The relevance. With the rise of cyber threats targeting various industries, honeypot systems have become a crucial component in cybersecurity frameworks. Organizations store sensitive data that requires robust protection, and traditional security measures often fail to detect and prevent sophisticated attacks. Cybercriminals continuously develop new methods to exploit system vulnerabilities, making proactive threat detection an essential strategy. Honeypot technology provides a real-time approach to studying attack patterns, identifying weaknesses, and strengthening security protocols before real damage occurs.
The scientific Novelty. The novelty of this research lies in the enhancement of honeypot systems through advanced security integrations such as machine learning-based threat analysis and real-time intrusion detection. Unlike traditional honeypots that only log attack attempts, this study aims to create a dynamic honeypot capable of interacting with attackers, learning from their techniques, and adapting its defenses accordingly. Additionally, the project explores the role of honeypots in securing business investment platforms, where confidential financial data demands the highest level of protection.
The object of Research is honeypot technology as a cybersecurity measure for detecting and analyzing cyber threats.
The subject of Research is the development and enhancement of honeypot systems to improve their effectiveness in identifying and mitigating cyber attacks.
Practical Value is the development of an advanced honeypot model that can be integrated into various security infrastructures, including financial and business platforms. Honeypot systems can provide organizations with real-time insights into cyber threats, enabling them to develop better security measures and optimize incident response strategies. Moreover, this research contributes to the growing field of cyber threat intelligence, helping businesses stay ahead of emerging threats.
The main Goal is to design and develop a honeypot system that effectively enhances cybersecurity by analyzing hacker behaviors and attack methods.
To achieve this goal, several tasks must be completed:
Analyze the requirements for developing honeypot systems and define security standards for their implementation.
Compare existing honeypot solutions and determine the best methodologies for threat detection.
Create an experimental honeypot environment using suitable technologies and tools.
Develop a security model that incorporates machine learning for automated threat analysis.
Implement and test the honeypot system to ensure its effectiveness in detecting and logging cyber threats.
Evaluate the integration of honeypot technology into real-world security frameworks and assess its impact on cybersecurity.
Research Methods: observation, experimental testing, simulation, comparative analysis, standard examination, decomposition, and penetration testing.
The structure of this research includes an introduction, analytical part, theortical part, and a conclusion.
Honeypot systems have become more popular for creating proactive security systems in the late 1990s and early 2000s due to the growing level of threats, especially such as ransomware and attacks on IoT. The market for honeypot systems is actively growing and estimated by experts as one of the most dynamic in the cybersecurity sector. Key market trends include the integration of Honeypot with SIEM system to simplify monitoring of security events, and the use of analytics and machine learning to improve threat detection and analysis of malicious behaviour. Demand for Honeypot systems is growing steadily in sectors such as finance, healthcare, and industrial networks. These industries are more susceptible to sophisticated cyberattacks that can leak sensitive information or disrupt operations. In the financial sector, Honeypots help track attempts to steal financial data and hack payment systems.
In healthcare, they allow to protect electronic medical records and maintain the security of patients' personal data. SIEM (Security Information and Event Management) has become one of the central elements of the security infrastructure, allowing centrally manage events and analyse security incidents. Integration of honeypot systems with SIEM makes it easier to manage and analyse data received from the Honeypot. These systems not only capture events, but also provide advanced analytics capabilities, which improves incident response time and reduces the likelihood of missing critical events. The Honeypot Technology Market size was valued at approximately USD 0.3 billion in 2023 and is expected to reach USD 0.6 billion by 2032, growing at a compound annual growth rate (CAGR) of about 8% from 2023 to 2032.