Detection of Cyber Attacks using Machine Learning based Intrusion Detection System for IoT Based Smart Cities
Keywords:Internet of Things (IoT), Smart Cities, UAVs
The world’s dynamics is evolving with artificial intelligence (AI) and the results are smart products. A smart city has smart city is collection of smart innovations powered with AI and internet of things (IoTs). Along with the ease and comfort that the concept of a smart city pointed at, many security concerns are being raised that hinders the path of its flourishment. An Intrusion Detection System (IDS) monitors the whole network traffic and alerts in case of any anomaly. A Machine Learning-based IDS intelligently senses the network threats, takes decisions about data packet legibility and alarm the user. Researchers have deployed various ML techniques to IDS to improve the detection accuracy. This work presents a comparative analysis of various ML algorithms trained over UNSW-NB15 dataset. ADA Boost, Linear Support Vector Machine (LSVM), Auto Encoder Classifier, Quadratic Support Vector Machine (QSVM) and Multi-Layer Perceptron algorithms are being employed in the stimulation. ADA Boost showed an excellent accuracy of 98.3% in the results.
Çimen, H.; Palacios-García, E.J.; Kolaek, M.; Çetinkaya, N.; Vasquez, J.C.; Guerrero, J.M. Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring. IEEE Ind. Electron. Mag. 2020, 15, 4–15. DOI: https://doi.org/10.1109/MIE.2020.3023075
Santiago, I.; Moreno-Munoz, A.; Quintero-Jiménez, P.; Garcia-Torres, F.; Gonzalez-Redondo, M. Electricity demand during pandemic times: The case of the COVID-19 in Spain. Energy Policy 2021, 148, 111964. DOI: https://doi.org/10.1016/j.enpol.2020.111964
Coffey, K.; Maglaras, L.A.; Smith, R.; Janicke, H.; Ferrag, M.A.; Derhab, A.; Mukherjee, M.; Rallis, S.; Yousaf, A. Vulnerability assessment of cyber security for SCADA systems. In Guide to Vulnerability Analysis for Computer Networks and Systems; Springer: Berlin/Heidelberg, Germany, 2018; pp. 59–80. DOI: https://doi.org/10.1007/978-3-319-92624-7_3
Panagiotis, Fountas, Kouskouras Taxiarxchis, Kranas Georgios, Leandros Maglaras, and Mohamed Amine Ferrag. "Intrusion Detection in Critical Infrastructures: A Literature Review." Smart Cities 4, no. 3 (2021): 1146-1157. DOI: https://doi.org/10.3390/smartcities4030061
L. Hung-Jen and C.-h. R. Lin, “Intrusion detection system a comprehensive review,” Journal of network and applications, vol. 36, no. 1, pp. 16–24, 2013. DOI: https://doi.org/10.1016/j.jnca.2012.09.004
H. L. Motoda and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, vol. 454, Springer, 1998.
L. D. S. Silva, A. C. Santos, T. D. Mancilha, J. D. Silva, and A. Montes, “Detecting attack signatures in the real network traffic with ANNIDA,” Expert Systems with Applications, vol. 34, no. 4, pp. 2326–2333, 2008. DOI: https://doi.org/10.1016/j.eswa.2007.03.011
Rincy N, Thomas, and Roopam Gupta. "Design and development of an efficient network intrusion detection system using machine learning techniques." Wireless Communications and Mobile Computing 2021 (2021). DOI: https://doi.org/10.1155/2021/9974270
A. Qayyum, L. Viennot, and A. Laouiti, “Multipoint relaying for flooding broadcast messages in mobile wireless networks,” in Proceedings of the 35th annual Hawaii international conference on system sciences, pp. 3866–3875, Big Island, HI, USA, 2002.
