ANALYSIS OF THE USE OF SOFTWARE HONEYPOTS IN INTERNET OF THINGS

DOI: 10.31673/2786-8362.2024.025763

Abstract

This article examines the use of software honeypots as an effective tool
for protecting information in the face of modern cyber threats. A detailed analysis of various types of lures,
their advantages and disadvantages, possible security threats, as well as aspects of the configuration and
overall efficiency of the systems was carried out. It is important to emphasize that even with the most
modern protection tools, it is impossible to ensure the absolute invulnerability of the company's data, since
attackers are constantly improving their attack methods. Software honeypots offer a variety of configuration
parameters − from simple software solutions to complex hardware complexes, which allows you to adapt
them to specific needs and protection goals.
Lures are classified into three main groups according to the level of interaction: low, medium and
high. In addition to practical applications, their role in cyber threat research plays an important role. The
problem of insufficient information about potential threats is extremely relevant for security professionals,
because there are often no clear answers to questions about the sources of attacks, their motives and
methods. Honeypots allow not only to detect and track attacks, but also to obtain important information
about the methods and tools used by attackers.
As technology advances and the number of connected devices grow, attacks are becoming more
sophisticated and sophisticated. In this context, software honeypots become especially relevant, helping to
reduce risks for real systems and develop more effective protection strategies. The use of honeypots is a
critical component of a cyber defense strategy, providing a deep understanding of new threats and methods
of their implementation. Honeypots provide critical data to improve security systems and prevent attacks.
However, for maximum effect, such systems must be carefully monitored and configured to avoid potential
risks and ensure their effectiveness in combating cyber threats. A balanced approach to the use of honeypots
allows you to increase the level of protection and ensure greater security of information systems.

Keywords: cyber defense, cyber threat, vulnerability, honeypot, IoT

List of used literature:
1. Khan, Z.A., Abbasi, U. (2020). Reputation Management Using Honeypots for Intrusion
Detection in the Internet of Things. Electronics, 9, 415. DOI:
http://dx.doi.org/10.3390/electronics9030415
2. M. Anwer, S. M. Khan, M. U. Farooq, W. Waseemullah, Attack Detection in IoT using
Machine Learning, Eng. Technol. Appl. Sci. Res., vol. 11, no. 3, pp. 7273–7278, Jun. 2021. DOI:
https://doi.org/10.48084/etasr.4202
3. Diandra Amiruddin Firmansyah, Amalia Zahra. (2023). Honeypot-Based Thread Detection
using Machine Learning Techniques, International Journal of Engineering Trends and Technology,
vol. 71, no. 8, pp. 243-252. Crossref, DOI: https://doi.org/10.14445/22315381/IJETT-V71I8P221
4. Opirskyy, I., Vasylyshyn, S., & Piskozub , A. (2020). Аналіз використання програмних
приманок як засобу забезпечення інформаційної безпеки. Електронне фахове наукове видання
«Кібербезпека: освіта, наука, техніка», 2(10), 88–97. DOI: https://doi.org/10.28925/2663-
4023.2020.10.8897
5. Mr. Kartik Chawda ,Mr. Ankit D. Patel. (2014). Dynamic & Hybrid Honeypot Model for
Scalable Network Monitoring. IEEE. URL: http://www.cse.umich.edu/techreports/cse/2004/CSETR-499-04.pdf
6. Lee S, Abdullah A, Jhanjhi N, Kok S. (2021). Classification of botnet attacks in IoT smart
factory using Honeypot combined with machine learning. PeerJ Computer Science, 7, 350 DOI:
https://doi.org/10.7717/peerj-cs.350
7. Rabhi, S., Abbes, T. & Zarai, F. (2023). IoT Routing Attacks Detection Using Machine
Learning Algorithms. Wireless Pers Commun 128, 1839–1857. DOI: https://doi.org/10.1007/s11277-
022-10022-7