Abstract: Anomaly detection plays a critical role in modern cybersecurity systems due to the increasing scale, complexity, and temporal nature of network traffic. Traditional intrusion detection systems often generate isolated anomaly alerts without providing a higher-level interpretation of entity reliability. To address this limitation, this paper proposes TrustCast, a trust-aware deep learning framework that integrates temporal anomaly detection with dynamic trust com- putation. TrustCast employs data augmentation to address class imbalance, a GRU-based sequential autoencoder for time-series anomaly detection, and a trust computation module that converts anomaly evidence into dynamically evolving trust scores. Experimental results demonstrate that TrustCast outperforms baseline models in detection accuracy while providing interpretable trust trajectories suitable for proactive security decision-making.
Keywords: Anomaly Detection, Trust Computation, Deep Learning, Cybersecurity, Time-Series Analysis.
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DOI:
10.17148/IJARCCE.2026.15224
[1] Prof. Veena Amit Mali, Shravani Sanjay Tingare, Rajkunwar Amarsinh Mane, Yuvraj Mandendra Wankhede, Prajwal Damodhar Tade, Sanika Abhay Patil, "TrustCast: A Trust-Aware Deep Learning Framework for Time-Series Anomaly Detection in Cybersecurity," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15224
