Implementing an Advanced Hybrid Models for Network Intrusion Detection Systems Against Insider Threats

In the realm of cybersecurity, detecting insider threats presents a significant challenge due to their subtlety and complexity. This study employs advanced machine learning techniques, focusing on Long Short-Term Memory (LSTM) networks with attention mechanisms, alongside XGBoost and Transformer models, to enhance the detection of these threats. The LSTM models, particularly those utilizing attention mechanisms, demonstrated remarkable robustness by effectively prioritizing significant user behavior patterns, leading to high performance metrics. These findings underscore the effectiveness of LSTMbased attention mechanisms in capturing temporal dependencies and improving the model's sensitivity to anomalies, making them particularly suitable for identifying malicious behavior within networks. For future research, we recommend exploring hybrid models that integrate these attention-driven LSTM approaches to further enhance robustness and adaptability in evolving threat landscapes in intrusion detection systems.