[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

IoT-based botnet attacks systematic mapping study of literature

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The rapid escalation in the usage of the Internet of Things (IoT) devices is threatened by botnets. The expected increase in botnet attacks has seen numerous botnet detection/mitigation proposals from academia and industry. This paper conducts a systematic mapping study of the literature so as to distinguish, sort, and synthesize research in this domain. The investigation is guided by various research questions that are relevant to the botnet studies. In this research, a total of 3,645 studies were gotten from our preliminary pursuit outcomes. Seventy four (74) studies were recognized based on importance, of which 52 were at last picked dependent on our characterized Incorporation and Elimination criteria. A classification for the mapping study with the following components: key contribution, research aspect, validation methods, network forensic methods, datasets and evaluation metric was proposed. Likewise, in this study, we identified eleven (11) key contributions which include evaluation, approach, model, system, software architecture, method, technique, framework, mechanism, algorithm and dataset. The findings of this systematic mapping investigation demonstrate that exploration of IoT-based botnet attacks is picking up more consideration in the past three years with steady distribution yield. Finally, this investigation can be a beginning point in examining researches on botnet assaults in IoT devices and finding better ways to detect and mitigate such assaults.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Acarali, D., Rajarajan, M., Komninos, N., & Zarpelão, B. B. (2019). Modelling the spread of botnet malware in IoT-based wireless sensor networks. Security and Communication Networks. https://doi.org/10.1155/2019/3745619.

  • Ahmad, I., Ahmed, G., Shah, S. A. A., & Ahmed, E. (2018). A decade of big data literature: Analysis of trends in light of bibliometrics. The Journal of Supercomputing, 76(5), 3555–3571.

    Article  Google Scholar 

  • Al, A., Hossam, S., & Ibrahim, F. (2019). Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection. Journal of Ambient Intelligence and Humanized Computing, 11(7), 2809–2825.

    Google Scholar 

  • Alshuqayran, N., Ali, N., & Evans, R. (2016). A systematic mapping study in microservice architecture. In 2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA) (pp. 44–51).

  • Amjad, K., Ahmad, R., Akhunzada, A., Hairul, M., Nasir, N., & Khan, S. U. (2015). Impact analysis and change propagation in service-oriented enterprises : A systematic review. Inf. Syst., 54, 43–73.

    Article  Google Scholar 

  • Araki, S., Takahashi, K., Hu, B., Kamiya, K., & Tanikawa, M. (2019). Subspace clustering for interpretable botnet traffic analysis. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC) (pp. 1–6). https://doi.org/10.1109/icc.2019.8761218.

  • Bahsi H., Nomm S., La Torre F. B. (2018) Dimensionality Reduction for Machine Learning Based IoT Botnet Detection. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, pp. 1857–1862.

  • Balasubramanian, Y., & Baggam, D. S. (2018). Quantum IDS for mitigation of DDoS attacks by mirai botnets. Singapore: Springer. https://doi.org/10.1007/978-981-10-8660-1.

    Book  Google Scholar 

  • Baldassarre, M. T., Caivano, D., Dimauro, G., Gentile, E., & Visaggio, G. (2018). Cloud computing for education: A systematic mapping study. IEEE Transactions on Education, 61(3), 234–244.

    Article  Google Scholar 

  • Bansal, A., & Mahapatra, S. (2017). A comparative analysis of machine learning techniques for botnet detection. Proceedings of the 10th International Conference on Security of Information and Networks, (pp. 91–100). https://doi.org/10.1145/3136825.3136874.

  • Bertino, E., & Islam, N. (2017). Botnets and Internet of Things Security. Computer, 50(2), 76–79. https://doi.org/10.1109/MC.2017.62.

    Google Scholar 

  • Budgen D., Turner M., Brereton P., Kitchenham B. (2007) Using Mapping Studies in Software Engineering. vol. 2.

  • Cayton-hodges, A. G. A., et al. (2018). (2018) Gameification in Education: A Systematic Mapping Study. Journal of Educational Technology & Society, 18(2), 3–20.

