A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids
arXiv:2606.25200v1 Announce Type: new Abstract: The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels. Current IDS, however, exhibit three inherent limitations: inadequate modeling of the temporal progression of multi-step attacks, degraded scalability under extremely skewed class distributions of standard benchmark datasets, and restricted generalization acr
The accelerating digitalization of smart grids with IoT, AMI, and SCADA systems expands the attack surface, necessitating advanced cybersecurity solutions now to counter sophisticated cyber threats.
Cybersecurity for critical infrastructure like smart energy grids is paramount for national security and economic stability; advanced AI-driven detection systems are crucial to protect against disruptive attacks.
The proposed hybrid CNN-LSTM framework offers a more robust method for detecting multi-step and stealthy cyberattacks on smart grids, potentially improving resilience against sophisticated threats.
- · Cybersecurity firms specializing in AI/ML
- · Renewable energy grid operators
- · Critical infrastructure protection agencies
- · AI/ML researchers
- · Cyber attackers targeting energy grids
- · Legacy intrusion detection systems
- · Nations with vulnerable energy infrastructure
Improved detection of cyberattacks on smart energy grids leads to enhanced grid reliability and security.
Increased investment in AI-driven cybersecurity for critical infrastructure becomes a standard, driving innovation and market growth.
The development of more resilient energy grids reduces the geopolitical leverage of states that might consider energy infrastructure as a target in hybrid warfare scenarios.
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Read at arXiv cs.LG