Collusion attacks in Internet of Things: Detection and mitigation using a fog based model

This paper discusses the problem of collusion attacks in Internet of Things (IoT) environments and how mobility of IoT devices increases the difficulty of detecting such types of attacks. It demonstrates how approaches used in detecting collusion attacks in WSNs are not applicable in IoT environments. To this end, the paper introduces a model based on the Fog Computing infrastructure to keep track of IoT devices and detect collusion attackers. The model uses fog computing layer for real-time monitoring and detection of collusion attacks in IoT environments. Moreover, the model uses a software defined system layer to add a degree of flexibility for configuring Fog nodes in order to enable them to detect various types of collusion attacks. Furthermore, the paper highlights the possible overhead on Fog nodes and network when applying the proposed model, and claims that the Fog layer infrastructure can provide the required resources for the scalability of the model.

[1]  Paul Van Dooren,et al.  Iterative Filtering in Reputation Systems , 2010, SIAM J. Matrix Anal. Appl..

[2]  Anders Hast,et al.  Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop , 2009 .

[3]  Yaser Jararweh,et al.  Internet of surveillance: a cloud supported large-scale wireless surveillance system , 2016, The Journal of Supercomputing.

[4]  Yaser Jararweh,et al.  A Fog Computing Based System for Selective Forwarding Detection in Mobile Wireless Sensor Networks , 2016, 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W).

[5]  Yi-Cheng Zhang,et al.  Information filtering via Iterative Refinement , 2006, ArXiv.

[6]  Mani B. Srivastava,et al.  Reputation-based framework for high integrity sensor networks , 2008, TOSN.

[7]  Elisa Bertino,et al.  Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks , 2015, IEEE Transactions on Dependable and Secure Computing.

[8]  Yi-Cheng Zhang,et al.  Decoding Information from noisy, redundant, and intentionally-distorted sources , 2006 .

[9]  Tao Zhou,et al.  A robust ranking algorithm to spamming , 2010, ArXiv.

[10]  Anurag Agarwal,et al.  The Internet of Things—A survey of topics and trends , 2014, Information Systems Frontiers.

[11]  Meichun Hsu,et al.  Clustering billions of data points using GPUs , 2009, UCHPC-MAW '09.

[12]  Mahmoud Al-Ayyoub,et al.  CloudExp: A comprehensive cloud computing experimental framework , 2014, Simul. Model. Pract. Theory.

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

[14]  Belle L. Tseng,et al.  User reputation in a comment rating environment , 2011, KDD.

[15]  Erman Ayday,et al.  An iterative algorithm for trust and reputation management , 2009, 2009 IEEE International Symposium on Information Theory.

[16]  Mahmoud Al-Ayyoub,et al.  The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing , 2016, 2016 23rd International Conference on Telecommunications (ICT).

[17]  Elisa Bertino,et al.  A robust iterative filtering technique for wireless sensor networks in the presence of malicious attacks , 2013, SenSys '13.

[18]  Mahmoud Al-Ayyoub,et al.  SDMEC: Software Defined System for Mobile Edge Computing , 2016, 2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW).

[19]  Hong Cheng,et al.  Robust Reputation-Based Ranking on Bipartite Rating Networks , 2012, SDM.

[20]  Mahmoud Al-Ayyoub,et al.  A new broadcast scheme for sensor networks , 2014, 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA).

[21]  Sangkyum Kim,et al.  Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems , 2010, 2010 IEEE International Conference on Data Mining.

[22]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.