This paper presents a comprehensive study on anomaly-driven security intelligence as a proactive approach to cyber threat mitigation, emphasizing the growing need for adaptive, intelligent systems capable of detecting emerging and unknown attacks in increasingly complex digital environments. Traditional signature-based detection methods fall short in addressing modern threats such as zero-day exploits and advanced persistent threats, prompting the integration of machine learning and deep learning techniques into cybersecurity frameworks. The research explores multiple anomaly detection models, including Isolation Forest, Autoencoders, LSTM networks, and an ensemble of Autoencoder-LSTM, applied to benchmark datasets. Results reveal that the ensemble model outperforms others in precision, recall, F1-score, and AUC-ROC, demonstrating its effectiveness in accurately identifying anomalies with reduced false positives. The study also discusses operational considerations, model interpretability, and limitations such as threshold tuning and adversarial robustness. By validating the utility of anomaly-based models in real-time detection systems, this paper supports the transition from reactive to proactive cybersecurity and sets the foundation for future work on explainable, resilient, and scalable threat detection frameworks.
Anomaly Detection, Cybersecurity, Machine Learning, Threat Mitigation, Security Intelligence.
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