Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network

The revolutionary idea of the internet-of-things (IoT) architecture has gained enormous recognition over the last decade, resulting in exponential growth in the networks, connected devices, and the data processed therein. Since IoT devices generate and exchange a massive amount of sensitive data ove...

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Main Author:	Zeeshan, Ahmad
Format:	Thesis
Language:	English English
Published:	2023
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/41363/5/Thesis%20Form_Zeeshan%20Ahmad.pdf http://ir.unimas.my/id/eprint/41363/6/Zeeshan%20Ahmad%20ft.pdf
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id	my-unimas-ir.41363
record_format	uketd_dc
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Zeeshan, Ahmad Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
description	The revolutionary idea of the internet-of-things (IoT) architecture has gained enormous recognition over the last decade, resulting in exponential growth in the networks, connected devices, and the data processed therein. Since IoT devices generate and exchange a massive amount of sensitive data over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network-based anomaly detection system can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false-negative rate in classifying network traffic. To this end, this thesis proposes an efficient novel Multistage Spectrogram image-based network anomaly detection system using a deep convolution neural network that utilizes a short-time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re-training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zeeshan, Ahmad
author_facet	Zeeshan, Ahmad
author_sort	Zeeshan, Ahmad
title	Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_short	Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_full	Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_fullStr	Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_full_unstemmed	Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_sort	spectrogram based anomaly detection scheme for internet-of-things using deep convolutional neural network
granting_institution	Faculty of Computer Science and Information Technology
granting_department	FCSIT
publishDate	2023
url	http://ir.unimas.my/id/eprint/41363/5/Thesis%20Form_Zeeshan%20Ahmad.pdf http://ir.unimas.my/id/eprint/41363/6/Zeeshan%20Ahmad%20ft.pdf
_version_	1804888420598677504
spelling	my-unimas-ir.413632024-06-21T08:53:50Z Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network 2023-02-22 Zeeshan, Ahmad QA75 Electronic computers. Computer science The revolutionary idea of the internet-of-things (IoT) architecture has gained enormous recognition over the last decade, resulting in exponential growth in the networks, connected devices, and the data processed therein. Since IoT devices generate and exchange a massive amount of sensitive data over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network-based anomaly detection system can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false-negative rate in classifying network traffic. To this end, this thesis proposes an efficient novel Multistage Spectrogram image-based network anomaly detection system using a deep convolution neural network that utilizes a short-time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re-training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%. UNIMAS 2023-02 Thesis http://ir.unimas.my/id/eprint/41363/ http://ir.unimas.my/id/eprint/41363/5/Thesis%20Form_Zeeshan%20Ahmad.pdf text en staffonly http://ir.unimas.my/id/eprint/41363/6/Zeeshan%20Ahmad%20ft.pdf text en validuser phd doctoral Faculty of Computer Science and Information Technology FCSIT UNIMAS Acar, G., Huang, D. Y., Li, F., Narayanan, A., & Feamster, N. (2018). Web-based attacks to discover and control local IoT devices. IoT S and P 2018 - Proceedings of the 2018 Workshop on IoT Security and Privacy, Part of SIGCOMM 2018, 29–35. https://doi.org/10.1145/3229565.3229568 Adat, V., & Gupta, B. B. (2018). Security in Internet of Things: issues, challenges, taxonomy, and architecture. Telecommunication Systems, 67(3), 423–441. https://doi.org/10.1007/S11235-017-0345-9/TABLES/3 Ahmad, F., Ahmad, Z., Kerrache, C. A., Kurugollu, F., Adnane, A., & Barka, E. (2019). Blockchain in internet-of-things: Architecture, applications and research directions. 