Scalable and Configurable End-to-End Collection and Analysis of IoT Security Data : Towards End-to-End Security in IoT Systems

In recent years, there is a surge of interest in approaches pertaining to security issues of Internet of Things deployments and applications that leverage machine learning and deep learning techniques. A key prerequisite for enabling such approaches is the development of scalable infrastructures for collecting and processing security-related datasets from IoT systems and devices. This paper introduces such a scalable and configurable data collection infrastructure for data-driven IoT security. It emphasizes the collection of (security) data from different elements of IoT systems, including individual devices and smart objects, edge nodes, IoT platforms, and entire clouds. The scalability of the introduced infrastructure stems from the integration of state of the art technologies for large scale data collection, streaming and storage, while its configurability relies on an extensible approach to modelling security data from a variety of IoT systems and devices. The approach enables the instantiation and deployment of security data collection systems over complex IoT deployments, which is a foundation for applying effective security analytics algorithms towards identifying threats, vulnerabilities and related attack patterns.

[1]  Liang Xiao,et al.  IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security? , 2018, IEEE Signal Processing Magazine.

[2]  Athanasios V. Vasilakos,et al.  Security of the Internet of Things: perspectives and challenges , 2014, Wireless Networks.

[3]  Prem Prakash Jayaraman,et al.  OpenIoT: Open Source Internet-of-Things in the Cloud , 2014, OpenIoT@SoftCOM.

[4]  Qi Shi,et al.  Machine Learning Based Trust Computational Model for IoT Services , 2019, IEEE Transactions on Sustainable Computing.

[5]  Sudharman K. Jayaweera,et al.  Multi-Agent Reinforcement Learning Based Cognitive Anti-Jamming , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[6]  Hwee Pink Tan,et al.  Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications , 2014, IEEE Communications Surveys & Tutorials.

[7]  Ali Feizollah,et al.  Evaluation of machine learning classifiers for mobile malware detection , 2014, Soft Computing.

[8]  Yang Yu,et al.  A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks , 2016, Sensors.

[9]  Zhendong Ma,et al.  Security Viewpoint in a Reference Architecture Model for Cyber-Physical Production Systems , 2017, 2017 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

[10]  Sara Matzner,et al.  An application of machine learning to network intrusion detection , 1999, Proceedings 15th Annual Computer Security Applications Conference (ACSAC'99).

[11]  Christopher Krügel,et al.  Bayesian event classification for intrusion detection , 2003, 19th Annual Computer Security Applications Conference, 2003. Proceedings..

[12]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[13]  H. Vincent Poor,et al.  Machine Learning Methods for Attack Detection in the Smart Grid , 2015, IEEE Transactions on Neural Networks and Learning Systems.