arXiv:2606.20415v1 Announce Type: new Abstract: Deep Neural Networks DNNs have achieved remarkable accuracy in various tasks including their application in CyberPhysical Systems CPS for detecting False Data Injection Attacks FDIA during critical operations However the unique infrastructure of CPS makes DNNs vulnerable to exploitation by attackers aiming to evade detection Additionally the distinct nature of CPS presents challenges for conventional defense mechanisms against FDIA This paper proposes an innovative defense framework that strengthens DNNs against such attacks by introducing an add
The increasing reliance on AI for critical infrastructure management, particularly in power grids, necessitates robust defense mechanisms against sophisticated cyber threats.
Securing cyber-physical systems like power grids from AI-driven false data injection attacks is crucial for national security, economic stability, and public safety.
This research introduces a lightweight method to enhance the resilience of deep neural networks in critical infrastructure against cyberattacks, potentially improving security at reduced computational cost.
- · Cybersecurity firms
- · Power grid operators
- · National security agencies
- · AI defense researchers
- · Malicious state actors
- · Cybercriminals
- · Legacy cybersecurity solutions
Improved resilience of power grids against advanced cyberattacks, reducing the risk of blackouts or system failures.
Increased trust in AI-driven automation for critical infrastructure management, leading to wider adoption and greater efficiency.
Deterrence of sophisticated cyber warfare tactics targeting essential services, potentially shifting geopolitical cyber strategies.
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Read at arXiv cs.LG