Reviewing the Landscape of Security Anomaly Detection through Deep Learning Techniques

Security anomaly detection, a critical element in safeguarding digital systems, has undergone a transformative evolution through the integration of deep learning techniques. This comprehensive review navigates the landscape of security anomaly detection, unveiling the potential and challenges within this realm. The research methodology involved systematic data collection from renowned databases, including Scopus, Web of Science, and Google Scholar. Key topics explored include the integration of deep learning models, benchmark datasets, preprocessing techniques, ethical considerations, and future directions. Deep learning models, such as autoencoders, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), have proven invaluable in enhancing detection accuracy and efficiency. Benchmark datasets like NSL-KDD, CICIDS2017, and UNSW-NB15 have emerged as essential evaluation tools. Tailored preprocessing techniques ensure data readiness for these models. Challenges encompass data imbalance, model interpretability, adversarial attacks, and scalability. Ethical and privacy considerations emphasize privacy preservation, fairness, transparency, and accountability. The convergence of deep learning with security anomaly detection heralds a new era in cybersecurity. While challenges persist, a commitment to ethical principles and exploration of innovative avenues are set to realize the full potential of deep learning for robust, efficient, and responsible security anomaly detection systems, ensuring a safer digital landscape for all.