The paper proposes using geometric metrics, specifically eigenspace alignment, to monitor the structural integrity of large behavioral populations, demonstrating its effectiveness in detecting network…

The paper proposes a novel adversarial defense approach, TopFeaRe, by modeling graph adversarial attacks using complex dynamic system theory to locate the graph's critical state of resilience.

The paper introduces the Hiremath Early Detection (HED) Score, a new measure-theoretic standard that accurately quantifies the time-value of early detection, significantly outperforming traditional me…

The paper analyzes low-degree estimation thresholds for recovering hidden signals in planted hypergraphs and tensor PCA, establishing sharp phase transitions and providing polynomial-time recovery alg…

The paper proposes a novel structural invariant approach, derived from the economic constraints of fraud, that amplifies weak, low-precision signals into highly accurate fraud detections without requi…

PARD-SSM is a probabilistic framework that models network traffic as a switching state-space system to detect multi-stage cyber-attacks in real-time with high accuracy and predictive capability.

This paper proposes and evaluates the KAN-LSTM model, demonstrating that Kolmogorov-Arnold Networks (KANs) significantly outperform traditional deep learning models for accurate and parameter-efficien…

The paper proposes a Doeblin-anchored contrastive chart to learn valid Markov transition kernels by combining the target transition with a restart law, ensuring the learned object is mathematically so…

The paper introduces and analyzes several novel data appraisal metrics, including the Vendi Score and matrix spectral functions, demonstrating that efficient optimization techniques make these metrics…

The paper proposes a dynamic queueing framework that estimates an organization's cyber resources and attack surface dynamics by analyzing the timestamps of vulnerabilities and fixes, achieving high ac…

The paper validates a specialized mathematical metric (the Burau-Lyapunov exponent) designed for detecting privilege escalation in cloud IAM graphs by applying it to an unrelated physical system: sola…

The paper introduces COPF, an online framework that ensures deployment-stable counterfactual fairness in link recommendation systems operating on evolving graphs by monitoring and controlling group di…

CALIBURN introduces a novel, five-component streaming pipeline for intrusion detection that allows operators to specify alerting behavior using cost and budget constraints, achieving state-of-the-art…

The paper proposes a semi-relaxed Gromov-Wasserstein objective to estimate the latent connectivity structure of large-scale networks, achieving statistically consistent and efficient recovery of the u…

The paper introduces ARCANE, a Bayesian network framework for cross-campaign cyber attribution, finding that while aggregating telemetry improves identification, structural feature limitations prevent…

This paper demonstrates that visual phishing detectors can be completely bypassed by employing simple timing-based attacks that delay the rendering of key webpage elements.

The paper develops a stochastic framework using Laplace-Carson transforms to model and quantify optimal proactive defense timing against a single cyberattack, providing closed-form solutions for defen…

The paper proposes a dynamic risk assessment framework that combines Bayesian Attack Graphs (BAGs) with process mining to continuously monitor system behavior and update the probability of active vuln…

The paper introduces a Jacobian-based spectral audit to evaluate neural operators, demonstrating that standard prediction error metrics fail to capture crucial local dynamical structures and operator…

The paper introduces BRIDGE, a standardized benchmark for cross-domain IoT botnet detection, and TCH-Net, a novel multi-branch network that achieves state-of-the-art generalization performance across…