AI-BASED INTRUSION DETECTION SYSTEMS FOR EDGE COMPUTING ENVIRONMENTS
Keywords:
Edge Computing, Intrusion Detection Systems (IDS), Artificial Intelligence (AI), Machine Learning, CybersecurityAbstract
With the exponential growth of Internet of Things (IoT) devices and the proliferation of edge computing architectures, ensuring robust security has become paramount. Traditional centralized intrusion detection systems (IDS) often fall short in addressing the dynamic and distributed nature of edge environments, leading to increased latency and vulnerability to sophisticated cyber-attacks. This paper explores the integration of artificial intelligence (AI) techniques into intrusion detection systems tailored for edge computing environments. By leveraging machine learning algorithms, deep learning models, and anomaly detection mechanisms, AI-based IDS can efficiently analyze vast amounts of data in real-time, identifying and mitigating potential threats with higher accuracy and lower false-positive rates. Additionally, the decentralized nature of edge computing benefits from AI’s ability to adapt and learn from local data, enhancing the scalability and resilience of security frameworks. We discuss the latest advancements, challenges such as resource constraints and data privacy, and propose future directions for optimizing AI- driven security solutions in edge-centric architectures. The findings underscore the critical role of AI in fortifying edge computing infrastructures against evolving cyber threats, ensuring secure and reliable operations in increasingly connected environments.
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