A Security Based Perspective of Internet of Things
DOI:
https://doi.org/10.26438/ijsrnsc.v13i2.275Keywords:
Internet of Things(IoT), Cloud Computing, Machine learning, Deep learning, Intrusion Detection System, Wireless Sensor Network, Information AssuranceAbstract
Information technology is offering many technologies to all of us and among such systems and technologies IoT, Big Data, Cloud Computing etc. are considered as important and vital. The advancement and escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives by different sorts. The deployment of a large number of objects adhered to the internet has unlocked the vision of developing Digital Society and simply smart world around us, thereby paving a road towards automation and humongous data generation and collection. This intelligent Internet systems supported by the automation and continuous explosion of information to the digital world provides a healthy ground to the adversaries to perform numerous IT based Services and making our lives easy and it also helps in adhering cyber systems and information enriched society. The Security related aspects are important in emerging systems and here IoT based systems play a perfect role. Timely detection and prevention of such threats are pre-requisites to prevent serious consequences. Here in this work the survey conducted provides a brief insight into the technology with core attention to various attacks and anomalies including their detection based on the intelligent intrusion detection system(IDS). Further here comprehensive look-presented which provides an in-depth analysis as well as assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Moreover in this work aspects of healthcare in IoT is presented. This study also deals about the architecture, security, and privacy issues including their utilizations of learning paradigms in this sector. The research assessment here finally concluded by the listing of the results derived from the knowledge sources and literature. The paper also discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.
References
A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Commun. Surv. Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015, doi: 10.1109/COMST.2015.2444095.
P. Anand, Y. Singh, A. Selwal, M. Alazab, S. Tanwar, and N. Kumar, “IoT Vulnerability Assessment for Sustainable Computing: Threats, Current Solutions, and Open Challenges,” IEEE Access, vol. 8, pp. 1–1, 2020, doi: 10.1109/access.2020.3022842.
A. Yazdinejad, R. M. Parizi, A. Dehghantanha, Q. Zhang, and K. K. R. Choo, “An Energy-efficient SDN Controller Architecture for IoT Networks with Blockchain-based Security,” IEEE Trans. Serv. Comput., vol. 1374, 2020, doi: 10.1109/TSC.2020.2966970.
A. Čolaković and M. Hadžialić, “PT,” Comput. Networks, 2018, doi: 10.1016/j.comnet.2018.07.017.
“IoT Enabling Technologies - IoTbyHVM - Bits & Bytes of IoT.” .
X. Xu, J. Zhou, and H. Wang, “Research on the basic characteristics, the key technologies, the network architecture and security problems of the Internet of things,” Proc. 2013 3rd Int. Conf. Comput. Sci. Netw. Technol. ICCSNT 2013, pp. 825–828, 2014, doi: 10.1109/ICCSNT.2013.6967233.
X. Jia, Q. Feng, T. Fan, and Q. Lei, “RFID Technology and Its Applications in Internet of Things ( IOT ),” pp. 1282–1285, 2012.
“Importance of Sensors in the Internet of Things | IoT Sensors.” .
M. A. Matin, M. N. Islam, M. A. Matin, and M. M. Islam, “Overview of Wireless Sensor Network Chapter 1 Overview of Wireless Sensor Network,” Sep. 2012, doi: 10.5772/49376.
“Embedded systems in the Internet of Things | Embedded system | IoT.” .
“What is Cloud Computing.” .
S. Gupta, N. Kishore Sharma, and M. Dave, “Internet of Thing: A Survey on Architecture and Elements,” Int. J. Eng. Manag. Res., no. 6, pp. 239–242, 2016.
I. Yaqoob et al., “Internet of Things Architecture: Recent Advances, Taxonomy, Requirements, and Open Challenges,” IEEE Wirel. Commun., vol. 24, no. 3, pp. 10–16, 2017, doi: 10.1109/MWC.2017.1600421.
D. Navani, S. Jain, and M. S. Nehra, “The internet of things (IoT): A study of architectural elements,” Proc. - 13th Int. Conf. Signal-Image Technol. Internet-Based Syst. SITIS 2017, vol. 2018-Janua, pp. 473–478, 2018, doi: 10.1109/SITIS.2017.83.
