An ensemble hyper-tuned model for IoT sensors attacks and anomaly detection

References

• Sharma, Pradip Kumar, Saurabh Singh, Young-Sik Jeong, and Jong Hyuk Park. “Distblocknet: A distributed blockchains-based secure sdn architecture for iot networks.” IEEE Communications Magazine 55, no. 9 (2017): 78-85.
   Web of Science ®Google Scholar
• Al-Fuqaha, Ala, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, and Moussa Ayyash. “Internet of things: A survey on enabling technologies, protocols, and applications.” IEEE communications surveys & tutorials 17, no. 4 (2015): 2347-2376.
   Web of Science ®Google Scholar
• Vishal Kumar, Gayatri Sakya & Chandra Shankar .“WSN and IoT based smart city model using the MQTT protocol.”, Journal of Discrete Mathematical Sciences and Cryptography, 22, no.8 (2019): 1423-1434.
   Web of Science ®Google Scholar
• Manyika, James, Michael Chui, Jacques Bughin, Richard Dobbs, Peter Bisson, and Alex Marrs. Disruptive technologies: Advances that will transform life, business, and the global economy. Vol. 180. San Francisco, CA: McKinsey Global Institute, 2013.
   Google Scholar
• Vinita Malik & Sukhdip Singh. “Security risk management in IoT environment.”, Journal of Discrete Mathematical Sciences and Cryptography, 22, no.4 (2019), 697-709.
   Web of Science ®Google Scholar
• Sheng, Zhengguo, Shusen Yang, Yifan Yu, Athanasios V. Vasilakos, Julie A. McCann, and Kin K. Leung. “A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities.” IEEE wireless communications 20, no. 6 (2013): 91-98.
   Web of Science ®Google Scholar
• Andrea, Ioannis, Chrysostomos Chrysostomou, and George Hadjichristofi. “Internet of Things: Security vulnerabilities and challenges.” In 2015 IEEE Symposium on Computers and Communication (ISCC), pp. 180-187. IEEE, 2015.
   Google Scholar
• Xiao, Liang, Xiaoyue Wan, Xiaozhen Lu, Yanyong Zhang, and Di Wu. “IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?.” IEEE Signal Processing Magazine 35, no. 5 (2018): 41-49.
   Web of Science ®Google Scholar
• Suo, Hui, Jiafu Wan, Caifeng Zou, and Jianqi Liu. “Security in the internet of things: a review.” In 2012 international conference on computer science and electronics engineering, vol. 3, pp. 648-651. IEEE, 2012.
   Google Scholar
• Lopez-Martin, Manuel, Belen Carro, Antonio Sanchez-Esguevillas, and Jaime Lloret. “Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot.” Sensors 17, no. 9 (2017): 1967.
   Web of Science ®Google Scholar
• Acarali, Dilara, Muttukrishnan Rajarajan, Nikos Komninos, and Bruno BogazZarpelão. “Modelling the spread of botnet malware in IoT-based wireless sensor networks.” Security and Communication Networks 2019 (2019).
   Web of Science ®Google Scholar
• Hosseinpour, Farhoud, Payam Vahdani Amoli, Juha Plosila, Timo Hämäläinen, and Hannu Tenhunen. “An intrusion detection system for fog computing and IoT based logistic systems using a smart data approach.” International Journal of Digital Content Technology and its Applications 10 (2016).
   Google Scholar
• M.-O. Pahl, F.-X. Aubet, DS2OS traffic traces. ( https://www.kaggle.com/francoisxa/ds2ostraffictraces ). [Online; accessed 17-June-2020].
   Google Scholar
• Breiman, L. (June 1997). “Arcing The Edge” (PDF). Technical Report 486. Statistics Department, University of California, Berkeley.
   Google Scholar
• Meidan, Yair, Michael Bohadana, Yael Mathov, Yisroel Mirsky, Asaf Shabtai, Dominik Breitenbacher, and Yuval Elovici. “N-baiot—network-based detection of iot botnet attacks using deep autoencoders.” IEEE Pervasive Computing 17, no. 3 (2018): 12-22.
   Web of Science ®Google Scholar
• Brun, Olivier, Yonghua Yin, and Erol Gelenbe. “Deep learning with dense random neural network for detecting attacks against iot-connected home environments.” Procedia computer science 134 (2018): 458-463.
   Google Scholar
• Cervantes, Christian, Diego Poplade, Michele Nogueira, and Aldri Santos. “Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things.” In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 606-611. IEEE, 2015.
   Google Scholar
• Bhunia, Suman Sankar, and Mohan Gurusamy. “Dynamic attack detection and mitigation in IoT using SDN.” In 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1-6. IEEE, (2017).
   Google Scholar
• Hasan, Mahmudul, Md Milon Islam, Md Ishrak Islam Zarif, and M. M. A. Hashem. “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches.” Internet of Things 7, (2019): 100059.
   Google Scholar
• Hodo, Elike, Xavier Bellekens, Andrew Hamilton, Pierre-Louis Dubouilh, Ephraim Iorkyase, Christos Tachtatzis, and Robert Atkinson. “Threat analysis of IoT networks using artificial neural network intrusion detection system.” In 2016 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1-6. IEEE, (2016).