I. U. Khan, I. M. Qureshi, M. A. Aziz, T. A. Cheema, and S. B. H. Shah, “Smart IoT control-based nature inspired energy efficient routing protocol for flying ad hoc network (FANET),” IEEE Access, vol. 8, pp. 56371–56378, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2981531
M. Ahmed and A. K. Pathan, “False data injection attack (FDIA): an overview and new metrics for fair evaluation of its countermeasure,” Complex Adaptive Systems Modeling, vol. 8, no. 1, p. 4, 2020. DOI: https://doi.org/10.1186/s40294-020-00070-w
A. Abdollahi and M. Fathi, “An intrusion detection system on ping of death attacks in IoT networks,” Wireless Personal Communications, vol. 112, no. 4, pp. 2057–2070, 2020. DOI: https://doi.org/10.1007/s11277-020-07139-y
Khan, Inam Ullah, Asrin Abdollahi, Ryan Alturki, Mohammad Dahman Alshehri, Mohammed Abdulaziz Ikram, Hasan J. Alyamani, and Shahzad Khan. "Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks." Wireless Communications and Mobile Computing 2021 (2021). DOI: https://doi.org/10.1155/2021/1542657
Khan, Inam Ullah, Muhammad Abul Hassan, Muhammad Fayaz, Jeonghwan Gwak, and Muhammad Adnan Aziz. "Improved sequencing heuristic DSDV protocol using nomadic mobility model for FANETS." Comput., Mater. Continua 70, no. 2 (2022): 3653-3666. DOI: https://doi.org/10.32604/cmc.2022.020697
Khan, Inam Ullah, Muhammad Abul Hassan, Mohammad Dahman Alshehri, Mohammed Abdulaziz Ikram, Hasan J. Alyamani, Ryan Alturki, and Vinh Truong Hoang. "Monitoring system-based flying IoT in public health and sports using ant-enabled energy-aware routing." Journal of Healthcare Engineering 2021 (2021). DOI: https://doi.org/10.1155/2021/1686946
Khan, Inam Ullah, Ryan Alturki, Hasan J. Alyamani, Mohammed Abdulaziz Ikram, Muhammad Adnan Aziz, Vinh Truong Hoang, and Tanweer Ahmad Cheema. "RSSI-controlled long-range communication in secured IoT-enabled unmanned aerial vehicles." Mobile information systems 2021 (2021). DOI: https://doi.org/10.1155/2021/5523553
Alasbali, Nada, Saaidal Razalli Bin Azzuhri, Rosli Bin Salleh, Miss Laiha Mat Kiah, Ahmad Aliff AS Shariffuddin, Nik Muhammad Izwan bin Nik Mohd Kamel, and Leila Ismail. "Rules of Smart IoT Networks within Smart Cities towards Blockchain Standardization." Mobile Information Systems 2022 (2022). DOI: https://doi.org/10.1155/2022/9109300
Abomhara, Mohamed, and Geir M. Køien. "Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks." Journal of Cyber Security and Mobility (2015): 65-88. DOI: https://doi.org/10.13052/jcsm2245-1439.414
Saharkhizan, Mahdis, et al. "An ensemble of deep recurrent neural networks for detecting IoT cyber attacks using network traffic." IEEE Internet of Things Journal 7.9 (2020): 8852-8859. DOI: https://doi.org/10.1109/JIOT.2020.2996425
I. Stellios, P. Kotzanikolaou, M. Psarakis, C. Alcaraz and J. Lopez, "A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services," in IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 3453-3495, Fourthquarter 2018, doi: 10.1109/COMST.2018.2855563. DOI: https://doi.org/10.1109/COMST.2018.2855563
Dvorkin, Yury, and Siddharth Garg. "IoT-enabled distributed cyber-attacks on transmission and distribution grids." 2017 North American Power Symposium (NAPS). IEEE, 2017. DOI: https://doi.org/10.1109/NAPS.2017.8107363
Rana, Md Masud. "IoT-based electric vehicle state estimation and control algorithms under cyber attacks." IEEE Internet of Things Journal 7.2 (2019): 874-881. DOI: https://doi.org/10.1109/JIOT.2019.2946093
Diaz Lopez, Daniel, et al. "Shielding IoT against cyber-attacks: An event-based approach using SIEM." Wireless Communications and Mobile Computing 2018 (2018). DOI: https://doi.org/10.1155/2018/3029638
Tabassum, Aliya, and Wadha Lebda. "Security Framework for IoT Devices against Cyber-attacks." arXiv preprint arXiv:1912.01712 (2019). DOI: https://doi.org/10.5121/csit.2019.91321
Roopak, Monika, Gui Yun Tian, and Jonathon Chambers. "Deep learning models for cyber security in IoT networks." 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). IEEE, 2019. DOI: https://doi.org/10.1109/CCWC.2019.8666588
F. Farivar, M. S. Haghighi, A. Jolfaei and M. Alazab, "Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT," in IEEE Transactions on Industrial Informatics, vol. 16, no. 4, pp. 2716-2725, April 2020, doi: 10.1109/TII.2019.2956474. DOI: https://doi.org/10.1109/TII.2019.2956474
Sikder, Amit Kumar, et al. "A survey on sensor-based threats and attacks to smart devices and applications." IEEE Communications Surveys & Tutorials 23.2 (2021): 1125-1159.