H. F. Atlam and G. B. Wills, IoT Security, Privacy, Safety and Ethics, no. March. Springer International Publishing, 2020.
M. Bauer, “Internet-of-Things Architecture Project Deliverable D1 . 2 – Initial Architectural Reference Model for IoT,” Architecture, no. 257521, pp. 1–97, 2011.
L. Atzori, A. Iera, G. Morabito, and M. Nitti, “The social internet of things (SIoT) - When social networks meet the internet of things: Concept, architecture and network characterization,” Comput. Networks, vol. 56, no. 16, pp. 3594–3608, 2012, doi: 10.1016/j.comnet.2012.07.010.
Z. Qin, G. Denker, C. Giannelli, P. Bellavista, and N. Venkatasubramanian, “A software defined networking architecture for the internet-of-things,” IEEE/IFIP NOMS 2014 - IEEE/IFIP Netw. Oper. Manag. Symp. Manag. a Softw. Defin. World, 2014, doi: 10.1109/NOMS.2014.6838365.
J. Li, Y. Zhang, Y. F. Chen, K. Nagaraja, S. Li, and D. Raychaudhuri, “A mobile phone based WSN infrastructure for IoT over future internet architecture,” Proc. - 2013 IEEE Int. Conf. Green Comput. Commun. IEEE Internet Things IEEE Cyber, Phys. Soc. Comput. GreenCom-iThings-CPSCom 2013, pp. 426–433, 2013, doi: 10.1109/GreenCom-iThings-CPSCom.2013.89.
D. Singh, G. Tripathi, and A. J. Jara, “A survey of Internet-of-Things: Future vision, architecture, challenges and services,” 2014 IEEE World Forum Internet Things, WF-IoT 2014, pp. 287–292, 2014, doi: 10.1109/WF-IoT.2014.6803174.
C. Cecchinel, M. Jimenez, S. Mosser, and M. Riveill, “An Architecture to Support the Collection of Big Data in the Internet of Things,” pp. 442–449, 2014, doi: 10.1109/services.2014.83.
S. Kraijak and P. Tuwanut, “A survey on internet of things architecture, protocols, possible applications, security, privacy, real-world implementation and future trends,” Int. Conf. Commun. Technol. Proceedings, ICCT, vol. 2016-Febru, pp. 26–31, 2016, doi: 10.1109/ICCT.2015.7399787.
P. P. Ray, “A survey on Internet of Things architectures,” J. King Saud Univ. - Comput. Inf. Sci., vol. 30, no. 3, pp. 291–319, 2018, doi: 10.1016/j.jksuci.2016.10.003.
M. Weyrich and C. Ebert, “Reference architectures for the internet of things,” IEEE Softw., vol. 33, no. 1, pp. 112–116, 2016, doi: 10.1109/MS.2016.20.
D. Priyadarshi and A. Behura, “Analysis of Different IoT Protocols for Heterogeneous Devices and Cloud Platform,” Proc. 2018 IEEE Int. Conf. Commun. Signal Process. ICCSP 2018, pp. 868–872, 2018, doi: 10.1109/ICCSP.2018.8524531.
S. Al-sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, “Review,” pp. 685–690, 2017.
P. Sethi and S. R. Sarangi, “Internet of Things: Architectures, Protocols, and Applications,” J. Electr. Comput. Eng., vol. 2017, 2017, doi: 10.1155/2017/9324035.
“What is IoT Architecture? Explanation with Example of IoT Architecture.” .
T. Yu, V. Sekar, S. Seshan, Y. Agarwal, and C. Xu, “Handling a trillion ( unfixable ) flaws on a billion devices : Rethinking network security for the Internet-of-Things,” 2020.
I. Andrea, C. Chrysostomou, and G. Hadjichristofi, “Internet of Things: Security vulnerabilities and challenges,” Proc. - IEEE Symp. Comput. Commun., vol. 2016-Febru, pp. 180–187, 2016, doi: 10.1109/ISCC.2015.7405513.
O. El Madrasah al-Muḥammadīyah lil-Muhandisīn., M. Lahmer, and M. Belkasmi, “E-TI : la revue électronique des technologies de l’information.,” Electron. J. Inf. Technol., vol. 0, no. 9, pp. 24–37, 2016, doi: 10.1016/j.fitote.2010.12.007.
