A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices
المؤلفون المشاركون
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-05
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
The Internet of Things (IoT) is rapidly spreading in various application scenarios through its salient features in ubiquitous device connections, ranging from agriculture and industry to transportation and other fields.
As the increasing spread of IoT applications, IoT security is gradually becoming one of the most significant issues to guard IoT devices against various cybersecurity threats.
Usually, IoT devices are the main components responsible for sensing, computing, and transmitting; in this case, how to efficiently protect the IoT device itself away from cyber attacks, like malware, virus, and worm, becomes the vital point in IoT security.
This paper presents a brand new architecture of intrusion detection system (IDS) for IoT devices, which is designed to identify device- or host-oriented attacks in a lightweight manner in consideration of limited computation resources on IoT devices.
To this end, in this paper, we propose a stacking model to couple the Extreme Gradient Boosting (XGBoost) model and the Long Short-Term Memory (LSTM) model together for the abnormal state analysis on the IoT devices.
More specifically, we adopt the system call sequence as the indicators of abnormal behaviors.
The collected system call sequences are firstly processed by the famous n-gram model, which is a common method used for host-based intrusion detections.
Then, the proposed stacking model is used to identify abnormal behaviors hidden in the system call sequences.
To evaluate the performance of the proposed model, we establish a real-setting IP camera system and place several typical IoT attacks on the victim IP camera.
Extensive experimental evaluations show that the stacking model has outperformed other existing anomaly detection solutions, and we are able to achieve a 0.983 AUC score in real-world data.
Numerical testing demonstrates that the XGBoost-LSTM stacking model has excellent performance, stability, and the ability of generalization.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xiali& Lu, Xiang. 2020. A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1214667
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xiali& Lu, Xiang. A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1214667
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xiali& Lu, Xiang. A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1214667
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214667
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