Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network
المؤلفون المشاركون
Wang, Bailing
Liu, Hongri
Qu, Haikuo
Wang, Chao
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-03
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency.
The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT.
ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives.
ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years.
The risk of being attacked increases as a result.
In order to protect these assets, intrusion detection systems (IDS) have drawn much attention.
As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS.
In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed.
Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone.
With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack.
In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset.
The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Chao& Wang, Bailing& Liu, Hongri& Qu, Haikuo. 2020. Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1214941
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Chao…[et al.]. Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1214941
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Chao& Wang, Bailing& Liu, Hongri& Qu, Haikuo. Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1214941
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214941
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر