Cybersecurity Threat Detection with AI: A Deep Learning Approach to Anomaly Detection
Abstract
As cyber threats grow more sophisticated, artificial intelligence is becoming a critical tool for detecting and mitigating security breaches. This paper presents a deep learning-based anomaly detection system for identifying malicious activities in networks and cloud environments. We develop a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to detect real-time cyber threats. Experimental evaluation on cybersecurity datasets demonstrates high accuracy in identifying zero-day attacks and insider threats. Challenges such as adversarial attacks, data imbalance, and explainability in AI-based security systems are explored.
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