2019 International Conference on Computer and Information Sciences, ICCIS 2019. https://doi.org/10.1109/ICCISCI.2019.8716450 Ahmad, Z., Khan, A. S., Nisar, K., Haider, I., Hassan, R., Haque, M. R., Tarmizi, S., & Rodrigues, J. J. P. C. (2021). Anomaly Detection Using Deep Neural Network for IoT Architecture. Applied Sciences 2021, Vol. 11, Page 7050, 11(15), 7050. https://doi.org/10.3390/APP11157050 Ahmad, Z., Khan, A. S., Shiang, C. W., Abdullah, J., & Ahmad, F. (2021). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32(1), e4150. https://doi.org/10.1002/ETT.4150 Ahmim, A., Derdour, M., & Ferrag, M. A. (2018). An intrusion detection system based on combining probability predictions of a tree of classifiers. International Journal of Communication Systems, 31(9), e3547. https://doi.org/10.1002/DAC.3547 Al-Sarawi, S., Anbar, M., Abdullah, R., & Al Hawari, A. B. (2020). Internet of things market analysis forecasts, 2020-2030. Proceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020, 449–453. https://doi.org/10.1109/WORLDS450073.2020.9210375 Al-Shareeda, M. A., Manickam, S., Laghari, S. A., & Jaisan, A. (2022). Replay-Attack Detection and Prevention Mechanism in Industry 4.0 Landscape for Secure SECS/GEM Communications. Sustainability 2022, Vol. 14, Page 15900, 14(23), 15900. https://doi.org/10.3390/SU142315900 Ali, M. H., Al Mohammed, B. A. D., Ismail, A., & Zolkipli, M. F. (2018). A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization. IEEE Access, 6, 20255–20261. https://doi.org/10.1109/ACCESS.2018.2820092 Alladi, T., Chamola, V., Sikdar, B., & Choo, K. K. R. (2020). Consumer IoT: Security Vulnerability Case Studies and Solutions. IEEE Consumer Electronics Magazine, 9(2), 17–25. https://doi.org/10.1109/MCE.2019.2953740 Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., & Razaque, A. (2020). Deep recurrent neural network for IoT intrusion detection system. Simulation Modelling Practice and Theory, 101, 102031. https://doi.org/10.1016/J.SIMPAT.2019.102031 Aloul, F., Al-Ali, A. R., Al-Dalky, R., Al-Mardini, M., & El-Hajj, W. (2012). Smart Grid Security: Threats, Vulnerabilities and Solutions. Energy Procedia, 1(1), 1–6. www.elsevier.com/locate/procedia Anderson, J. P. (1980). Computer security threat monitoring and surveillance. Technical Report, James P. Anderson Company. https://ci.nii.ac.jp/naid/10014688737 Arshad, J., Azad, M. A., Amad, R., Salah, K., Alazab, M., & Iqbal, R. (2020). A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT. Electronics 2020, Vol. 9, Page 629, 9(4), 629. https://doi.org/10.3390/ELECTRONICS9040629 Aversano, L., Bernardi, M. L., Cimitile, M., Pecori, R., & Veltri, L. (2021). Effective Anomaly Detection Using Deep Learning in IoT Systems. Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/9054336 Azab, A., & Khasawneh, M. (2020). MSIC: Malware Spectrogram Image Classification. IEEE Access, 8, 102007–102021. https://doi.org/10.1109/ACCESS.2020.2999320 Azlinah, M. W. B., Bee, M., & Yap, W. (2019). Supervised and Unsupervised Learning for Data Science Unsupervised and Semi-Supervised Learning Series Editor: M. Emre Celebi. Springer Nature. http://www.springer.com/series/15892 Bangyal, W. H., Ahmad, J., Rauf, H. T., & Shakir, R. (2018). Evolving artificial neural networks using opposition based particle swarm optimization neural network for data classification. 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2018. https://doi.org/10.1109/3ICT.2018.8855772 Ben Ida, I., Jemai, A., & Loukil, A. (2016). A survey on security of IoT in the context of eHealth and clouds. International Design and Test Workshop, 0, 25–30. https://doi.org/10.1109/IDT.2016.7843009 Benzarti, S., Triki, B., & Korbaa, O. (2018). A survey on attacks in Internet of Things based networks. Proceedings - 2017 International Conference on Engineering and MIS, ICEMIS 2017, 2018-January, 1–7. https://doi.org/10.1109/ICEMIS.2017.8273006 Bisong, E., & Bisong, E. (2019). Google Colaboratory. In Building Machine Learning and Deep Learning Models on Google Cloud Platform (pp. 59–64). Apress. https://doi.org/10.1007/978-1-4842-4470-8_7 Bovenzi, G., Aceto, G., Ciuonzo, D., Persico, V., & Pescape, A. (2020). A hierarchical hybrid intrusion detection approach in IoT scenarios. 