performance [30], [31], [34], [35]. Figure 1 Shows the schematic illustration of the setup of the experimental workflow process of the iDRP framework.…”

Section: Methodsmentioning

confidence: 99%

iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

Eboya¹,

Juremi²

2021

Adv. sci. technol. eng. syst. j.

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The Internet of Things (IoT) is at a face paced growth in the advanced Industrial Revolution (IR) 4.0 in the modern digital world. Considering the current network security challenges and sophistication of attacks in the heavily computerized and interconnected systems, such as an IoT ecosystem, the need for an innovative, robust, intelligent and adaptive malware attacks and threats security solution is becoming predominant in the current cyberspace. An integrated and scalable IoT malware detection framework called iDRP framework with deep learning method was proposed as a solution to current IoT malware attacks that are largely obfuscated. The novel framework utilized systematic pre-processing and post-processing techniques and methods on the BoTNetIoT malware datasets that contains both benign and malicious IoT traffic data infected by modern day IoT attacks such as Mirai and Gafgyt etc. IoT malware variants in an IoT ecosystem. The raw IoT malware binaries were converted to image files (Gray-scaled) and computed statistically with synthesised sparsed and differential evolutionary hidden feature structures techniques, which were cyclically trained, tested, and cross-validated to establish empirical anomalies with precision in the detection, recognizing, and prediction of malware anomalies in a modern IoT ecosystem. Preliminary experiments were conducted with standardized image binary files such as the datasets as sound scientific exploratory experiments with profound results. The comparative results of the performance of our integrated techniques and methods on the BoTNetIoT IoT malware datasets achieved a 99.98% accuracy, 99.99% ROC/AUC, 99.95% precision, and 99.93 recall rate etc. utilizing the integrated iDRP framework mechanisms for effectively detecting IoT malware in an IoT ecosystem.

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Section: Methodsmentioning

confidence: 99%

iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

Eboya¹,

Juremi²

2021

Adv. sci. technol. eng. syst. j.

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“…The existing cyber-security research [9]- [11] most relies on cyber signals, integrating physical signals for cyber-threat detection is still in its infancy. Methods were developed to detect cy-ber threats by monitoring and analyzing structural health of the finished parts [12], multiple physical signals of manufacturing process (such as acoustic signals, vibration, magnetic intensity, coal feed) [13], [14], vision and acoustic signals [15]. Traditionally, quality controls are used in MMS to reduce product variability and detect the equipment aging or failure with the goal to ensure the quality of manufacturing processes.…”

Section: State Of the Artmentioning

confidence: 99%

Cyber-Physical Taint Analysis in Multi-stage Manufacturing Systems (MMS): A Case Study

Liu,

Yang,

et al. 2021

Preprint

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Information flows are intrinsic properties of an multi-stage manufacturing systems (MMS). In computer security, a basic information flow tracking technique is dynamic taint analysis (DTA). DTA tracks taint propagation from one data variable (e.g., a buffer holding a HTTP request) to another. Taint propagation paths are typically determined by data flows and implicit flows in a computer program. And the union of all the taint propagation paths forms a taint graph. It is clear that taints graphs could significantly enhance intrusion diagnosis. However, the existing DTA techniques cannot be directly used in an MMS, and a main reason is as follows: Without manufacturing-specific taint propagation rules, DTA cannot be implemented. In this work, we conduct a case study which (a) extends the existing DTA method with manufacturing-specific taint propagation rules, and (b) applies the extended method to perform preliminary intrusion diagnosis with a small-scale test-bed.

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“…However, most of the data-driven PdM models in the literature employ supervised approaches that require prior labeled anomalies and are limited to short-range predictions (Tang et al, 2019;Huang et al, 2016;X. Li et al, 2020;Langone et al, 2020;Wang, Liu, Zhu, Guo, & Hu, 2018;Hadj-Kacem, Jemaa, Allio, & Slimen, 2020).…”

Section: Introductionmentioning

confidence: 99%

Long Horizon Anomaly Prediction in Multivariate Time Series with Causal Autoencoders

et al. 2022

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Predictive maintenance is essential for complex industrial systems to foresee anomalies before major system faults or ultimate breakdown. However, the existing efforts on Industry 4.0 predictive monitoring are directed at semi-supervised anomaly detection with limited robustness for large systems, which are often accompanied by uncleaned and unlabeled data. We address the challenge of predicting anomalies through data-driven end-to-end deep learning models using early warning symptoms on multivariate time series sensor data. We introduce AnoP, a long multi-timestep anomaly prediction system based on unsupervised attention-based causal residual networks, to raise alerts for anomaly prevention. The experimental evaluation on large data sets from detector health monitoring of the Hadron Calorimeter of the CMS Experiment at LHC CERN demonstrates the promising efficacy of the proposed approach. AnoP predicted around 60% of the anomalies up to seven days ahead, and the majority of the missed anomalies are abnormalities with unpredictable noisy-like behavior. Moreover, it has discovered previously unknown anomalies in the calorimeter’s sensors.

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Sensor Data Based System-Level Anomaly Prediction for Smart Manufacturing

Cited by 26 publications

References 23 publications

iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

Cyber-Physical Taint Analysis in Multi-stage Manufacturing Systems (MMS): A Case Study

Long Horizon Anomaly Prediction in Multivariate Time Series with Causal Autoencoders

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