F. Aubet, “Machine Learning-Based Adaptive Anomaly Detection in Smart Spaces Machine Learning-Based Adaptive Anomaly Detection in Smart Spaces Frano,” no. January, 2019, doi: 10.13140/RG.2.2.35293.26088.
W. Meng, “Intrusion Detection in the Era of IoT: Building Trust via Traffic Filtering and Sampling,” Computer (Long. Beach. Calif)., vol. 51, no. 7, pp. 36–43, 2018, doi: 10.1109/MC.2018.3011034.
M. Nawir, A. Amir, N. Yaakob, and O. B. Lynn, “Internet of Things (IoT): Taxonomy of security attacks,” 2016 3rd Int. Conf. Electron. Des. ICED 2016, pp. 321–326, 2017, doi: 10.1109/ICED.2016.7804660.
I. Butun, P. Osterberg, and H. Song, “Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures,” IEEE Commun. Surv. Tutorials, vol. XX, no. X, pp. 1–1, 2019, doi: 10.1109/comst.2019.2953364.
“Into the Battlefield: A Security Guide to IoT Botnets - Security News - Trend Micro IN.” .
H. A. Abdul-Ghani, D. Konstantas, and M. Mahyoub, “A comprehensive IoT attacks survey based on a building-blocked reference model,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 3, pp. 355–373, 2018, doi: 10.14569/IJACSA.2018.090349.
K. M. Sadique, R. Rahmani, and P. Johannesson, “Towards security on internet of things: Applications and challenges in technology,” Procedia Comput. Sci., vol. 141, pp. 199–206, 2018, doi: 10.1016/j.procs.2018.10.168.
A. Riahi Sfar, E. Natalizio, Y. Challal, and Z. Chtourou, “A roadmap for security challenges in the Internet of Things,” Digit. Commun. Networks, vol. 4, no. 2, 2018, doi: 10.1016/j.dcan.2017.04.003.
M. binti Mohamad Noor and W. H. Hassan, “Current research on Internet of Things (IoT) security: A survey,” Comput. Networks, vol. 148, pp. 283–294, 2019, doi: 10.1016/j.comnet.2018.11.025.
I. Gudymenko and M. Hutter, “Security in the Internet of Things Supervisor :,” no. Itt, pp. 1–7, 2011.
J. Whitter-Jones, “Security review on the Internet of Things,” 2018 3rd Int. Conf. Fog Mob. Edge Comput. FMEC 2018, pp. 163–168, 2018, doi: 10.1109/FMEC.2018.8364059.
S. Benzarti, B. Triki, and O. Korbaa, “A survey on attacks in Internet of Things based networks,” Proc. - 2017 Int. Conf. Eng. MIS, ICEMIS 2017, vol. 2018-Janua, pp. 1–7, 2018, doi: 10.1109/ICEMIS.2017.8273006.
J. Deogirikar and A. Vidhate, “Security attacks in IoT: A survey,” Proc. Int. Conf. IoT Soc. Mobile, Anal. Cloud, I-SMAC 2017, pp. 32–37, 2017, doi: 10.1109/I-SMAC.2017.8058363.
G. Rajendran, R. S. Ragul Nivash, P. P. Parthy, and S. Balamurugan, “Modern security threats in the internet of things (IoT): Attacks and countermeasures,” Proc. - Int. Carnahan Conf. Secur. Technol., vol. 2019-Octob, 2019, doi: 10.1109/CCST.2019.8888399.
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,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 3453–3495, 2018, doi: 10.1109/COMST.2018.2855563.
N. Neshenko, E. Bou-harb, J. Crichigno, G. Kaddoum, and N. Ghani, “Demystifying IoT Security : An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-scale IoT Exploitations,” IEEE Commun. Surv. Tutorials, vol. 21, no. 3, pp. 2702–2733, 2019, doi: 10.1109/COMST.2019.2910750.
“Top 10 IoT vulnerabilities | Network World.” .
M. Bhardwaj, “security in Internet of Things applications.,” 2017. .
“IoT security starts with secure boot.” .