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings, 2020-January. https://doi.org/10.1109/GLOBECOM42002.2020.9348167 Cekerevac, Z., Dvorak, Z., Prigoda, L., & Cekerevac, P. (2017). ] Internet of things and the Man-In-The-Middle attacks – Security and economic risks . MEST Journal , 5(2), 15–25. https://doi.org/10.12709/mest.05.05.02.03 Cervantes, C., Poplade, D., Nogueira, M., & Santos, A. (2015). Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things. Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, 606–611. https://doi.org/10.1109/INM.2015.7140344 Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019). Network Intrusion Detection for IoT Security Based on Learning Techniques. IEEE Communications Surveys and Tutorials, 21(3), 2671–2701. https://doi.org/10.1109/COMST.2019.2896380 Chen, K., Zhang, S., Li, Z., Zhang, Y., Deng, Q., Ray, S., & Jin, Y. (2018). Internet-of-Things Security and Vulnerabilities: Taxonomy, Challenges, and Practice. Journal of Hardware and Systems Security 2018 2:2, 2(2), 97–110. https://doi.org/10.1007/S41635-017-0029-7 Choudhary, S., & Kesswani, N. (2020). Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT. Procedia Computer Science, 167, 1561–1573. https://doi.org/10.1016/J.PROCS.2020.03.367 Corrales, D. C., Corrales, J. C., & Ledezma, A. (2018). How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning. Symmetry 2018, Vol. 10, Page 99, 10(4), 99. https://doi.org/10.3390/SYM10040099 da Costa, K. A. P., Papa, J. P., Lisboa, C. O., Munoz, R., & de Albuquerque, V. H. C. (2019). Internet of Things: A survey on machine learning-based intrusion detection approaches. Computer Networks, 151, 147–157. https://doi.org/10.1016/J.COMNET.2019.01.023 de Souza, C. A., Westphall, C. B., & Machado, R. B. (2022). Two-step ensemble approach for intrusion detection and identification in IoT and fog computing environments. Computers & Electrical Engineering, 98, 107694. https://doi.org/10.1016/J.COMPELECENG.2022.107694 Debar, H., Dacier, M., & Wespi, A. (1999). Towards a taxonomy of intrusion-detection systems. Computer Networks, 31(8), 805–822. https://doi.org/10.1016/S1389-1286(98)00017-6 Denning, D. E. (1987). An Intrusion-Detection Model. IEEE Transactions on Software Engineering, SE-13(2), 222–232. https://doi.org/10.1109/TSE.1987.232894 Es-Sabery, F., Hair, A., Qadir, J., Sainz-De-Abajo, B., Garcia-Zapirain, B., & Torre-DIez, I. (2021). Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier. IEEE Access, 9, 17943–17985. https://doi.org/10.1109/ACCESS.2021.3053917 Farahnakian, F., & Heikkonen, J. (2018). A deep auto-encoder based approach for intrusion detection system. International Conference on Advanced Communication Technology, ICACT, 2018-February, 178–183. https://doi.org/10.23919/ICACT.2018.8323688 Fatayer, T. S., & Azara, M. N. (2019). IoT secure communication using ANN classification algorithms. Proceedings - 2019 International Conference on Promising Electronic Technologies, ICPET 2019, 142–146. https://doi.org/10.1109/ICPET.2019.00033 Feng, Y., Wang, W., Weng, Y., & Zhang, H. (2017). A Replay-Attack Resistant Authentication Scheme for the Internet of Things. Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017, 1, 541–547. https://doi.org/10.1109/CSE-EUC.2017.101 Fernandes, E., Rahmati, A., Eykholt, K., & Prakash, A. (2017). Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges? IEEE Security and Privacy, 15(4), 79–84. https://doi.org/10.1109/MSP.2017.3151346 Ferrag, M. A., Maglaras, L., Ahmim, A., Derdour, M., & Janicke, H. (2020). RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks. Future Internet 2020, Vol. 12, Page 44, 12(3), 44. https://doi.org/10.3390/FI12030044 Frustaci, M., Pace, P., Aloi, G., & Fortino, G. (2018). Evaluating critical security issues of the IoT world: Present and future challenges. IEEE Internet of Things Journal, 5(4), 2483–2495. https://doi.org/10.1109/JIOT.2017.2767291 Fuentes, F., & Kar, D. C. (2005). Ethereal vs. Tcpdump: a comparative study on packet sniffing tools for educational purpose. Journal of Computing Sciences in Colleges, 20(4), 169–176. https://dl.acm.org/doi/abs/10.5555/1047846.1047873 Gao, X., Shan, C., Hu, C., Niu, Z., & Liu, Z. (2019). An Adaptive Ensemble Machine Learning Model for Intrusion Detection. IEEE Access, 7, 82512–82521. https://doi.org/10.1109/ACCESS.2019.2923640 García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1–2), 18–28. https://doi.org/10.1016/J.COSE.2008.08.003 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/ Gowthami, J., & Shanthi, N. (2018). Multi-factor based user authentication scheme for lightweight IoT devices. Proceedings of IEEE International Conference on Intelligent Computing and Communication for Smart World, I2C2SW 2018, 89–99. https://doi.org/10.1109/I2C2SW45816.2018.8997157 Goyal, P., & Goyal, A. (2018). Comparative study of two most popular packet sniffing tools-Tcpdump and Wireshark. Proceedings - 9th International Conference on Computational Intelligence and Communication Networks, CICN 2017, 2018-January, 77–81. https://doi.org/10.1109/CICN.2017.8319360 Gu, S., & Rigazio, L. (2014). Towards Deep Neural Network Architectures Robust to Adversarial Examples. 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings. http://arxiv.org/abs/1412.5068 Guo, C., Ping, Y., Liu, N., & Luo, S. S. (2016). A two-level hybrid approach for intrusion detection. Neurocomputing, 214, 391–400. https://doi.org/10.1016/J.NEUCOM.2016.06.021 Gupta, N., Naik, V., & Sengupta, S. (2017). A firewall for Internet of Things. 2017 9th International Conference on Communication Systems and Networks, COMSNETS 2017, 411–412. https://doi.org/10.1109/COMSNETS.2017.7945418 Hamza, A., Gharakheili, H. H., & Sivaraman, V. (2020). IoT Network Security: Requirements, Threats, and Countermeasures. https://arxiv.org/abs/2008.09339v1 Hanif, M., Ashraf, H., Jalil, Z., Jhanjhi, N. Z., Humayun, M., Saeed, S., & Almuhaideb, A. M. (2022). AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks. Electronics 2022, Vol. 11, Page 2324, 11(15), 2324. https://doi.org/10.3390/ELECTRONICS11152324 Hanif, S., Ilyas, T., & Zeeshan, M. (2019). Intrusion Detection in IoT Using Artificial Neural Networks on UNSW-15 Dataset. HONET-ICT 2019 - IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT, IoT and AI, 152–156. https://doi.org/10.1109/HONET.2019.8908122 Herrera, H. A., Rivas, W. R., & Kumar, S. (2018). Evaluation of internet connectivity under distributed denial of service attacks from botnets of varying magnitudes. Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018, 123–126. https://doi.org/10.1109/ICDIS.2018.00026 Hoque, N., Bhuyan, M. H., Baishya, R. C., Bhattacharyya, D. K., & Kalita, J. K. (2014). Network attacks: Taxonomy, tools and systems. In Journal of Network and Computer Applications (Vol. 40, Issue 1, pp. 307–324). Academic Press. https://doi.org/10.1016/j.jnca.2013.08.001 Huang, G. Bin, Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501. https://doi.org/10.1016/J.NEUCOM.2005.12.126 Huang, J., Chen, B., Yao, B., & He, W. (2019). ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network. IEEE Access, 7, 92871–92880. https://doi.org/10.1109/ACCESS.2019.2928017 Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys and Tutorials, 22(3), 1686–1721. https://doi.org/10.1109/COMST.2020.2986444 Islam, N., Farhin, F., Sultana, I., Kaiser, S., Rahman, S., Mahmud, M., Hosen, S., & Cho, G. H. (2021). Towards Machine Learning Based Intrusion Detection in IoT Networks. Computers, Materials and Continua, 69(2), 1801–1821. https://doi.org/10.32604/CMC.2021.018466 Javeed, D., Gao, T., Khan, M. T., & Ahmad, I. (2021). A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT). Sensors 2021, Vol. 21, Page 4884, 21(14), 4884. https://doi.org/10.3390/S21144884 Kamaldeep, Dutta, M., & Granjal, J. (2020). Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms. IEEE Access, 8, 127272–127312. https://doi.org/10.1109/ACCESS.2020.3005643 Karatas, G., Demir, O., & Sahingoz, O. K. (2020). Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset. IEEE Access, 8, 32150–32162. https://doi.org/10.1109/ACCESS.2020.2973219 Khan, F. A., Gumaei, A., Derhab, A., & Hussain, A. (2019). TSDL: A Two-Stage Deep Learning Model for Efficient Network Intrusion Detection. IEEE Access, 7, 30373–30385. https://doi.org/10.1109/ACCESS.2019.2899721 Khan, S., Rahmani, H., Shah, S. A. A., & Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision, 8(1), 1–207. https://doi.org/10.2200/s00822ed1v01y201712cov015 Khonji, M., Iraqi, Y., & Jones, A. (2013). Phishing Detection: A Literature Survey. IEEE Communications Surveys & Tutorials, 15(4), 2091–2121. Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., & Alazab, A. (2019). A Novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks. Electronics 2019, Vol. 8, Page 1210, 8(11), 1210. https://doi.org/10.3390/ELECTRONICS8111210 Kolias, C., Kambourakis, G., Stavrou, A., & Gritzalis, S. (2016). Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset. IEEE Communications Surveys and Tutorials, 18(1), 184–208. https://doi.org/10.1109/COMST.2015.2402161 Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019). Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Generation Computer Systems, 100, 779–796. https://doi.org/10.1016/J.FUTURE.2019.05.041 Kowsari, K., Sali, R., Ehsan, L., Adorno, W., Ali, A., Moore, S., Amadi, B., Kelly, P., Syed, S., & Brown, D. (2020). HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach. Information 2020, Vol. 11, Page 318, 11(6), 318. https://doi.org/10.3390/INFO11060318 Krishnan, S. (2021). Advanced analysis of biomedical signals. Biomedical Signal Analysis for Connected Healthcare, 157–222. https://doi.org/10.1016/B978-0-12-813086-5.00003-7 Kumar, V., Das, A. K., & Sinha, D. (2021). UIDS: a unified intrusion detection system for IoT environment. Evolutionary Intelligence, 14(1), 47–59. https://doi.org/10.1007/S12065-019-00291-W/TABLES/13 Kumari, R., Sheetanshu, Singh, M. K., Jha, R., & Singh, N. K. (2016). Anomaly detection in network traffic using K-mean clustering. 2016 3rd International Conference on Recent Advances in Information Technology, RAIT 2016, 387–393. https://doi.org/10.1109/RAIT.2016.7507933 Li, G., Niu, P., Duan, X., & Zhang, X. (2014). Fast learning network: A novel artificial neural network with a fast learning speed. Neural Computing and Applications, 24(7–8), 1683–1695. https://doi.org/10.1007/S00521-013-1398-7/TABLES/9 Li, J., Qu, Y., Chao, F., Shum, H. P. H., Ho, E. S. L., & Yang, L. (2019). Machine Learning Algorithms for Network Intrusion Detection. Intelligent Systems Reference Library, 151, 151–179. https://doi.org/10.1007/978-3-319-98842-9_6 Li, Z., Li, Y., & Xu, L. (2011). Anomaly intrusion detection method based on K-means clustering algorithm with particle swarm optimization. Proceedings - 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, ICM 2011, 2, 157–161. https://doi.org/10.1109/ICM.2011.184 Liang, C., Shanmugam, B., Azam, S., Jonkman, M., Boer, F. De, & Narayansamy, G. (2019). Intrusion Detection System for Internet of Things based on a Machine Learning approach. Proceedings - International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019. https://doi.org/10.1109/VITECON.2019.8899448 Liu, H., & Lang, B. (2019). Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Applied Sciences 2019, Vol. 9, Page 4396, 9(20), 4396. https://doi.org/10.3390/APP9204396 Liu, P. (2019). An intrusion detection system based on convolutional neural network. ACM International Conference Proceeding Series, 62–67. https://doi.org/10.1145/3313991.3314009 Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017). Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT. Sensors 2017, Vol. 17, Page 1967, 17(9), 1967. https://doi.org/10.3390/S17091967 Lu, N., Cheng, N., Zhang, N., Shen, X., & Mark, J. W. (2014). Connected vehicles: Solutions and challenges. IEEE Internet of Things Journal, 1(4), 289–299. https://doi.org/10.1109/JIOT.2014.2327587 Luo, E., Bhuiyan, M. Z. A., Wang, G., Rahman, M. A., Wu, J., & Atiquzzaman, M. (2018). PrivacyProtector: Privacy-Protected Patient Data Collection in IoT-Based Healthcare Systems. IEEE Communications Magazine, 56(2), 163–168. https://doi.org/10.1109/MCOM.2018.1700364 Ma, W. (2020). Analysis of anomaly detection method for Internet of things based on deep learning. Transactions on Emerging Telecommunications Technologies, 31(12), e3893. https://doi.org/10.1002/ETT.3893 Ma, Z., & Kaban, A. (2013). K-Nearest-Neighbours with a novel similarity measure for intrusion detection. 2013 13th UK Workshop on Computational Intelligence, UKCI 2013, 266–271. https://doi.org/10.1109/UKCI.2013.6651315 Mahdavifar, S., & Ghorbani, A. A. (2019). Application of deep learning to cybersecurity: A survey. Neurocomputing, 347, 149–176. https://doi.org/10.1016/J.NEUCOM.2019.02.056 Maheshwari, N., & Dagale, H. (2018). Secure communication and firewall architecture for IoT applications. 2018 10th International Conference on Communication Systems and Networks, COMSNETS 2018, 2018-January, 328–335. https://doi.org/10.1109/COMSNETS.2018.8328215 Mishra, P., Pilli, E. S., Varadharajan, V., & Tupakula, U. (2017). Intrusion detection techniques in cloud environment: A survey. Journal of Network and Computer Applications, 77, 18–47. https://doi.org/10.1016/J.JNCA.2016.10.015 Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). 2015 Military Communications and Information Systems Conference, MilCIS 2015 - Proceedings. https://doi.org/10.1109/MILCIS.2015.7348942 Mukkamala, S., Janoski, G., & Sung, A. (2002). Intrusion detection using neural networks and support vector machines. Proceedings of the International Joint Conference on Neural Networks, 2, 1702–1707. https://doi.org/10.1109/IJCNN.2002.1007774 Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data 2015 2:1, 2(1), 1–21. https://doi.org/10.1186/S40537-014-0007-7 Nawir, M., Amir, A., Yaakob, N., & Lynn, O. B. (2017). Internet of Things (IoT): Taxonomy of security attacks. 2016 3rd International Conference on Electronic Design, ICED 2016, 321–326. https://doi.org/10.1109/ICED.2016.7804660 Nguyen, X. H., Nguyen, X. D., Huynh, H. H., & Le, K. H. (2022). Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways. Sensors 2022, Vol. 22, Page 432, 22(2), 432. https://doi.org/10.3390/S22020432 Nimbalkar, P., & Kshirsagar, D. (2021). Feature selection for intrusion detection system in Internet-of-Things (IoT). ICT Express, 7(2), 177–181. https://doi.org/10.1016/J.ICTE.2021.04.012 Nirmal, K., Janet, B., & Kumar, R. (2020). Analyzing and eliminating phishing threats in IoT, network and other Web applications using iterative intersection. Peer-to-Peer Networking and Applications, 14(4), 2327–2339. https://doi.org/10.1007/S12083-020-00944-Z/FIGURES/7 Oppenheim, A. V., Willsky, A. S., & Young, I. T. (1983). Signals and systems (Second Edi). Prentice Hall, Upper Saddle River, New Jersey 07458. Otoum, Y., & Nayak, A. (2021). AS-IDS: Anomaly and Signature Based IDS for the Internet of Things. Journal of Network and Systems Management, 29(3), 1–26. https://doi.org/10.1007/S10922-021-09589-6/FIGURES/9 Ouyang, Z., Yu, H., Zhu, W. P., & Champagne, B. (2019). A Fully Convolutional Neural Network for Complex Spectrogram Processing in Speech Enhancement. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019-May, 5756–5760. https://doi.org/10.1109/ICASSP.2019.8683423 Panda, M., Mousa, A. A. A., & Hassanien, A. E. (2021). Developing an Efficient Feature Engineering and Machine Learning Model for Detecting IoT-Botnet Cyber Attacks. IEEE Access, 9, 91038–91052. https://doi.org/10.1109/ACCESS.2021.3092054 Panigrahi, R., & Borah, S. (2019). Dual-stage intrusion detection for class imbalance scenarios. Computer Fraud & Security, 2019(12), 12–19. https://doi.org/10.1016/S1361-3723(19)30128-9 Popoola, S. I., Adebisi, B., Hammoudeh, M., Gui, G., & Gacanin, H. (2021). Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks. IEEE Internet of Things Journal, 8(6), 4944–4956. https://doi.org/10.1109/JIOT.2020.3034156 Prasad, N. R., Almanza-Garcia, S., & Lu, T. T. (2009). Anomaly detection. ACM Computing Surveys (CSUR), 14(1), 1–22. https://doi.org/10.1145/1541880.1541882 Prasad, R., & Rohokale, V. (2020). Artificial Intelligence and Machine Learning in Cyber Security. 