Survey Perspective: The Role of Explainable AI in Threat Intelligence

The increasing reliance on AI-based security tools in Security Operations Centers (SOCs) has transformed threat detection and response, yet analysts frequently struggle with alert overload, false positives, and lack of contextual relevance. The inability to effectively analyze AI-generated security alerts lead to inefficiencies in incident response and reduces trust in automated decision-making. In this paper, we show results and analysis of our investigation of how SOC analysts navigate AI-based alerts, their challenges with current security tools, and how explainability (XAI) integrated into their security workflows has the potential to become an effective decision support. In this vein, we conducted an industry survey. Using the survey responses, we analyze how security analysts' process, retrieve, and prioritize alerts. Our findings indicate that most analysts have not yet adopted XAI-integrated tools, but they express high interest in attack attribution, confidence scores, and feature contribution explanations to improve interpretability, and triage efficiency. Based on our findings, we also propose practical design recommendations for XAI-enhanced security alert systems, enabling AI-based cybersecurity solutions to be more transparent, interpretable, and actionable.

Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading

Stock return forecasting is a major component of numerous finance applications. Predicted stock returns can be incorporated into portfolio trading algorithms to make informed buy or sell decisions which can optimize returns. In such portfolio trading applications, the predictive performance of a time series forecasting model is crucial. In this work, we propose the use of the Evolutionary eXploration of Augmenting Memory Models (EXAMM) algorithm to progressively evolve recurrent neural networks (RNNs) for stock return predictions. RNNs are evolved independently for each stocks and portfolio trading decisions are made based on the predicted stock returns. The portfolio used for testing consists of the 30 companies in the Dow-Jones Index (DJI) with each stock have the same weight. Results show that using these evolved RNNs and a simple daily long-short strategy can generate higher returns than both the DJI index and the S&P 500 Index for both 2022 (bear market) and 2023 (bull market).