arXiv:2410.22967v5 Announce Type: replace Abstract: The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that can adapt to evolving traffic pattern is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. In this paper, we design Adaptive NAD, an online and self-Adaptive unsupervised Network Anomaly Detection framework for security domains. A two-layer anomaly detection strategy is proposed to g
The proliferation of IoT devices and increasing cyber threats necessitate more adaptive and real-time anomaly detection systems in network security.
Adaptive NAD introduces a self-adaptive, online unsupervised method that addresses a critical gap in current network anomaly detection, enhancing resilience against evolving cyberattacks.
Traditional offline anomaly detection systems will be incrementally replaced by more dynamic, online, and self-adaptive frameworks, improving internet security and infrastructure integrity.
- · Cybersecurity industry
- · IoT device manufacturers
- · Critical infrastructure operators
- · AI/ML security solution providers
- · Cybercriminals
- · Developers of static, offline anomaly detection systems
Improved detection and mitigation of cyber threats targeting IoT and network infrastructure.
Increased trust and security in smart systems and interconnected environments, leading to broader adoption of IoT.
A potential arms race in AI-driven cybersecurity, where defensive and offensive AI systems continually adapt against each other.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG