A Pervasive Computational Intelligence based Cognitive Security Co-design Framework for Hype-connected Embedded Industrial IoT

  • Usman Tariq Prince Sattam bin Abdulaziz University
  • Tariq Ahamed Ahanger
  • Muneer Nusir Prince Sattam bin Abdulaziz University
  • Atef Ibrahim Prince Sattam bin Abdulaziz University

Abstract

The amplified connectivity of routine IoT entities can expose various security trajectories for cybercriminals to execute malevolent attacks. These dangers are even amplified by the source limitations and heterogeneity of low-budget IoT/IIoT nodes, which create existing multitude-centered and fixed perimeter-oriented security tools inappropriate for vibrant IoT settings. The offered emulation assessment exemplifies the remunerations of implementing context aware co-design oriented cognitive security method in assimilated IIoT settings and delivers exciting understandings in the strategy execution to drive forthcoming study. The innovative features of our system is in its capability to get by with irregular system connectivity as well as node limitations in terms of scares computational ability, limited buffer (at edge node), and finite energy. Based on real-time analytical data, projected scheme select the paramount probable end-to-end security system possibility that ties with an agreed set of node constraints. The paper achieves its goals by recognizing some gaps in the security explicit to node subclass that is vital to our system’s operations.

References

[1] Athreya, A.; DeBruhl, A.; Tague, A. (2013) Designing for Self-Configuration and Self- Adaptation in the Internet of Things, in Proceedings of the 9th IEEE International Conference on Collaborative Computing: Networking,Applications and Work sharing, pp. 585-592, doi: 10.4108/icst.collaboratecom.2013.254091
https://doi.org/10.4108/icst.collaboratecom.2013.254091

[2] Al-Turjman, F.; Ever, Y. K.; Ever, E.; Nguyen, H. X.;David, D. B. (2017) Seamless Key Agreement Framework for Mobile-Sink in IoT Based Cloud-Centric Secured Public Safety Sensor Networks, IEEE Access, Vol. 5, pp. 24617-24631, doi: 10.1109/ACCESS.2017.2766090
https://doi.org/10.1109/ACCESS.2017.2766090

[3] Aleskerov, E.; Rao, B. (1997), CARDWATCH: a neural network based database mining system for credit card fraud detection, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), New York City, NY, USA, pp. 220-226, doi: 10.1109/CIFER.1997.618940.
https://doi.org/10.1109/CIFER.1997.618940

[4] Atlam, H.; Walters. R.; Wills, G. (2018), Fog Computing and the Internet of Things: A Review, Big Data and Cognitive Computing, Vol.2(2), Issue 10. pp.1-18,
https://doi.org/10.3390/bdcc2020010

[5] Abomhara, M.; Kien, G. (2015), Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks, J. Cyber Secur. Mobil., Vol. 4, Issue 1, pp. 65-88,
https://doi.org/10.13052/jcsm2245-1439.414

[6] Atzori, L.; Iera, A.; Morabito, G. (2010), The Internet of Things: A survey, Comput. Networks, Vol. 54, Issue 15, pp. 2787-2805,
https://doi.org/10.1016/j.comnet.2010.05.010

[7] Ahanger, T.; Aljumah, A. (2018), Internet of Things: A Comprehensive Study of Security Issues and Defense Mechanisms, IEEE Access, Vol. 7, pp. 11020-11028, doi: 10.1109/ACCESS. 2018.2876939
https://doi.org/10.1109/ACCESS.2018.2876939

[8] Al-Shihabi, T.; Mourant, R. (2003), Toward More Realistic Driving Behavior Models for Autonomous Vehicles in Driving Simulators, Transp. Res. Rec. J. Transp. Res. Board. Vol 1843, Issue 1, pp. 41-49. doi:10.3141/1843-06
https://doi.org/10.3141/1843-06

[9] Bilal, M. (2017), Review of Internet of Things Architecture , Technologies and Analysis Smartphone-based Attacks Against 3D printers, ArXiv: Networking and Internet Architecture, Vol. abs/1708.04560, pp. 1-21, http://arxiv.org/abs/1708.04560

[10] Bhattacharjee, P.; Roy, S.; and Pa, R. (2015), Mutual Authentication Technique with Four Entities Using Fuzzy Neural Network in 4-G Mobile Communications, IOSR Journal of Computer Engineering (IOSR-JCE), Vol.1, Issue 4, pp. 69-76.

[11] Bhattacharjee, P., C Koner, CT Bhunia, U Maulik (2009), A novel four entity mutual authentication technique for 3-G mobile communications International Journal Recent Trends in Engineering, Vol 2, Issue 2, pp. 29-31.