AlDairi, Anwaar. "Cyber security attacks on smart cities and associated mobile technologies." Procedia Computer Science 109 (2017): 1086-1091. DOI: https://doi.org/10.1016/j.procs.2017.05.391
Al‐Turjman, Fadi, Hadi Zahmatkesh, and Ramiz Shahroze. "An overview of security and privacy in smart cities' IoT communications." Transactions on Emerging Telecommunications Technologies 33.3 (2022): e3677. DOI: https://doi.org/10.1002/ett.3677
Sikder, Amit Kumar, et al. "A survey on sensor-based threats and attacks to smart devices and applications." IEEE Communications Surveys & Tutorials 23.2 (2021): 1125-1159. DOI: https://doi.org/10.1109/COMST.2021.3064507
Zhang, Kuan, et al. "Sybil attacks and their defenses in the internet of things." IEEE Internet of Things Journal 1.5 (2014): 372-383. DOI: https://doi.org/10.1109/JIOT.2014.2344013
Gowtham, M., and H. B. Pramod. "Semantic query-featured ensemble learning model for SQL-injection attack detection in IoT-ecosystems." IEEE Transactions on Reliability (2021).
Falco, Gregory, et al. "A master attack methodology for an AI-based automated attack planner for smart cities." IEEE Access 6 (2018): 48360-48373. DOI: https://doi.org/10.1109/ACCESS.2018.2867556
Garcia-Teodoro, Pedro, et al. "Anomaly-based network intrusion detection: Techniques, systems and challenges." computers & security 28.1-2 (2009): 18-28. DOI: https://doi.org/10.1016/j.cose.2008.08.003
Kumar, Vinod, and Om Prakash Sangwan. "Signature based intrusion detection system using SNORT." International Journal of Computer Applications & Information Technology 1.3 (2012): 35-41.
Otoum, Yazan, and Amiya Nayak. "As-ids: Anomaly and signature based ids for the internet of things." Journal of Network and Systems Management 29.3 (2021): 1-26. DOI: https://doi.org/10.1007/s10922-021-09589-6
Einy, Sajad, Cemil Oz, and Yahya Dorostkar Navaei. "The anomaly-and signature-based IDS for network security using hybrid inference systems." Mathematical Problems in Engineering 2021 (2021). DOI: https://doi.org/10.1155/2021/6639714
Xu, Chuanfeng, et al. "An SDNFV-based DDoS defense technology for smart cities." IEEE Access 7 (2019): 137856-137874. DOI: https://doi.org/10.1109/ACCESS.2019.2943146
Moustafa, Nour, and Jill Slay. "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)." Military Communications and Information Systems Conference (MilCIS), 2015. IEEE, 2015. DOI: https://doi.org/10.1109/MilCIS.2015.7348942
Moustafa, Nour, and Jill Slay. "The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 dataset and the comparison with the KDD99 dataset." Information Security Journal: A Global Perspective (2016): 1-14. DOI: https://doi.org/10.1080/19393555.2015.1125974
Moustafa, Nour, et al. "Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks." IEEE Transactions on Big Data (2017).
Moustafa, Nour, et al. "Big data analytics for intrusion detection system: statistical decision-making using finite dirichlet mixture models." Data Analytics and Decision Support for Cybersecurity. Springer, Cham, 2017. 127-156. DOI: https://doi.org/10.1007/978-3-319-59439-2_5
Sarhan, Mohanad, Siamak Layeghy, Nour Moustafa, and Marius Portmann. NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems. In Big Data Technologies and Applications: 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, December 11, 2020, Proceedings (p. 117). Springer Nature. DOI: https://doi.org/10.1007/978-3-030-72802-1_9
Moustafa, Nour, et al. "An Ensemble Intrusion Detection Technique based on proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things." IEEE Internet of Things Journal (2018). DOI: https://doi.org/10.1109/JIOT.2018.2871719
Koroniotis, Nickolaos, Moustafa, Nour, et al. "Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques." International Conference on Mobile Networks and Management. Springer, Cham, 2017. DOI: https://doi.org/10.1007/978-3-319-90775-8_3
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