A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.

[1]  Prakash Kripakaran,et al.  Support vector regression for anomaly detection from measurement histories , 2013, Adv. Eng. Informatics.

[2]  Chetan S. Kulkarni,et al.  Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models , 2019, International Journal of Prognostics and Health Management.

[3]  Michelle Lochner,et al.  Bayesian Anomaly Detection and Classification , 2019, Int. J. Hybrid Intell. Syst..

[4]  Fiorella Lauro,et al.  Fault detection analysis using data mining techniques for a cluster of smart office buildings , 2015, Expert Syst. Appl..

[5]  Milos Hauskrecht,et al.  Mining recent temporal patterns for event detection in multivariate time series data , 2012, KDD.

[6]  Georg Carle,et al.  Traffic Anomaly Detection Using K-Means Clustering , 2007 .

[7]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[8]  S M Abdullah Al Mamun,et al.  Anomaly Detection and Classification in Cellular Networks Using Automatic Labeling Technique for Applying Supervised Learning , 2018 .

[9]  Bart Baesens,et al.  Comprehensible software fault and effort prediction: A data mining approach , 2015, J. Syst. Softw..

[10]  Adam Prügel-Bennett,et al.  Data mining approaches for network intrusion detection: from dimensionality reduction to misuse and anomaly detection , 2012 .

[11]  Gary M. Weiss.  Data Mining in Telecommunications , 2005, The Data Mining and Knowledge Discovery Handbook.

[12]  Taghi M. Khoshgoftaar,et al.  Using regression trees to classify fault-prone software modules , 2002, IEEE Trans. Reliab..

[13]  Xiangji Huang,et al.  Mining network data for intrusion detection through combining SVMs with ant colony networks , 2014, Future Gener. Comput. Syst..

[14]  Simon Plass,et al.  VHF Data Exchange System (VDES): an enabling technology for maritime communications , 2018, CEAS Space Journal.

[15]  Subhransu Ranjan Samantaray,et al.  Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line , 2009 .

[16]  Tatsuya Morita,et al.  PCA-based robust anomaly detection using periodic traffic behavior , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[17]  Ejaz Ahmed,et al.  Clustering‐based real‐time anomaly detection—A breakthrough in big data technologies , 2019, Trans. Emerg. Telecommun. Technol..

[18]  Yedid Hoshen,et al.  Classification-Based Anomaly Detection for General Data , 2020, ICLR.

[19]  Tetsuo Kinoshita,et al.  Four Decades of Data Mining in Network and Systems Management , 2015, IEEE Transactions on Knowledge and Data Engineering.

[20]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[21]  L. Javier García-Villalba,et al.  Reasoning and Knowledge Acquisition Framework for 5G Network Analytics , 2017, Sensors.

[22]  Ashutosh Tiwari,et al.  Incremental information gain analysis of input attribute impact on RBF-kernel SVM spam detection , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[23]  Kristie B. Hadden,et al.  2020 , 2020, Journal of Surgical Orthopaedic Advances.

[24]  Mohammad Esmalifalak,et al.  A data mining approach for fault diagnosis: An application of anomaly detection algorithm , 2014 .

[25]  Xiufeng Liu,et al.  Regression-based Online Anomaly Detection for Smart Grid Data , 2016, ArXiv.

[26]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[27]  Shonali Krishnaswamy,et al.  Mining data streams: a review , 2005, SGMD.