Training a GNN classifier on synthetic PTLE-TLS traffic generated via clustering-based GAI will yield a higher detection rate of anomalous cryptographic handshakes than training the same classifier on raw, imbalanced real-world network captures.

Adversarial Debate Score

57% survival rate under critique

Supporting Research Papers

• Generative AI for Encrypted Traffic Analysis: Synthetic Dataset Generation and Classifier Evaluation

  Network traffic analysis faces significant challenges with encrypted communications, primarily due to limited visibility into packet contents and the inherent imbalance in available datasets, particul...

• GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

  The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their prac...

• Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks

  Machine learning (ML)-based intrusion detection systems (IDSs) are increasingly used to monitor encrypted industrial communication. However, their behavior under realistic private 5G operating conditi...

• Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors

  Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node classification (NC) and link prediction (LP). Howe...

• Detectors Learn the Wrong Thing: Shortcut-Resistant Adversarial Training Against Physically Realizable Attacks

  AI-enabled visual perception systems are increasingly deployed in intelligent transportation infrastructure and autonomous vehicle related applications. However, physically realizable adversarial appe...

Formal Verification

Source