[12] Choi, D.; Lee, K. (2018), An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation, Security and Communication Networks. Vol. 2018, pp. 1-15 ,
https://doi.org/10.1155/2018/5483472

[13] Chen, D.; Cong, J.; Gurumani, S.; Hwu, W.; Rupnow, K.; Zhang, Z. (2016), Platform choices and design demands for IoT platforms: cost, power, and performance trade offs, IET Cyber-Physical Syst. Theory, Vol. 1, Issue 1, pp. 70-77,
https://doi.org/10.1049/iet-cps.2016.0020

[14] Frotzscher, A.; Wetzker, U.; Bauer, M.; Rentschler, M.; Beyer, M.; Elspass, S.; Klessing, H. (2014) Requirements and current solutions of wireless communication in industrial automation, in 2014 IEEE International Conference on Communications Workshops, ICC 2014. pp. 67-72, doi: 10.1109/ICCW.2014.6881174
https://doi.org/10.1109/ICCW.2014.6881174

[15] Gehrke, J.; Ganti, V.; Ramakrishnan, R.; Lo, W. (1999), BOAT-optimistic decision tree construction, in Proceedings of the 1999 ACM SIGMOD international conference on Management of data - SIGMOD '99, Vol. 28, Issue 2, pp. 1-12,
https://doi.org/10.1145/304181.304197

[16] Hodo, E.; Bellekens, X.; Hamilton, A.; Dubouilh, P.; Lorkyase, E.; Tachtatzis, C.; Atkinson, R. (2016), Threat analysis of IoT networks using artificial neural network intrusion detection system, 2016 International Symposium on Networks, Computers and Communications. pp. 1-6, doi: 10.1109/ISNCC.2016.7746067
https://doi.org/10.1109/ISNCC.2016.7746067

[17] Hopalı, E.; Vayvay, O. (2018), Internet of Things (IoT) and its Challenges for Usability in Developing Countries, Int. J. Innov. Eng. Sci. Res., vol. 2, Issue 1, pp. 1-5

[18] Hiller, J.; Henze, M.; Serror, M.; Wagner, E.; Richter, J.; Wehrle, K. (2018) Secure Low Latency Communication for Constrained Industrial IoT Scenarios, IEEE 43rd Conference on Local Computer Networks (LCN), Chicag, pp. 614-622, doi: 10.1109/LCN.2018.8638027
https://doi.org/10.1109/LCN.2018.8638027

[19] Hummen, R.; Shafagh, H.; Raza, S.; Voig, T.; Wehrle, K. (2014), Delegation-based authentication and authorization for the IP-based Internet of Things, in 2014 11th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2014. pp. 284-292, doi: 10.1109/SAHCN.2014.6990364.
https://doi.org/10.1109/SAHCN.2014.6990364

[20] ICO (2017), Big data, artificial intelligence, machine learning and data protection Data Protection Act and General Data Protection Regulation, Data Protection Act and General Data Protection Regulation. Version: 2.2, pp. 1-114

[21] Javaid, N.; Sher, A.; Nasir, H.; Guizani, N. (2018), Intelligence in IoT-Based 5G Networks: Opportunities and Challenges, IEEE Communications Magazine, vol. 56, Issue 10, pp. 94-100, doi: 10.1109/MCOM.2018.1800036.
https://doi.org/10.1109/MCOM.2018.1800036

[22] Kazmi, A.; Jan, Z.; Zappa, A.; Serrano, M; (2017) Overcoming the Heterogeneity in the Internet of Things for Smart Cities, InterOSS@IoT. vol 10218, pp. 20-35,
https://doi.org/10.1007/978-3- 319-56877-5_2

[23] Krishnan, G. S. S. (2013), Computational Intelligence, Cyber Security and Computational Models, in Proceedings of ICC3. pp. 1-262,
https://doi.org/10.1007/978-981-13-0716-4

[24] Kim, J.; Bahk, S. (2009), Design of certification authority using secret redistribution and multicast routing in wireless mesh networks, Comput.Networks, vol. 53, pp. 98-109,
https://doi.org/10.1016/j.comnet.2008.09.017

[25] Kim, J.; Bahk, S. (2016), How to design an IoT-ready infrastructure: The 4-stage architecture, TechBecon [Online], Available: https://techbeacon.com/4-stages-iot-architecture

[26] Katsikeas, S.; Fysarakis, K.; Miaoudakis, A.; Bemten, A.; Askoxylakis, I.; Papaefstathiou, I.; Plemenos, A (2017), Lightweight and secure industrial IoT communications via the MQ telemetry transport protocol, IEEE Symposium on Computers and Communications, pp. 1193-1200, doi: 10.1109/ISCC.2017.8024687
https://doi.org/10.1109/ISCC.2017.8024687