P. Porambage, C. Schmitt, P. Kumar, A. Gurtov, and M. Ylianttila, “PAuthKey : A Pervasive Authentication Protocol and Key Establishment Scheme for Wireless Sensor Networks in Distributed IoT Applications,” vol. 2014, 2014, doi: 10.1155/2014/357430.
T. Kothmayr, C. Schmitt, W. Hu, M. Br, and G. Carle, “DTLS based Security and Two-Way Authentication for the Internet of Things $,” no. May, 2013.
H. Shafagh, A. Hithnawi, and S. Duquennoy, “Talos : Encrypted Query Processing for the Internet of Things,” pp. 197–210, 2015.
E. Ronen and A. Shamir, “Extended Functionality Attacks on IoT Devices : The Case of Smart Lights ( Invited Paper ),” 2016, doi: 10.1109/EuroSP.2016.13.
B. Wei, G. Liao, and W. Li, “A Practical One-time File Encryption Protocol for IoT Devices,” pp. 0–5, 2017, doi: 10.1109/CSE-EUC.2017.206.
A. Sivanathan, H. H. Gharakheili, and V. Sivaraman, “Can We Classify an IoT Device using TCP Port Scan?,” in 2018 IEEE 9th International Conference on Information and Automation for Sustainability, ICIAfS 2018, 2018, doi: 10.1109/ICIAFS.2018.8913346.
L. Markowsky and G. Markowsky, “Scanning for Vulnerable Devices in the Internet of Things,” no. February, 2016, doi: 10.1109/IDAACS.2015.7340779.
V. J. Ercolani, M. W. Patton, and H. Chen, “Shodan Visualized,” pp. 193–195, 2016.
C. Konstantinou and M. Maniatakos, “Impact of Firmware Modification Attacks on Power Systems Field Devices,” pp. 283–288, 2015.
S. Schechter, “The Current State of Access Control for Smart Devices in Homes,” 2013.
D. Song and D. Wagner, “Smart Locks : Lessons for Securing Commodity Internet of Things Devices,” 2016.
“5 Simple IoT Devices That Can Become Entry Points for Hackers - CPO Magazine.” .
M. Saiful, I. Mamun, A. F. M. S. Kabir, S. Hossen, and R. Hayat, “Policy based intrusion detection and response system in hierarchical WSN architecture .,” no. September 2015, 2009.
M. F. Elrawy, A. I. Awad, and H. F. A. Hamed, “Intrusion detection systems for IoT-based smart environments: a survey,” J. Cloud Comput., vol. 7, no. 1, pp. 1–20, 2018, doi: 10.1186/s13677-018-0123-6.
U. Noor, Z. Anwar, T. Amjad, and K. K. R. Choo, “A machine learning-based FinTech cyber threat attribution framework using high-level indicators of compromise,” Futur. Gener. Comput. Syst., vol. 96, pp. 227–242, 2019, doi: 10.1016/j.future.2019.02.013.
J. R. Quinlan, “Induction of Decision Trees,” pp. 81–106, 2007.
S. Kaplantzis, A. Shilton, and N. Mani, “Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines,” pp. 335–340, 2007.
M. Martfnez-Arroyo and L. E. Sucar, “Learning an optimal naive Bayes classifier,” Proc. - Int. Conf. Pattern Recognit., vol. 3, pp. 1236–1239, 2006, doi: 10.1109/ICPR.2006.748.
Y. Electronics, O. Source, A. N. Networks, and A. Ann, “Introduction to Artificial Neural Networks ( ANN ),” no. February, pp. 1–5, 2009.
J. Oyelade et al., “Data Clustering: Algorithms and Its Applications,” Proc. - 2019 19th Int. Conf. Comput. Sci. Its Appl. ICCSA 2019, no. ii, pp. 71–81, 2019, doi: 10.1109/ICCSA.2019.000-1.
V. S. Anke Meyer-Baese, “Dimensionality reduction,” in Pattern Recognition and Signal Analysis in Medical Imaging, 2014.
E. Anthi, L. Williams, and P. Burnap, “Pulse: An adaptive intrusion detection for the internet of things,” IET Conf. Publ., vol. 2018, no. CP740, pp. 1–4, 2018, doi: 10.1049/cp.2018.0035.
