International Journal of Science, Engineering and TechnologyAuthors: Sujeet Gautam
Abstract: Remote Monitoring and Management (RMM) tools have become an important part of modern enterprise IT infrastructure. These tools are widely used by Managed Service Providers (MSPs), system administrators, and IT support teams to remotely manage devices, monitor infrastructure health, deploy software updates, and troubleshoot technical issues. However, cybercriminals increasingly exploit legitimate RMM software to gain stealthy and persistent access to victim systems without using traditional malware. This report explores the growing threat of malicious RMM abuse, analyzes existing detection approaches, and proposes a hybrid detection framework capable of identifying suspicious RMM behavior through machine learning, behavioral analytics, and network fingerprinting techniques. The study also evaluates current challenges, discusses attacker evasion strategies, and provides recommendations for vendors and defenders to improve security posture. The research concludes that hybrid behavioral analysis combined with telemetry correlation significantly improves detection accuracy while reducing false positive rates compared to traditional signature-based detection systems.
DOI: https://doi.org/10.5281/zenodo.20137033