    Google Scholar 

  • Ceron, J. M., Steding-Jessen, K., Hoepers, C., Granville, L. Z., & Margi, C. B. (2019). Improving iot botnet investigation using an adaptive network layer. Sensors (Switzerland), 19(3), 1–16. https://doi.org/10.3390/s19030727.

    Article  Google Scholar 

  • Cui P., Guin U. (2019) Countering Botnet of Things using Blockchain-Based Authenticity Framework. In: 2019 IEEE Computer Society Annual Symposium on VLSI, pp. 598–603, 2019.

  • Dietz, C., Castro, R. L., Steinberger, J., Wilczak, C., Antzek, M., Sperotto, A., & Pras, A. (2018). IoT-Botnet betection and isolation by access routers. In Proceedings of the 2018 9th international conference on the network of the future, NOF 2018 (pp. 88–95). https://doi.org/10.1109/NOF.2018.8598138.

  • Doshi, R., Apthorpe, N., & Feamster, N. (2018). Machine learning DDoS detection for consumer internet of things devices. In Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018, (MI) (pp. 29–35). https://doi.org/10.1109/SPW.2018.00013.

  • Farooq, M. J., & Zhu, Q. (2019). Modeling, analysis, and mitigation of dynamic botnet formation in wireless IoT networks. IEEE Transactions on Information Forensics and Security, 14(9), 2412–2426. https://doi.org/10.1109/TIFS.2019.2898817.

    Article  Google Scholar 

  • Gardner M. T., Beard C., Medhi D. (2017) Using SEIRS epidemic models for IoT botnets attacks. In: DRCN 2017-Design of Reliable Communication Networks; 13th International Conference. VDE, vol. 2017, pp. 62–69, 2017.

  • Geneiatakis D., Kounelis I., Neisse R., Nai-fovino I., Steri G., Baldini G. (2017) Security and Privacy Issues for an IoT based Smart Home. In: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, pp. 1292–1297, 2017.

  • Giachoudis, N., Damiris, G.-P., Theodoridis, G., & Spathoulas, G. (2019). Collaborative agent-based detection of DDoS IoT botnets. In 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 205–211). https://doi.org/10.1109/dcoss.2019.00055.

  • Giri, R. (2019). Influence of selected factors in journals’ citations. Aslib Journal of Information Management , 71(1), 90–104.

    Article  Google Scholar 

  • Gopal, T. S., Meerolla, M., Jyostna, G., Reddy Lakshmi Eswari, P., & Magesh, E. (2018). Mitigating Mirai Malware Spreading in IoT Environment. In 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018 (pp. 2226–2230). https://doi.org/10.1109/ICACCI.2018.8554643.

  • Gurulakshmi, K., & Nesarani, A. (2018). Analysis of IoT Bots Against DDOS Attack Using Machine Learning Algorithm. In Proceedings of the 2nd International Conference on Trends in Electronics and Informatics, ICOEI 2018, (Icoei) (pp. 1052–1057). https://doi.org/10.1109/ICOEI.2018.8553896.

  • Habibi, J., Midi, D., Mudgerikar, A., & Bertino, E. (2017). Heimdall: Mitigating the internet of insecure things. IEEE Internet Things Journal, 4(4), 968–978.

    Article  Google Scholar 

  • Hachinyan, O., Khorina, A., & Zapechnikov, S. (2018). A game-theoretic technique for securing IoT devices against Mirai botnet. In Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2018, 2018-Janua (pp. 1500–1503). https://doi.org/10.1109/EIConRus.2018.8317382.

  • Hallman, R., Bryan, J., Palavicini, G., Divita, J., & Romero-mariona, J. (2017). IoDDoS- the internet of distributed denial of service attacks a case study of the mirai malware and IoT-based botnets, (IoTBDS) (pp. 978–989). https://doi.org/10.5220/0006246600470058.

  • Hasan, M., Islam, M., Zarif, I. I., & Hashem, M. M. A. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7, 100059.

    Article  Google Scholar 

  • Hatzivasilis, G., Soultatos, O., Chatziadam, P., Fysarakis, K., Askoxylakis, I., Ioannidis, S., Alaxandris, G., Katos, V., & Spanoudakis, G. (2019). WARDOG: Awareness detection watchbog for Botnet infection on the host device. IEEE Transactions on Sustainable Computing, 3782(c), 1–1. https://doi.org/10.1109/tsusc.2019.2914917.