231–247. https://doi.org/10.1007/978-3-030-31703-4_16 Qureshi, A. ul H., Larijani, H., Ahmad, J., & Mtetwa, N. (2019). A Heuristic Intrusion Detection System for Internet-of-Things (IoT). Advances in Intelligent Systems and Computing, 997, 86–98. https://doi.org/10.1007/978-3-030-22871-2_7 Rahman, M. A., Asyhari, A. T., Wen, O. W., Ajra, H., Ahmed, Y., & Anwar, F. (2021). Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection. Multimedia Tools and Applications, 80(20), 31381–31399. https://doi.org/10.1007/S11042-021-10567-Y/TABLES/4 Roopak, M., Yun Tian, G., & Chambers, J. (2019). Deep learning models for cyber security in IoT networks. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019, 452–457. https://doi.org/10.1109/CCWC.2019.8666588 Saritas, M. M., & Yasar, A. (2019). Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification. International Journal of Intelligent Systems and Applications in Engineering, 7(2), 88–91. https://doi.org/10.18201//ijisae.2019252786 Satt, A., Rozenberg, S., & Hoory, R. (2017). Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms. https://doi.org/10.21437/Interspeech.2017-200 Shen, Y., Zheng, K., Wu, C., Zhang, M., Niu, X., & Yang, Y. (2018). An Ensemble Method based on Selection Using Bat Algorithm for Intrusion Detection. The Computer Journal, 61(4), 526–538. https://doi.org/10.1093/COMJNL/BXX101 Shone, N., Nguyen Ngoc, T., Dinh Phai, V., & Shi, Q. (2018). A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50. https://doi.org/10.1109/TETCI.2017.2772792 Shrivastava, R. K., Singh, S. P., Hasan, M. K., Gagandeep, Islam, S., Abdullah, S., & Aman, A. H. M. (2022). Securing Internet of Things devices against code tampering attacks using Return Oriented Programming. Computer Communications, 193, 38–46. https://doi.org/10.1016/J.COMCOM.2022.06.033 Singh, S., Sharma, P. K., Moon, S. Y., & Park, J. H. (2017). Advanced lightweight encryption algorithms for IoT devices: survey, challenges and solutions. Journal of Ambient Intelligence and Humanized Computing, 1–18. https://doi.org/10.1007/S12652-017-0494-4/TABLES/7 Stakhanova, N., Basu, S., & Wong, J. (2007). A taxonomy of intrusion response systems. International Journal of Information and Computer Security, 1(1–2), 169–184. https://doi.org/10.1504/IJICS.2007.012248 Susilo, B., & Sari, R. F. (2020). Intrusion Detection in IoT Networks Using Deep Learning Algorithm. Information 2020, Vol. 11, Page 279, 11(5), 279. https://doi.org/10.3390/INFO11050279 Thamilarasu, G., & Chawla, S. (2019). Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things. Sensors 2019, Vol. 19, Page 1977, 19(9), 1977. https://doi.org/10.3390/S19091977 Thomas, R., & Pavithran, D. (2019). A Survey of Intrusion Detection Models based on NSL-KDD Data Set. ITT 2018 - Information Technology Trends: Emerging Technologies for Artificial Intelligence, 286–291. https://doi.org/10.1109/CTIT.2018.8649498 Tiburski, R. T., Moratelli, C. R., Johann, S. F., Neves, M. V., Matos, E. De, Amaral, L. A., & Hessel, F. (2019). Lightweight security architecture based on embedded virtualization and trust mechanisms for IoT edge devices. IEEE Communications Magazine, 57(2), 67–73. https://doi.org/10.1109/MCOM.2018.1701047 Uddin, M., Rahman, A. A., Memon, J., Rehman, A. A., Uddin, N., Alsaqour, R., & Kazi, S. (2013). Signature-based Multi-Layer Distributed Intrusion Detection System using Mobile Agents . International Journal of Network Security, 15(1), 79–87. https://www.researchgate.net/publication/233915985 Ullah, I., & Mahmoud, Q. H. (2019). A Two-Level Hybrid Model for Anomalous Activity Detection in IoT Networks. 2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019. https://doi.org/10.1109/CCNC.2019.8651782 Ullah, I., & Mahmoud, Q. H. (2020a). A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks. Electronics 2020, Vol. 9, Page 530, 9(3), 530. https://doi.org/10.3390/ELECTRONICS9030530 Ullah, I., & Mahmoud, Q. H. (2020b). A Technique for Generating a Botnet Dataset for Anomalous Activity Detection in IoT Networks. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2020-October, 134–140. https://doi.org/10.1109/SMC42975.2020.9283220 Vasilomanolakis, E., Karuppayah, S., Muhlhauser, M., & Fischer, M. (2015). Taxonomy and Survey of Collaborative Intrusion Detection. ACM Computing Surveys (CSUR), 47(4). https://doi.org/10.1145/2716260 Verwoerd, T., & Hunt, R. (2002). Intrusion detection techniques and approaches. Computer Communications, 25(15), 1356–1365. https://doi.org/10.1016/S0140-3664(02)00037-3 Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access, 7, 41525–41550. https://doi.org/10.1109/ACCESS.2019.2895334 Wang, N., Jiao, L., Wang, P., Dabaghchian, M., & Zeng, K. (2018). Efficient Identity Spoofing Attack Detection for IoT in mm-Wave and Massive MIMO 5G Communication. 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings. https://doi.org/10.1109/GLOCOM.2018.8647707 Wei, P., Li, Y., Zhang, Z., Hu, T., Li, Z., & Liu, D. (2019). An optimization method for intrusion detection classification model based on deep belief network. IEEE Access, 7, 87593–87605. https://doi.org/10.1109/ACCESS.2019.2925828 Xiao, Y., Xing, C., Zhang, T., & Zhao, Z. (2019). An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks. IEEE Access, 7, 42210–42219. https://doi.org/10.1109/ACCESS.2019.2904620 Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access, 6, 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950 Xu, C., Shen, J., Du, X., & Zhang, F. (2018). An Intrusion Detection System Using a Deep Neural Network with Gated Recurrent Units. IEEE Access, 6, 48697–48707. https://doi.org/10.1109/ACCESS.2018.2867564 Yang, H., Qin, G., & Ye, L. (2019). Combined Wireless Network Intrusion Detection Model Based on Deep Learning. IEEE Access, 7, 82624–82632. https://doi.org/10.1109/ACCESS.2019.2923814 Yanyun, G., Fang, Z., Shaomeng, C., & Haiyong, L. (2017). A convolutional neural networks based transportation mode identification algorithm. 2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017, 2017-January, 1–7. https://doi.org/10.1109/IPIN.2017.8115930 Yao, H., Fu, D., Zhang, P., Li, M., & Liu, Y. (2019). MSML: A novel multilevel semi-supervised machine learning framework for intrusion detection system. IEEE Internet of Things Journal, 6(2), 1949–1959. https://doi.org/10.1109/JIOT.2018.2873125 Yavuz, F. Y., Ünal, D., & Gül, E. (2018). Deep Learning for Detection of Routing Attacks in the Internet of Things. International Journal of Computational Intelligence Systems, 12(1), 39–58. https://doi.org/10.2991/IJCIS.2018.25905181 Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks. IEEE Access, 5, 21954–21961. https://doi.org/10.1109/ACCESS.2017.2762418 Yuan, L., & Cao, J. (2018). Patients’ EEG data analysis via spectrogram image with a convolution neural network. Smart Innovation, Systems and Technologies, 72, 13–21. https://doi.org/10.1007/978-3-319-59421-7_2 Zaminkar, M., & Fotohi, R. (2020). SoS-RPL: Securing Internet of Things Against Sinkhole Attack Using RPL Protocol-Based Node Rating and Ranking Mechanism. Wireless Personal Communications, 114(2), 1287–1312. https://doi.org/10.1007/S11277-020-07421-Z/FIGURES/12 Zhang, J., Ling, Y., Fu, X., Yang, X., Xiong, G., & Zhang, R. (2020). Model of the intrusion detection system based on the integration of spatial-temporal features. Computers & Security, 89, 101681. https://doi.org/10.1016/J.COSE.2019.101681 Zhang, Yongguang, Lee, W., & Huang, Y. A. (2003). Intrusion Detection Techniques for Mobile Wireless Networks. Wireless Networks 2003 9:5, 9(5), 545–556. https://doi.org/10.1023/A:1024600519144 Zhang, Youqiang, Cao, G., Wang, B., & Li, X. (2019). A novel ensemble method for k-nearest neighbor. Pattern Recognition, 85, 13–25. https://doi.org/10.1016/J.PATCOG.2018.08.003 Zhu, Y., Yin, X., & Hu, J. (2018). Robust fingerprint matching based on convolutional neural networks. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 235, 56–65. https://doi.org/10.1007/978-3-319-90775-8_5 Zlomislić, V., Fertalj, K., & Sruk, V. (2017). Denial of service attacks, defences and research challenges. Cluster Computing, 20(1), 661–671. https://doi.org/10.1007/S10586-017-0730-X

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