Divyatmika and M. Sreekesh, “A two-tier network based intrusion detection system architecture using machine learning approach,” Int. Conf. Electr. Electron. Optim. Tech. ICEEOT 2016, pp. 42–47, 2016, doi: 10.1109/ICEEOT.2016.7755404.
H. H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, and K. K. R. Choo, “A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks,” IEEE Trans. Emerg. Top. Comput., vol. 7, no. 2, pp. 314–323, 2019, doi: 10.1109/TETC.2016.2633228.
M. R. Shahid, G. Blanc, Z. Zhang, and H. Debar, “Machine Learning for IoT Network Monitoring,” RESSI (Rendez-Vous la Rech. l’Enseignement la Sécurité des Systèmes d’Information), 2019.
S. M. Srinivasan, T. Truong-Huu, and M. Gurusamy, “Machine Learning-Based Link Fault Identification and Localization in Complex Networks,” IEEE Internet Things J., vol. 6, no. 4, pp. 6556–6566, 2019, doi: 10.1109/JIOT.2019.2908019.
N. Moustafa and J. Slay, “The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set,” Inf. Secur. J., vol. 25, no. 1–3, pp. 18–31, 2016, doi: 10.1080/19393555.2015.1125974.
J. Canedo and A. Skjellum, “Using machine learning to secure IoT systems,” 2016 14th Annu. Conf. Privacy, Secur. Trust. PST 2016, pp. 219–222, 2016, doi: 10.1109/PST.2016.7906930.
C. Ioannou and V. Vassiliou, “Classifying security attacks in IoT networks using supervised learning,” Proc. - 15th Annu. Int. Conf. Distrib. Comput. Sens. Syst. DCOSS 2019, pp. 652–658, 2019, doi: 10.1109/DCOSS.2019.00118.
S. Zhao, W. Li, T. Zia, and A. Y. Zomaya, “A dimension reduction model and classifier for anomaly-based intrusion detection in internet of things,” Proc. - 2017 IEEE 15th Int. Conf. Dependable, Auton. Secur. Comput. 2017 IEEE 15th Int. Conf. Pervasive Intell. Comput. 2017 IEEE 3rd Int. Conf. Big Data Intell. Compu, vol. 2018-Janua, pp. 836–843, 2018, doi: 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.141.
S. Prabavathy, K. Sundarakantham, and S. M. Shalinie, “Design of Cognitive Fog Computing for Intrusion Detection in Internet of Things,” J. Commun. Networks, vol. 20, no. 3, pp. 291–298, 2018, doi: 10.1109/JCN.2018.000041.
M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” Internet of Things, vol. 7, p. 100059, 2019, doi: 10.1016/j.iot.2019.100059.
N. Moustafa, B. Turnbull, and K. K. R. Choo, “An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things,” IEEE Internet Things J., vol. 6, no. 3, pp. 4815–4830, 2019, doi: 10.1109/JIOT.2018.2871719.
S. Y. Lee, S. R. Wi, E. Seo, J. K. Jung, and T. M. Chung, “ProFiOt: Abnormal Behavior Profiling (ABP) of IoT devices based on a machine learning approach,” 2017 27th Int. Telecommun. Networks Appl. Conf. ITNAC 2017, vol. 2017-Janua, pp. 1–6, 2017, doi: 10.1109/ATNAC.2017.8215434.
K. Yang, J. Ren, Y. Zhu, and W. Zhang, “SECURITY AND PRIVACY IN THE WIRELESS INTERNET OF THINGS : EMERGING TRENDS AND CHALLENGES Active Learning for Wireless IoT Intrusion Detection,” IEEE Wirel. Commun., vol. 25, no. December, pp. 19–25, 2018, doi: 10.1109/MWC.2017.1800079.
Q. Shafi, A. Basit, S. Qaisar, A. Koay, and I. Welch, “Fog-Assisted SDN Controlled Framework for Enduring Anomaly Detection in an IoT Network,” IEEE Access, vol. 6, pp. 73713–73723, 2018, doi: 10.1109/ACCESS.2018.2884293.
F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine Learning in IoT Security: Current Solutions and Future Challenges,” no. June, 2019.
V. K. Rahul, R. Vinayakumar, K. Soman, and P. Poornachandran, “Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security,” 2018 9th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2018, pp. 1–6, 2018, doi: 10.1109/ICCCNT.2018.8494096.
