Toward Autonomous SOC Operations: End-To-End LLM Framework for Threat Detection, Query Generation, and Resolution in Security Operations

Security Operations Centers SOCs face mounting operational challenges. These challenges come from increasing threat volumes, heterogeneous SIEM platforms, and time-consuming manual triage workflows. We present an end-to-end threat management framework that integrates ensemble-based detection,...

Memory-Based Malware Detection under Limited Data Conditions: A Comparative Evaluation of TabPFN and Ensemble Models

Artificial intelligence and machine learning have significantly advanced malware research by enabling automated threat detection and behavior analysis. However, the availability of exploitable data is limited, due to the absence of large datasets with real-world data. Despite the progress of AI i...

A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...

Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning

The routing protocol for low-power and lossy networks RPL has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number...

A Comparative Analysis of Ensemble-Based Machine Learning Approaches with Explainable AI for Multi-Class Intrusion Detection in Drone Networks

The growing integration of drones into civilian, commercial, and defense sectors introduces significant cybersecurity concerns, particularly with the increased risk of network-based intrusions targeting drone communication protocols. Detecting and classifying these intrusions is inherently...

Smart Buildings Energy Consumption Forecasting Using Adaptive Evolutionary Ensemble Learning Models

Smart buildings are gaining popularity because they can enhance energy efficiency, lower costs, improve security, and provide a more comfortable and convenient environment for building occupants. A considerable portion of the global energy supply is consumed in the building sector and plays a...