[27] Kama N., French T., Reynolds M. (2010), Considering Patterns in Class Interactions Prediction International Conference on Advanced Software Engineering and Its Applications, vol 117, pp.11- 22,
https://doi.org/10.1007/978-3-642-17578-7_2

[28] Kuflik, T.; Shoval, P. (2000), Generation of user profiles for information filtering research agenda (poster session), In Proceedings of the23rd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 313-315,
https://doi.org/10.1145/345508.345615

[29] Minoli, D.;Sohraby, K.; Occhiogrosso, B.. (2017), IoT Considerations, Requirements, and Architectures for Smart Buildings-Energy Optimization and Next-Generation Building Management Systems, Internet of Things Journal Vol. 4, Issue 1, pp. 269-283, doi: 10.1109/JIOT.2017.2647881
https://doi.org/10.1109/JIOT.2017.2647881

[30] Ma, H. D. (2011), Internet of things: Objectives and scientific challenges, J.Comput. Sci. Technol. Vol. 26, pp. 919-924,
https://doi.org/10.1007/s11390-011-1189-5

[31] Mahdavinejad, M.; Rezvan, M.; Barekatain, M.; Adibi, P.; Barnaghi, P.; Sheth, A. (2017), Machine learning for internet of things data analysis: a survey, Digit. Commun. Networks, Vol. 4, Issue. 3, pp. 161-175
https://doi.org/10.1016/j.dcan.2017.10.002

[32] Mayer, M. (2018), Artificial Intelligence and Cyber Power from a Strategic Perspective, IFS Insights, ISSN 1894-4795, pp. 1-34

[33] Mohmmad, S.; Sirajuddin, M.; sHABANA, A. (2016), IoT Middleware for Device Privacy on Big Data, International Journal of Innovative Research in Science, Engineering and Technology. Vol. 5, Issue 6, pp. 1-8, doi:10.15680/IJIRSET.2015.0506143

[34] Marafie, Z.; Lin, K.; Zhai, Y.; Li, J. (2018), ProActive Fintech: Using Intelligent IoT to Deliver Positive InsurTech Feedback, 20th IEEE International Conference on Business Informatics, pp. 72-81, doi: 10.1109/CBI.2018.10048.
https://doi.org/10.1109/CBI.2018.10048

[35] Ngu, A. H. H.; Gutierrez, M; Metsis, V; Nepal, S.; Shen, M. Z.;(2017) IoT Middleware: A Survey on Issues and Enabling Technologies, IEEE Internet Things J. Vol. 4, Issue 1, pp. 1-20, doi: 10.1109/JIOT.2016.2615180.
https://doi.org/10.1109/JIOT.2016.2615180

[36] Nascimento, N. (2015), A Self-Configurable IoT Agent System based on Environmental Variability, 17th International Conference on Autonomous Agents and MultiAgent, Pages 1761-1763, doi: 10.5555/3237383.3237966

[37] Nune, K. G.; Sena, P. (2013), Novel Artificial Neural Networks and Logistic Approach for Detecting Credit Card Deceit, International Journal of Computer Science and Network Security, Voll. 13, Issue 9, pp. 58-65

[38] Preuveneers, D.; Berbers, Y. (2008), Internet of things: A context-awareness perspective, The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems, Book, pp. 287-307
https://doi.org/10.1201/9781420052824.ch13

[39] Perez, J. A.; Deligianni, F.; Ravi, D.;Zang, G. (2016), Artificial Intelligence and Robotics, ArVix., pp. 1-56, https://arxiv.org/abs/1803.10813

[40] Phiri, J.;Zhao, T.; Zhu, C.; Mbale, J. (2011) Using Artificial Intelligence Techniques to Implement a Multi factor Authentication System, Int. J. Comput. Intell. Syst., Vol. 4, Issue 4, pp. 420-430
https://doi.org/10.1080/18756891.2011.9727801

[41] Patel, K.; Pate, S. M. (2016), Internet of Things-IOT: definition, characteristics, architecture, enabling technologies, application and future challenges, Int. J. Eng. Sci. Comput., Vol. 6, Issue 5, pp. 6122-6131

[42] Provos, P. (2018), Announcing the general availability of Azure IoT Central, Microsoft Azure Blog, https://azure.microsoft.com/en-us/blog/azure-iot-central-ga/

[43] Park, R. (2015), Guide to Zero-Day Exploits, Symantec, Online, https://tinyurl.com/3la3shcj/

[44] Roy, A.;Dasgupta, D. (2018) A fuzzy decision support system for multi factor authentication, Sofy Comput. Vol. 22, Issue 12, pp. 3959-3981
https://doi.org/10.1007/s00500-017-2607-6

[45] Ramakalyani, K.; Umadev, D. (2012), Fraud Detection of Credit Card Payment System by Genetic Algorithm, t. J. Sci. Eng. Res. Vol. 3, Issue 7, pp. 1-6.