D. S. Gupta, “Fundamentals of Deep Learning – Activation Functions and When to Use Them?,” 2020. .
“A Practical Guide to ReLU - Danqing Liu - Medium.” .
A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access, vol. 7, pp. 53040–53065, 2019, doi: 10.1109/ACCESS.2019.2912200.
S. Albawi, T. A. M. Mohammed, and S. Alzawi, “A DATA-DRIVEN APPROACH TO PRECIPITATION PARAMETERIZATIONS USING CONVOLUTIONAL ENCODER-DECODER NEURAL NETWORKS Pablo,” Ieee, 2017.
M. Roopak, G. Yun Tian, and J. Chambers, “Deep learning models for cyber security in IoT networks,” 2019 IEEE 9th Annu. Comput. Commun. Work. Conf. CCWC 2019, pp. 452–457, 2019, doi: 10.1109/CCWC.2019.8666588.
D. H. Kim and J. E. Ha, “Multi-lane detection using convolutional neural networks and transfer learning,” J. Inst. Control. Robot. Syst., vol. 23, no. 9, pp. 718–724, 2017, doi: 10.5302/J.ICROS.2017.17.0107.
“Understanding RNN and LSTM - Towards Data Science.” .
“Deep Learning | Introduction to Long Short Term Memory - GeeksforGeeks.” .
A. Diro and N. Chilamkurti, “Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications,” IEEE Commun. Mag., vol. 56, no. 9, pp. 124–130, 2018, doi: 10.1109/MCOM.2018.1701270.
M. R. Shahid, G. Blanc, and Z. Zhang, “Anomalous Communications Detection in IoT Networks Using Sparse Autoencoders.”
Y. Meidan et al., “N-BaIoT : Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders,” vol. 13, no. 9, pp. 1–8, 2018.
C. D. McDermott, F. Majdani, and A. V. Petrovski, “Botnet Detection in the Internet of Things using Deep Learning Approaches,” Proc. Int. Jt. Conf. Neural Networks, vol. 2018-July, pp. 1–8, 2018, doi: 10.1109/IJCNN.2018.8489489.
A. A. Diro and N. Chilamkurti, “Distributed attack detection scheme using deep learning approach for Internet of Things,” Futur. Gener. Comput. Syst., vol. 82, pp. 761–768, 2018, doi: 10.1016/j.future.2017.08.043.
N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A Deep Learning Approach to Network Intrusion Detection,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 2, no. 1, pp. 41–50, 2018, doi: 10.1109/tetci.2017.2772792.
F. Ullah et al., “Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach,” IEEE Access, vol. 7, pp. 124379–124389, 2019, doi: 10.1109/access.2019.2937347.
H. Yao, P. Gao, J. Wang, P. Zhang, C. Jiang, and Z. Han, “Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities,” IEEE Internet Things J., vol. 6, no. 5, pp. 7515–7525, 2019, doi: 10.1109/JIOT.2019.2901348.
A. Telikani and A. H. Gandomi, “Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things,” Internet of Things, p. 100122, 2019, doi: 10.1016/j.iot.2019.100122.
H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K. K. R. Choo, “A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting,” Futur. Gener. Comput. Syst., vol. 85, pp. 88–96, 2018, doi: 10.1016/j.future.2018.03.007.
C. H. Liao, H. H. Shuai, and L. C. Wang, “RNN-Assisted Network Coding for Secure Heterogeneous Internet of Things with Unreliable Storage,” IEEE Internet Things J., vol. 6, no. 5, pp. 7608–7622, 2019, doi: 10.1109/JIOT.2019.2902376.
S. Siboni et al., “Security Testbed for Internet-of-Things Devices,” IEEE Trans. Reliab., vol. 68, no. 1, pp. 23–44, 2019, doi: 10.1109/TR.2018.2864536.
Y. Teranishi, Y. Saito, S. Murono, and N. Nishinaga, “JOSE : An Open Testbed for Field Trials of Large-scale IoT Services,” J. Natl. Inst. Inf. Commun. Technol., vol. 62, no. 2, pp. 151–159, 2015.
