arXiv:2304.13905v2 Announce Type: replace-cross Abstract: While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market. In this environment, IoT device identification methods provide a preventive security measure as an important factor in identifying these devices and detecting the vulnerabilities they suffer from. In this study, we present an end-to-end machine learning pipeline that identifies IoT devices in the Aalto university dataset (IoT devices captures) using Long Sh
The rapid proliferation of IoT devices continues to outpace robust security measures, making immediate advancements in identification and vulnerability detection critical.
This development represents a proactive step in securing the increasingly interconnected digital infrastructure, crucial for both national security and economic stability against cyber threats.
The ability to accurately and automatically identify IoT devices using advanced machine learning models enhances defensive capabilities against emerging security vulnerabilities in a scalable manner.
- · Cybersecurity industry
- · IoT device manufacturers (if they adopt standards)
- · Government intelligence agencies
- · Cybercriminals
- · Under-secured IoT ecosystems
- · Organizations with legacy security infrastructure
Improved network security through automated identification and anomaly detection of IoT devices.
Potential for new regulatory frameworks requiring advanced IoT device identification and security protocols.
Reduced attack surface for state-sponsored and criminal cyber operations, shifting their focus to more complex or human-centric vectors.
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Read at arXiv cs.LG