We highlighted a new contribution featured in the SMU Research White Book, addressing the challenge of detecting malicious activity in encrypted network traffic.
This research introduced a novel semi-supervised framework combining Self-Supervised Learning (SSL) with a Custom Autoencoder classifier to uncover hidden anomalies without relying on large labeled datasets.
The publication, titled “Self-Supervised Learning Meets Custom Autoencoder Classifier: A Semi-Supervised Approach for Encrypted Traffic Anomaly Detection”, reflected the continued engagement of our research community in advancing innovation in cybersecurity, machine learning, and AI-driven network security.
For full access : https://ieeexplore.ieee.org/document/11113262
#SMURESEARCHWHITEBOOK #CyberSecurity #MachineLearning #DeepLearning #NetworkSecurity #AIInnovation
Impact :
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Industry, Innovation and Infrastructure (SDG9). |
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