    Google Scholar 

  • Indre, I., & Lemnaru, C. (2016). Detection and prevention system against cyber attacks and botnet malware for information systems and Internet of Things. In Proceedings - 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing, ICCP 2016 (pp. 175–182). https://doi.org/10.1109/ICCP.2016.7737142.

  • James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environment Evidence, 5(1), 1–13.

    Article  Google Scholar 

  • Jerkins J. A. (2017). Motivating a market or regulatory solution to IoT insecurity with the Mirai botnet code. In 2017 IEEE 7th annual computing and communication workshop and conference (CCWC) 2017 (pp. 1–5). https://doi.org/10.1109/CCWC.2017.7868464.

  • Ji, Y., Yao, L., Liu, S., Yao, H., Ye, Q., & Wang, R. (2018). The Study on the Botnet and its Prevention Policies in the Internet of Things. In Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018 (pp. 654–659). https://doi.org/10.1109/CSCWD.2018.8465280.

  • Jurca G., Hellmann T. D., Maurer F. (2014) Integrating agile and user-centered design: A systematic mapping and review of evaluation and validation studies of agile-UX. In: 2014 Agile Conference, pp. 24–32, 2014.

  • Kasurinen, J., & Knutas, A. (2018). Publication trends in gamification: A systematic mapping study. Computer Science Review, 27, 33–44.

    Article  Google Scholar 

  • Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology, 51(1), 7–15.

    Article  Google Scholar 

  • Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and other botnets. Computer, 50(7), 80–84. https://doi.org/10.1109/MC.2017.201.

    Google Scholar 

  • Koroniotis, N., Moustafa, N., & Sitnikova, E. (2018). Towards developing network forensic mechanism for botnet activities in the IoT based on machine learning techniques. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-90775-8.

    Book  Google Scholar 

  • Koroniotis, N., Moustafa, N., & Sitnikova, E. (2019b). Forensics and deep learning mechanisms for botnets in internet of things: A survey of challenges and solutions. IEEE Access, 7, 61764–61785.

    Article  Google Scholar 

  • Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019a). 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.

    Article  Google Scholar 

  • Kumar, A. (2019). A secure contained testbed for analyzing IoT botnets. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-12971-2.

    Book  Google Scholar 

  • Kumar A., & Lim T. J. (2019). EDIMA: Early detection of IoT malware network activity using machine learning techniques. In 2019 IEEE 5th World Forum Internet Things (WF-IoT) (pp. 289–294). https://doi.org/10.1109/wf-iot.2019.8767194.

  • Li, W., Jin, J., & Lee, J.-H. (2019). Analysis of Botnet Domain Names for IoT Cybersecurity. IEEE Access, 7, 94658–94665. https://doi.org/10.1109/access.2019.2927355.

    Article  Google Scholar 

  • Margolis J., Oh T. T., Jadhav S., Kim Y. H., Kim J. N. (2018) An in-depth analysis of the mirai botnet. In: 2017 International Conference on Software Security and Assurance (ICSSA), pp. 6–12.

  • Marzano A., Alexander, D., Fonseca, O., Fazzion, E., Hoepers, C., Steding-Jessen, K., Chaves, H. P. C. M., Cunha, I., Guedes, D., & Meira, W. (2018). The evolution of bashlite and mirai IoT botnets. In: Proceedings - IEEE Symposium on Computers and Communications, 2018-June (pp. 813–818). https://doi.org/10.1109/ISCC.2018.8538636.

  • McDermott, C. D., Isaacs, J. P., & Petrovski, A. V. (2019). Evaluating awareness and perception of botnet activity within consumer internet-of-things (IoT) networks. Informatics, 6(1), 8.

    Article  Google Scholar 

  • McDermott C. D., Majdani F., & Petrovski A. V. (2018a). Botnet Detection in the Internet of Things using Deep Learning Approaches. In 2018 international joint conference on neural networks (pp. 1–8).

  • McDermott C. D., Petrovski A. V., & Majdani F. (2018b). Towards situational awareness of botnet activity in the internet of things. In 2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018 (pp. 1–8). https://doi.org/10.1109/CyberSA.2018.8551408.