   Google Scholar
• Kozik, R., Choraś, M., Ficco, M. and Palmieri, F., 2018. A scalable distributed machine learning approach for attack detection in edge computing environments. Journal of Parallel and Distributed Computing, 119, pp.18-26.
   Web of Science ®Google Scholar
• Pongle, Pavan, and Gurunath Chavan. “Real time intrusion and wormhole attack detection in internet of things.” International Journal of Computer Applications 121, no. 9, (2015).
   Google Scholar
• Raza, Shahid, Linus Wallgren, and Thiemo Voigt. “SVELTE: Realtime intrusion detection in the Internet of Things.” Ad hoc networks 11, no. 8 (2013): 2661-2674.
   Web of Science ®Google Scholar
• Jameel Ahamed, Md. Zahid, Mohd Omar & Khaleel Ahmad., “AES and MQTT based security system in the internet of things”, Journal of Discrete Mathematical Sciences and Cryptography, 22:no.8,(2019),1589-1598.
   Web of Science ®Google Scholar
• Tsitsiroudi, Niki, Panagiotis Sarigiannidis, Eirini Karapistoli, and Anastasios A. Economides. “EyeSim: A mobile application for visual-assisted wormhole attack detection in IoT-enabled WSNs.” In 2016 9th IFIP Wireless and Mobile Networking Conference (WMNC), pp. 103-109. IEEE, 2016.
   Google Scholar
• Yin, Da, Lianming Zhang, and Kun Yang. “A DDoS attack detection and mitigation with software-defined Internet of Things frame-work.” IEEE Access 6 (2018): 24694-24705.
   Web of Science ®Google Scholar
• Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay. “Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques.” In International Conference on Mobile Networks and Management, pp. 30-44. Springer, Cham, 2017.
   Google Scholar
• Park, Kihong, and Heejo Lee. “On the effectiveness of route-based packet filtering for distributed DoS attack prevention in power-law internets.” ACM SIGCOMM computer communication review 31, no. 4 (2001): 15-26.
   Web of Science ®Google Scholar
• Ahmad, Iftikhar, Azween B. Abdullah, and Abdullah S. Alghamdi. “Application of artificial neural network in detection of probing attacks.” In 2009 IEEE Symposium on Industrial Electronics & Applications, vol. 2, pp. 557-562. IEEE, 2009.
   Google Scholar
• Abrams, Marshall, and Joe Weiss. “Malicious control system cyber security attack case study–Maroochy Water Services, Australia.” McLean, VA: The MITRE Corporation (2008).
   Google Scholar
• Ying Wang, Jian He, Hao Zhao, Yao-Hui Han & Xiao-Jiang Huang. “Intelligent community medical service based on internet of things.”, Journal of Interdisciplinary Mathematics, 21, no.5 (2018), 1121-1126.
   Web of Science ®Google Scholar
• Ali, SkSubidh, Samah M. Saeed, Ozgur Sinanoglu, and Ramesh Karri. “Novel test-mode-only scan attack and countermeasure for compression-based scan architectures.” IEEE transactions on computer-aided design of integrated circuits and systems 34, no. 5 (2015): 808-821.
   Web of Science ®Google Scholar
• Valente, Junia, Matthew A. Wynn, and Alvaro A. Cardenas. “Stealing, spying, and abusing: Consequences of attacks on internet of things devices.” IEEE Security & Privacy 17, no. 5 (2019): 10-21.
   Web of Science ®Google Scholar
• Zhang, Yun-Peng, and Xi-Ping Xu. “Design of a wearable human-computer interaction system based on bioelectrical signal recognition technology.” Journal of Interdisciplinary Mathematics 21, no. 5 (2018): 1049-1054.
   Web of Science ®Google Scholar
• Smutz, Charles, and Angelos Stavrou. “Malicious PDF detection using metadata and structural features.” In Proceedings of the 28th annual computer security applications conference, pp. 239-248. 2012.
   Google Scholar
• Polikar, Robi. “Ensemble learning.” In Ensemble machine learning, pp. 1-34. Springer, Boston, MA, 2012.
   Google Scholar
• Shaikh, Farooq, Elias Bou-Harb, Jorge Crichigno, and Nasir Ghani. “A machine learning model for classifying unsolicited iot devices by observing network telescopes.” In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 938-943. IEEE, 2018.
   Google Scholar
• gradient Boosting. https://liyanxu.blog/2018/10/29/gradient-boosting-vs-forward-stagewise-boosting/screen-shot-2018-10-30-at-22-45-44/. Accessed 23 June 2020.
   Google Scholar
• Omar, Nazlia, Mohammed Albared, Adel Qasem Al-Shabi, and Tareq Al-Moslmi. “Ensemble of classification algorithms for subjectivity and sentiment analysis of Arabic customers’ reviews.” International Journal of Advancements in Computing Technology 5, no. 14 (2013): 77.
   Google Scholar
• True Positive Rate and False Positive Rate. https://algolytics.com/how-to-assess-quality-and-correctness-of-classification-models-part-4-roc-curve. Accessed by 23-06-2020.
   Google Scholar
• Sonavane, Sonali, Alka Khade, and V. B. Gaikwad. “Novel approach for localization of indian car number plate recognition system using support vector machine.” International Journal of Advanced Research in Computer Science and Software Engineering 3, no. 8 (2013).
   Google Scholar