[46] Rahmatizadeh, R.; Khan, S.; Jayasumana, A.; Turgut, D.; Boloni, L. (2014), Routing towards a mobile sink using virtual coordinates in a wireless sensor network, in IEEE International Conference on Communications, pp. 12-17, doi: 10.1109/ICC.2014.6883287.
https://doi.org/10.1109/ICC.2014.6883287

[47] Sayar, D.; Er O. (2018), The Antecedents of Successful IoT Service and System Design: Cases from the Manufacturing Industry, Int. J. Des. vol. 12, Issue 1, pp. 1-12
https://doi.org/10.1111/dmj.12035

[48] Solmaz, G.; Turgut. D. (2013), Event coverage in theme parks using wireless sensor networks with mobile sinks, in IEEE International Conference on Communications. pp. 1522-1526, doi: 10.1109/ICC.2013.6654729.
https://doi.org/10.1109/ICC.2013.6654729

[49] Sharma, V; Liu, H.; Honggang, W.; Shelley, Z. (2017), Securing wireless communications of connected vehicles with artificial intelligence, in IEEE International Symposium on Technologies for Homeland Security, pp. 1-7, doi: 10.1109/THS.2017.7943477
https://doi.org/10.1109/THS.2017.7943477

[50] Talari, S.; Shafie-khah, M.; Siano, P.; Loia, V.; Tommasetti, A.; Catalao, J. (2017), A Review of Smart Cities Based on the Internet of Things Concept, MDPI Energies Vol. 10, Issue 4, pp. 1-23,
https://doi.org/10.3390/en10040421

[51] Taddeo, M.; Floridi, L. (2018), Regulate artificial intelligence to avert cyber arms race. Nature 556.7701, pp. 296-298, doi: 10.1038/d41586-018-04602-6
https://doi.org/10.1038/d41586-018-04602-6

[52] Tariq, U.; Aseeri, A.; Alkatheiri, M, ; Zhuang, Y. (2020), Context-aware autonomous security assertion for Industrial IoT, IEEE Access, Vol. 8, pp. 191785-191794, doi: 10.1109/ACCESS. 2020.3032436.
https://doi.org/10.1109/ACCESS.2020.3032436

[53] Vermesan, O.; Eisenhauer, M.; Sundmaeker, H.; Guillemin, P.; Serrano, M.; Tragos, E.; Valino, J.; Wees, A.; Gluhak, A.; Bahr, R. (2017), Internet of Things Cognitive Transformation Technology Research Trends and Applications, Cogn. Hyperconnected Digit.Transform. Internet Things Intell. Evol. pp. 17-95, http://hdl.handle.net/11250/2489025

[54] Xu, K.; Qu, Y.; Yang, K. (2016), tutorial on the internet of things: From a heterogeneous network integration perspective, IEEE Network, Vol. 30, Iaaue 2, pp. 102-108, doi: 10.1109/MNET.2016.7437031.
https://doi.org/10.1109/MNET.2016.7437031

[55] Yang, K.; Cho, S. (2017), A context-aware system in Internet of Things using modular Bayesian networks, International Journal of Distributed Sensor Networks, Vol. 13, Issue 5, pp. 1-18, doi:10.1177/1550147717708986
https://doi.org/10.1177/1550147717708986

[56] Yang, L.; Chen, Y.; Zuo, W.; Nguyen, T.; Gurumani, S.; Rupnow, K.; Chen, D. (2015), Systemlevel design solutions: Enabling the IoT explosion, in IEEE 11th International Conference on ASIC (ASICON), 2015, pp. 1-4, doi: 10.1109/ASICON.2015.7517023.
https://doi.org/10.1109/ASICON.2015.7517023

[57] Zahoor, S.; Mir, R. (2018) Resource management in pervasive Internet of Things: A survey, Journal of King Saud University - Computer and Information Sciences, pp. 1-15,
https://doi.org/10.1016/j.jksuci.2018.08.014.
Published
2021-03-03
How to Cite
TARIQ, Usman et al. A Pervasive Computational Intelligence based Cognitive Security Co-design Framework for Hype-connected Embedded Industrial IoT. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 2, mar. 2021. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/4029>. Date accessed: 15 apr. 2021. doi: https://doi.org/10.15837/ijccc.2021.2.4029.