  • Medaglia, C. M., & Serbanati, A. (2010). An overview of privacy and security issues in the internet of things. In The Internet of Things. New York: Springer. https://doi.org/10.1007/978-1-4419-1674-7.

    Google Scholar 

  • Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018). N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3), 12–22. https://doi.org/10.1109/MPRV.2018.03367731.

    Article  Google Scholar 

  • Nguyen H. T., Ngo Q. D., & Le V. H. (2018). IoT botnet detection approach based on PSI graph and DGCNN classifier. In: 2018 IEEE International Conference on Information Communication and Signal Processing, ICICSP 2018, (Icsp) (pp. 118–122). https://doi.org/10.1109/ICICSP.2018.8549713.

  • Nomm S., Bahsi H. (2019) Unsupervised Anomaly Based Botnet Detection in IoT Networks. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE. pp. 1048–1053.

  • Oliveri, A., & Lauria, F. (2019). Sagishi: An undercover software agent for infiltrating IoT botnets. Network Security, 2019(1), 9–14.

    Article  Google Scholar 

  • Ozcelik M., Chalabianloo N., Gur G. (2017) Software-Defined Edge Defense Against IoT-Based DDoS. In: 2017 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2017. pp. 308–313.

  • Petersen K., Feldt R., MujtabaS., Mattsson M. (2008) Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering, pp. 1–10, 2008.

  • Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering : An update. Information and Software Technology, 64, 1–18.

    Article  Google Scholar 

  • Proano J. P. Z., Villamar V. C. P. (2018) Systematic mapping study of literature on educational data mining to determine factors that affect school performance. In: 2018 International Conference on Information Systems and Computer Science (INCISCOS). pp. 239–245.

  • Rasheed, R. A., Kamsin, A., Abdullah, N. A., Zakari, A., & Haruna, K. (2019). A systematic mapping study of the empirical MOOC literature. IEEE Access, 7, 124809–124827.

    Article  Google Scholar 

  • Rathore, S., & Park, J. H. (2018). Semi-supervised learning based distributed attack detection framework for IoT. Applied Soft Computing , 72, 79–89.

    Article  Google Scholar 

  • Remillano II A., (2019) ThinkPHP Vulnerability Abused by Botnets Hakai and Yowaie. [Online]. Available: https://blog.trendmicro.com/trendlabs-security-intelligence/thinkphp-vulnerability-abused-by-botnets-hakai-and-yowai/. [Accessed: 25-Jan-2019].

  • Sagirlar G., Carminati D., & Ferrari E. (2018). AutoBotCatcher: Blockchain-based P2P botnet detection for the internet of things. In Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018, (pp. 1–8). https://doi.org/10.1109/CIC.2018.00-46.

  • Sajjad S. M., & Yousaf M. (2018). UCAM: Usage, Communication and Access Monitoring Based Detection System for IoT Botnets. In Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 (pp. 1547–1550). https://doi.org/10.1109/TrustCom/BigDataSE.2018.00221.

  • Shafi Q. A., & Basit A. (2019). DDoS botnet prevention using blockchain in software defined internet of things. In Proceedings of 2019 16th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2019 (pp. 624–628). https://doi.org/10.1109/IBCAST.2019.8667147.

  • Shah, T., & Venkatesan, S. (2019). A Method to Secure IoT devices against botnet attacks. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-23357-0.

    Book  Google Scholar 

  • Shahrokni, A., & Feldt, R. (2013). A systematic review of software robustness. Information and Software Technology, 55(1), 1–17.

    Article  Google Scholar 

  • Sinanovic, H., & Mrdovic, S. (2017). Analysis of Mirai malicious software. In 25th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2017. https://doi.org/10.23919/SOFTCOM.2017.8115504.

  • Spaulding, J., Park, J., Kim, J., & Nyang, D. (2018). Thriving on chaos : Proactive detection of command and control domains in internet of things-scale botnets using DRIFT. Transactions on Emerging Telecommunications Technologies, 30(4), 1–17.

