arXiv:2606.05776v1 Announce Type: cross Abstract: With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification, dataset integration, and temporal feature learning to enhance detection performance in IoT networks. Using network traffic data, the proposed approach is evaluated on intrusion detection tasks and achieves an accuracy of approximately 97%. Experimental
The rapid proliferation of IoT devices and increasing cyber threats are driving urgent demand for more robust and autonomous security solutions.
Improved AI-driven intrusion detection for IoT networks is critical for maintaining the security and stability of a vast, interconnected digital infrastructure, preventing widespread disruptions and data breaches.
The ability to more effectively detect and mitigate intrusions in IoT environments through advanced AI allows for greater network resilience and potentially reduces human intervention in cybersecurity operations.
- · IoT device manufacturers
- · Cybersecurity firms
- · AI/ML developers
- · Critical infrastructure operators
- · Cybercriminals
- · Organizations with outdated security systems
Enhanced security for IoT deployments will lead to greater adoption of IoT in sensitive applications.
The automation of intrusion detection may shift cybersecurity resource allocation from reactive monitoring to proactive threat intelligence and system hardening.
As IoT security becomes more robust, the attack surface for nation-state actors may shift to other vectors or more sophisticated supply chain compromises.
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Read at arXiv cs.LG