DOI: https://doi.org/10.32515/2664-262X.2022.6(37).1.77-87

Analysis of Botnet Countermeasures in IoT Systems

Viktoria Germak, Roman Minailenko

About the Authors

Viktoria Germak, Lecturer, Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, ORCID ID: 0000-0001-8473-4156

Roman Minailenko, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, e-mail: aron70@ukr.net, ORCID ID: 0000-0002-3783-0476

Abstract

The article analyzes the methods of countering botnets in IoT systems. Today, the Internet of Things has become a popular term to describe scenarios in which Internet connectivity and computing power are spread across a multitude of objects, devices, sensors, etc. The main concept of IoT is the ability to connect all kinds of objects (things) that a person can use in everyday life. These objects (things) must be equipped with built-in sensors or sensors that have the ability to process information coming from the environment, exchange it and perform certain actions depending on the received information. The current lack of standards for the protection of such autonomous networks somewhat slows down the introduction of the Internet of Things into everyday life, so there are numerous vulnerabilities in the rapidly growing field of IoT technologies, which are used all over the world. Information protection and confidentiality is one of the priority components when choosing a certain system. Therefore, without adequate confidence in the security and privacy of user data, the IoT system will be uncompetitive. The Internet of Things can cause huge changes in everyday life, bringing a whole new level of comfort to ordinary users. But if the elements of such a system are not properly protected from unauthorized intervention, with the help of a reliable cryptographic algorithm, they will bring harm instead of good, giving cybercriminals a loophole to undermine information security. Since devices with built-in computers store a lot of information about their owner, including the ability to know their exact location, access to such information can help criminals commit a crime. To date, the level of success of botnet countermeasures depends mainly on organizational and political general conditions. Given that the establishment of cooperation or diplomatic agreements takes time, it can be concluded that the establishment of appropriate relations that legitimize cooperation for joint action is not suitable as an ad hoc scheme to combat current attacks. The situation is aggravated, given that modern botnet infrastructures are not under the responsibility of a single entity. In contrast, distributed peer-to-peer networks operate worldwide, so shutting down local parts (often no more than single computers) is not an effective solution. In general, countermeasures that require close cooperation are generally unfeasible today for both technical and political reasons Experts believe that prosecuting botnet developers is unlikely to have a strong impact on the global threat. Instead, botnets need to be fought on a technical level. Proactive measures should be taken by joint efforts of international security groups together with pro-government structures.

Keywords

user, IoT system, botnet, information protection

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References

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Citations

  1. Check Point Software Tech. LTD . Most Wanted Malware: Attacks Targeting IoT and Networking doubled since, May 2018. URL: https://blog.checkpoint.com/2018/08/15/julys-most-wanted-malware-attacks-targeting-iot-and-networking-doubled-since-may-2018/
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  3. Felix LEDER, Tillmann WERNER, and Peter MARTINI. Institute of Computer Science IV, University of Bonn, Germany . Proactive Botnet Countermeas- ures – An Offensive Approaches. URL: http://four.cs.uni-bonn.de/fileadmin/user_upload/leder/proactivebotnetcountermeasures.pdf
  4. Ivo van der Elzen Jeroen van Heugten . Techniques for detecting compro- mised IoT devices. URL: http://www.delaat.net/rp/2016-2017/p59/report.pdf
  5. Manos Antonakakis . Understanding the Mirai Botnet.
  6. Rohan Doshi, Noah Apthorpe, Nick Feamster . Machine Learning DDoS Detection for Consumer Internet of Things Devices.
  7. Sebastian-Dan Naste . A multidisciplinary study on DDoS attacks in the EU IoT ecosystem.
  8. OWASP–«IoT Vulnerabilities Project» URL: https://www.owasp.rg/index.php/ OWASP_Internet_of_Things_Project#tab=IoT_Vu lnerabilities (hast accessed: 22.10.2019).
  9. OWASP IoT. Attack Surface Project. URL: https://www.owasp.org/ index.php/OWASP_Internet_of_Things_Project#tab=IoT_Att ack_Surface_Areas (hast accessed: 22.10.2019)
  10. Daniel Elizalde .IoT Hardware – Introduction and Explanation. URL: https://www.iotforall.com/iot-hardware-introduction-explanation/ (hast accessed: 22.10.2019)
  11. Earlence Fernandes et al. FlowFence: Practical Data Protection for Emerging IoT Application Frameworks. URL: https://www.usenix.org/ system/files/conference/usenixsecurity16/sec16 _paper_ferna ndes.pdf (hast accessed: 22.10.2019)
  12. HESSELDAHL A. The Hacker’s Eye View of the Internet of Things. URL: http://recode.net/2015/04/07/a- hackers- eye- view- of- the- internet- of- things/ (hast accessed: 22.10.2019)
  13. FERNANDES, E., JUNG, J., AND PRAKASH, A. Security analysis of emerging smart home applications. На IEEE Symposium on Security and Privacy (S&P)
  14. Yi home camera. URL: https://www. yitechnology.com/yi- home- camera (hast accessed: 22.10.2019).
  15. Hewlett Packard Enterprise . Internet of things research study. URL: http:// h20195.www2.hpe.com/V4/getpdf.aspx/4aa5- 4759enw (hast accessed: 22.10.2019).
  16. Internet of things (iot) security and privacy recommendations.
  17. S. Hilton . Dyn analysis summary of friday october 21 attack. URL: https://dyn.com/blog/ dyn-analysis-summary-of-friday-october- 21- attack/ (hast accessed: 22.10.2019)
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  19. E. Eskin, W. Lee, and W. Stolfo . Modeling system call for intrusion detection using dynamic window sizes.
  20. Qin, M. and Hwang, K. 2004. Frequent episode rules for internet anomaly detection. In Proceedings of the 3rd IEEE International Symposium on Network Computing and Applications. IEEE Computer Society.
  21. M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A. Sadeghi, S. Tarkoma . Iot sentinel: Automated device-type identification for security enforcement in IoT. Computer Science. 2017. IEEE 37th International Conference on Distributed Computing Systems (ICDCS).
Copyright (c) 2022 Viktoria Germak, Roman Minailenko