    Google Scholar 

  • Spaulding, J., Park, J., Kim, J., Nyang, D. H., & Mohaisen, A. (2019). Thriving on chaos: Proactive detection of command and control domains in internet of things-scale botnets using DRIFT. Transactions on Emerging Telecommunications Technologies, 30(4), 1–17. https://doi.org/10.1002/ett.3505.

    Article  Google Scholar 

  • Su, J., Danilo Vasconcellos, V., Prasad, S., Daniele, S., Feng, Y., & Sakurai, K. (2018). Lightweight classification of IoT malware based on image recognition. In 2018 IEEE 42Nd annual computer software and applications conference (Vol. 2, pp. 664–669). https://doi.org/10.1109/COMPSAC.2018.10315.

  • Sven, N., Benedetto, F., & Torre, L. (2018). Dimensionality reduction for machine learning based IoT botnet detection (pp. 1857–1862).

  • Tzagkarakis C., Petroulakis N., Ioannidis S. (2019) Botnet attack detection at the IoT edge based on sparse representation. In 2019 Global. IoT Summit (pp. 1–6).

  • Vlajic, N., & Zhou, D. (2018). IoT as a land of opportunity for DDoS hackers. Computer, 51(7), 26–34. https://doi.org/10.1109/MC.2018.3011046.

    Article  Google Scholar 

  • Wainwright P. Kettani H. (2019) An analysis of botnet models. Proceedings of the 2019 3rd International Conference on Compute and Data Analysis (pp. 116–121).

  • Wildani, I. M., & Yulita, I. N. (2019). Classifying botnet attack on internet of things device using random forest. In IOP Conference Series: Earth and Environmental Science (Vol. 248, pp. 6). https://doi.org/10.1088/1755-1315/248/1/012002.

  • Xu Y., Koide H., Vargas D. V., & Sakurai K. (2018). Tracing MIRAI malware in networked system. In Proceedings - 2018 6th International Symposium on Computing and Networking Workshops, CANDARW 2018 (pp. 534–538). https://doi.org/10.1109/CANDARW.2018.00104.

  • Yin, L., Luo, X., Zhu, C., Wang, L., Xu, Z., & Lu, H. (2019). ConnSpoiler: Disrupting C&C communication of IoT-based botnet through fast detection of anomalous domain queries. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2940742.

    Article  Google Scholar 

  • Zakari, A., Lee, S. P., Alam, K. A., & Ahmad, R. (2018). Software fault localization: A systematic mapping study. IET Software, 13(1), 2–22.

    Google Scholar 

  • Zein, S., Salleh, N., & Grundy, J. (2016). The journal of systems and software A systematic mapping study of mobile application testing techniques. Journal of Systems and Software, 117, 334–356.

    Article  Google Scholar 

Download references

Acknowledgement

This work is supported by Partnership Grant RK004-2017 and Faculty Grant GPF004D-2019 University of Malaya.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Habiba Hamid, Rafidah Md Noor, Ismail Ahmedy, Shaik Shabana Anjum, Fazidah Othman & Emran Mohd Tamil

  2. Faculty of Computer Science and Information Technology, Centre for Mobile Cloud Computing Research, University of Malaya, Kuala Lumpur, Malaysia

    Rafidah Md Noor & Shaik Shabana Anjum

  3. Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia

    Syaril Nizam Omar

  4. Department of Computer Science & Information Technology, University of Engineering & Technology, Peshawar, Pakistan

    Syed Adeel Ali Shah

  5. English Language Dept, Faculty of Languages and Linguistics, University of Malaya, Kuala Lumpur, Malaysia

    Sheena Kaur

Authors
  1. Habiba Hamid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafidah Md Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Syaril Nizam Omar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ismail Ahmedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shaik Shabana Anjum
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Syed Adeel Ali Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sheena Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fazidah Othman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Emran Mohd Tamil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Habiba Hamid or Rafidah Md Noor.

Appendix

Appendix

See Table 14.

Table 14 Selected primary studies
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hamid, H., Noor, R.M., Omar, S.N. et al. IoT-based botnet attacks systematic mapping study of literature. Scientometrics 126, 2759–2800 (2021). https://doi.org/10.1007/s11192-020-03819-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-020-03819-5

Keywords

Search

Navigation