Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018
arXiv:2606.05714v1 Announce Type: cross Abstract: Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing. The traditional cybersecurity approaches, including signature-based intrusion detection systems, have become less effective against today's cyber attacks, as they are unable to detect unknown and changing attacks in real time. To overcome these constraints, this research suggests a smart cyber-defense system, which
The rapid growth of digital infrastructure and sophisticated cyber threats necessitates immediate advancements in cybersecurity, pushing for AI-driven defense mechanisms.
This research highlights the critical need for advanced AI in protecting essential national infrastructure, impacting economic stability and national security.
Traditional cybersecurity methods are being replaced by AI-driven approaches capable of real-time detection and prevention of advanced cyber attacks.
- · AI cybersecurity firms
- · Critical infrastructure operators
- · National security agencies
- · Legacy cybersecurity providers
- · Cyber attackers
- · Organizations relying solely on traditional IDS
Widespread adoption of AI-powered intrusion detection and prevention systems.
Increased resilience of critical national infrastructure against state-sponsored and sophisticated cyber attacks.
Elevated strategic competition in developing and deploying offensive and defensive AI cyber capabilities among nations.
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