Anomaly Detection

For anomaly detection, we implemented an autoencoder, which used a neural network using TensorFlow and Keras. This encompassed comprehensive data, preprocessing, and normalization techniques on the NSL-KDD dataset. The autoencoder was exclusively trained on normal, non-anomalous network traffic, allowing it to effectively capture standard behavior patterns. Reconstruction errors were then calculated to identify anomalies, and optimal thresholds were determined through rigorous analysis using precision-recall and F1-score metrics. GPT-2 then imported this training to demonstrate Generative AI’s capability to produce synthetic cybersecurity logs. Tailored prompts mimicking cybersecurity incidents and log structures were crafted, resulting in synthetic logs that illustrate Generative AI’s potential application to real world examples of malicious activities.

Fig. 3. Autoencoder training and validation loss over 10 epochs, showing a steady decrease in mean squared error (MSE) for both datasets. The close alignment of the curves suggests the model is learning effectively without overfitting, consistently improving its ability to reconstruct normal input data.

Fig. 4. (Threat Detection and Anomaly Generation) Confusion matrix illustrating binary classification results for anomaly detection. The model correctly identified 106 normal instances and 7,702 anomalies, but also misclassified 17 normal samples as anomalies and 14,719 anomalies as normal.

The anomaly detection demonstration in Fig 3 and Fig 4 highlighted the effectiveness of autoencoders in recognizing anomalous network behavior. By exclusively training on normal traffic from the NSL-KDD dataset, the autoencoder successfully identified anomalies through reconstruction errors. Threshold optimization was critical in balancing false positives and negatives, thereby validating the practical defensive potential of Generative AI models. The synthetic cybersecurity logs generated by GPT-2 demonstrated a convincing capacity to mimic genuine cybersecurity threats and alerts. This capability emphasizes an emerging risk where malicious actors could utilize Generative AI technologies to automate cyberattacks, potentially complicating detection and response efforts.

Future research and practical improvements could focus on enhancing anomaly detection models by integrating Generative AI with traditional rule-based systems, thus improving accuracy and reducing false positives. Additionally, fine-tuning Generative AI models on cybersecurity-specific datasets would enable more realistic attack simulations, significantly benefiting penetration testing and defense readiness. Lastly, developing comprehensive ethical frameworks and governance structures is essential to prevent misuse and ensure responsible use of Generative AI in cybersecurity contexts.

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