F. Alam, R. Mehmood, I. Katib, and A. Albeshri, “Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT),” Procedia Comput. Sci., vol. 58, no. DaMIS 2016, pp. 437–442, 2016, doi: 10.1016/j.procs.2016.09.068.
R. R. R. Robinson and C. Thomas, “Ranking of machine learning algorithms based on the performance in classifying DDoS attacks,” 2015 IEEE Recent Adv. Intell. Comput. Syst. RAICS 2015, no. December, pp. 185–190, 2016, doi: 10.1109/RAICS.2015.7488411.
L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?,” IEEE Signal Process. Mag., vol. 35, no. 5, pp. 41–49, 2018, doi: 10.1109/MSP.2018.2825478.
M. A. Alsheikh, S. Lin, D. Niyato, and H. P. Tan, “Machine learning in wireless sensor networks: Algorithms, strategies, and applications,” IEEE Commun. Surv. Tutorials, vol. 16, no. 4, pp. 1996–2018, 2014, doi: 10.1109/COMST.2014.2320099.
R. C. Deo and B. K. Nallamothu, “Learning about Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record,” Circ. Cardiovasc. Qual. Outcomes, vol. 9, no. 6, pp. 618–620, 2016, doi: 10.1161/CIRCOUTCOMES.116.003308.
“Recurrent Neural Networks and LSTM explained - purnasai gudikandula - Medium.” .
E. Fazeldehkordi, O. Owe, and J. Noll, “Security and privacy in iot systems: A case study of healthcare products,” Int. Symp. Med. Inf. Commun. Technol. ISMICT, vol. 2019-May, pp. 1–8, 2019, doi: 10.1109/ISMICT.2019.8743971.
I. Singh and D. Kumar, “Improving IOT Based Architecture of Healthcare System,” 2019 4th Int. Conf. Inf. Syst. Comput. Networks, ISCON 2019, pp. 113–117, 2019, doi: 10.1109/ISCON47742.2019.9036287.
S. Lavanya, G. Lavanya, and J. Divyabharathi, “Remote prescription and I-Home healthcare based on IoT,” IEEE Int. Conf. Innov. Green Energy Healthc. Technol. - 2017, IGEHT 2017, pp. 1–3, 2017, doi: 10.1109/IGEHT.2017.8094069.
N. Kumar, “IoT architecture and system design for healthcare systems,” Proc. 2017 Int. Conf. Smart Technol. Smart Nation, SmartTechCon 2017, pp. 1118–1123, 2018, doi: 10.1109/SmartTechCon.2017.8358543.
A. C. Tokognon, B. Gao, G. Y. Tian, and Y. Yan, “Structural Health Monitoring Framework Based on Internet of Things: A Survey,” IEEE Internet Things J., vol. 4, no. 3, pp. 629–635, 2017, doi: 10.1109/JIOT.2017.2664072.
S. Alromaihi, W. Elmedany, and C. Balakrishna, “Cyber security challenges of deploying IoT in smart cities for healthcare applications,” Proc. - 2018 IEEE 6th Int. Conf. Futur. Internet Things Cloud Work. W-FiCloud 2018, pp. 140–145, 2018, doi: 10.1109/W-FiCloud.2018.00028.
S. Poorejbari and W. Mansoor, “Smart healthcare systems on improving the efficiency of healthcare services,” 2019 2nd Int. Conf. Signal Process. Inf. Secur. ICSPIS 2019, pp. 1–4, 2019, doi: 10.1109/ICSPIS48135.2019.9045894.
A. I. Newaz, A. K. Sikder, M. A. Rahman, and A. S. Uluagac, “HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems,” 2019 6th Int. Conf. Soc. Networks Anal. Manag. Secur. SNAMS 2019, pp. 389–396, 2019, doi: 10.1109/SNAMS.2019.8931716.
S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh, and W. C. Hong, “Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward,” IEEE Access, vol. 8, pp. 474–448, 2020, doi: 10.1109/ACCESS.2019.2961372.
N. V. Pardakhe and V. M. Deshmukh, “Machine Learning and Blockchain Techniques Used in Healthcare System,” 2019 IEEE Pune Sect. Int. Conf. PuneCon 2019, pp. 1–5, 2019, doi: 10.1109/PuneCon46936.2019.9105710.
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