Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks
arXiv:2606.31594v1 Announce Type: cross Abstract: The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to address issues related to their security and privacy. These devices are limited in resources, which complicates their security and the protection of the data that they manage. The paper aims to examine intrusion detection systems using the Gotham2025 dat
The proliferation of IoT devices and increasing cyber threats necessitates robust security solutions, making the effectiveness of AI-based intrusion detection a timely research area.
Securing the rapidly expanding IoT landscape is critical for protecting infrastructure, data privacy, and preventing large-scale system compromises.
This research provides a comparative analysis that could inform the development and deployment of more effective machine learning-based intrusion detection systems tailored for resource-constrained IoT environments.
- · Cybersecurity firms
- · IoT device manufacturers
- · Smart infrastructure operators
- · Cybercriminals
- · Organizations with vulnerable IoT deployments
- · Legacy security solution providers
Improved security postures for IoT deployments reduce the attack surface for bad actors.
Increased trust in IoT systems could accelerate their adoption across critical sectors, further integrating them into daily life.
As IoT security becomes more robust, the focus of cyber threats may shift to more sophisticated social engineering or supply chain attacks on the AI models themselves.
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Read at arXiv cs.AI