Harshith Guduru

Philip Huff, Dakota Dale, Harshith Guduru et al.

We address a fundamental challenge in cybersecurity operations of translating governance frameworks into actionable mitigation decisions under realistic resource constraints. Frameworks such as the NIST Cybersecurity Framework (CSF) provide widely adopted measures of organizational maturity, but do not directly support the selection and prioritization of defensive strategies against adversarial behavior. We present a system that operationalizes governance frameworks by mapping CSF maturity assessments into MITRE ATT\&CK mitigation capabilities, which enables direct integration of organizational security posture with adversary-informed defensive planning. To manage adversary complexity, we employ a Variable-Order Markov Model (VOMM) trained on observed ATT\&CK technique sequences to enable scalable adversary simulation within a Deep Reinforcement Learning (DRL) environment. We reconstruct likely attack paths and defensive responses using beam search, and then jointly optimize mitigation selection under explicit budget constraints. Our environment supports concurrent adversaries and realistic mitigation costs. Across multiple reward formulations and configurations, we show that the approach produces stable policies, meaningful cost-risk trade-offs, and interpretable mitigation plans aligned with organizational maturity. These results demonstrate that adversary-aware DRL can generate practical, resource-constrained defense strategies grounded in real-world frameworks and threat behavior.

Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski et al.

The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer classification across 31 different cancer types. The proposed approach combines differential gene expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features. The model architecture is based on the Kolmogorov-Arnold theorem principle and uses trainable univariate functions to enhance interpretability and feature analysis. MOGKAN achieves classification accuracy of 96.28 percent and exhibits low experimental variability in comparison to related deep learning-based models. The biomarkers identified by MOGKAN were validated as cancer-related markers through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates robust predictive performance and interpretability with potential to enhance the translation of complex multi-omics data into clinically actionable cancer diagnostics.