Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks

Telecommunication has registered strong and rapid growth in the past decade. Accordingly, the monitoring of computers and networks is too complicated for network administrators. Hence, network security represents one of the biggest serious challenges that can be faced by network security communities. Taking into consideration the fact that e-banking, e-commerce, and business data will be shared on the computer network, these data may face a threat from intrusion. The purpose of this research is to propose a methodology that will lead to a high level and sustainable protection against cyberattacks. In particular, an adaptive anomaly detection framework model was developed using deep and machine learning algorithms to manage automatically-configured application-level firewalls. The standard network datasets were used to evaluate the proposed model which is designed for improving the cybersecurity system. The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks. The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. The system was used to classify DoS and DDoS attacks in four stand datasets namely KDD cup 199, NSL-KDD, ISCX, and ICI-ID2017. The empirical results indicate that the deep learning based on the LSTM-RNN algorithm has obtained the highest accuracy. The proposed system based on the LSTM-RNN algorithm produced the highest testing accuracy rate of 99.51% and 99.91% with respect to KDD Cup’99, NSL-KDD, ISCX, and ICI-Id2017 datasets, respectively. A comparative result analysis between the machine learning algorithms, namely SVM and KNN, and the deep learning algorithms based on the LSTM-RNN model is presented. Finally, it is concluded that the LSTM-RNN model is efficient and effective to improve the cybersecurity system for detecting anomaly-based cybersecurity.

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Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data

Industrial Management & Data Systems ◽

10.1108/imds-06-2016-0195 ◽

2017 ◽

Vol 117 (5) ◽

pp. 927-945 ◽

Cited By ~ 12

Author(s):

Taehoon Ko ◽

Je Hyuk Lee ◽

Hyunchang Cho ◽

Sungzoon Cho ◽

Wounjoo Lee ◽

...

Keyword(s):

Machine Learning ◽

Quality Management ◽

Data Integration ◽

Anomaly Detection ◽

Learning Algorithms ◽

Perceived Quality ◽

Machine Learning Algorithms ◽

Series Data ◽

Content Type ◽

Service Data

Purpose Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines. Design/methodology/approach By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset. Findings By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped. Originality/value Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.

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Application and evaluation of selected machine learning algorithms in anomaly detection module for SOC

Developments of Artificial Intelligence Technologies in Computation and Robotics ◽

10.1142/9789811223334_0117 ◽

2020 ◽

Author(s):

A. Warzyński ◽

P. Bienias ◽

G. Kołaczek

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Algorithms ◽

Machine Learning Algorithms

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Machine Learning for Diagnosis of Event Synchronization Faults in Discrete Manufacturing Systems

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4052762 ◽

2021 ◽

pp. 1-31

Author(s):

Joseph Cohen ◽

Baoyang Jiang ◽

Jun Ni

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Random Forest ◽

Anomaly Detection ◽

Unsupervised Learning ◽

Learning Algorithms ◽

Discrete Event ◽

Machine Learning Algorithms ◽

Programmable Logic ◽

Discrete Manufacturing

Abstract Common in discrete manufacturing, timed event systems often have strict synchronization requirements for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based random forest and extreme random forest classifiers achieve excellent performance with a precision and recall score of 0.96 for multi-fault classification. Additionally, the unsupervised learning approach yields anomaly detection rates of 98% with false alarms under 3% with a training set 99% smaller than the supervised learning classifiers. These results obtained on a real use case are promising to enable prognostic tools in industrial automation systems in the future

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Anomaly Detection and Classification in Predictive Maintenance Tasks with Zero Initial Training

IoT ◽

10.3390/iot2040030 ◽

2021 ◽

Vol 2 (4) ◽

pp. 590-609

Author(s):

Filippo Morselli ◽

Luca Bedogni ◽

Umberto Mirani ◽

Michele Fantoni ◽

Simone Galasso

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Industrial Revolution ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Predictive Maintenance ◽

Initial Training ◽

Novel Technologies ◽

Fourth Industrial Revolution

The Fourth Industrial Revolution has led to the adoption of novel technologies and methodologies in factories, making these more efficient and productive. Among the new services which are changing industry, there are those based on machine learning algorithms, which enable machines to learn from their past observations and hence possibly forecast future states. Specifically, predictive maintenance represents the opportunity to understand in advance possible machine outages due to broken parts and schedule the necessary maintenance operations. However, in real scenarios predictive maintenance struggles to be adopted due to a multitude of variables and the heavy customization it requires. In this work, we propose a novel framework for predictive maintenance, which is trained online to recognize new issues reported by the operators. Our framework, tested on different scenarios and with a varying number and several kinds of sensors, shows recall levels above 0.85, demonstrating its effectiveness and adaptability.

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