Abstract The rapid evolution of cyber threats has exposed the limitations of traditional, reactive cybersecurity frameworks, necessitating data driven and adaptive defense mechanisms. This study investigates the dynamics between threat characteristics and system response latency through a comprehensive... The rapid evolution of cyber threats has exposed the limitations of traditional, reactive cybersecurity frameworks, necessitating data driven and adaptive defense mechanisms. This study investigates the dynamics between threat characteristics and system response latency through a comprehensive statistical and predictive analysis of 1,000 cybersecurity incident records. Employing chi-square tests, correlation analyses, effect size measurements, and Ridge regression modeling, the research evaluates how variables such as threat severity, access level, and detection status influence breach occurrence and response time. Results reveal that threat severity and privilege levels exhibit negligible associations with breach likelihood and response latency, challenging conventional risk prioritization assumptions. Instead, threat detection status emerges as the dominant predictor of response time (n²  0.76), indicating that current systems operate primarily on reactive, event triggered paradigms rather than proactive, severity aware mechanisms. Furthermore, the analysis identifies critical data quality challenges, including zero inflated response time distributions and timestamp induced feature leakage, which artificially inflate predictive metrics and compromise model generalizability. These findings underscore a significant gap between theoretical cybersecurity frameworks and practical system behavior. The study concludes that enhancing threatdetection accuracy, implementing rigorous data-preprocessing protocols, and integrating meaningful temporal and contextual features are essential for developing robust, proactive cybersecurity architectures. Future research should prioritize real world dataset validation and advanced machine learning techniques to overcome current analytical constraints. Read More Read Less

Abstract Traditional cyber risk assessment and intrusion detection systems frequently rely on mean based statistical summaries, which inadequately capture the extreme events and heavy tailed distributions inherent in network traffic. This study introduces a quantile based analytical framework... Traditional cyber risk assessment and intrusion detection systems frequently rely on mean based statistical summaries, which inadequately capture the extreme events and heavy tailed distributions inherent in network traffic. This study introduces a quantile based analytical framework to characterize network response time as a robust indicator of cyber threat severity. Utilizing a comprehensive real world dataset comprising over 211,000 network flows, we evaluate response time distributions across threat severity levels, detection statuses, attack categories, and temporal dynamics. The analysis reveals pronounced rightskewness, zero inflation, and substantial variance in response times, rendering average-based metrics insufficient. Quantile based comparisons demonstrate that high severity threats are distinguished not by typical interaction durations but by significantly elevated upper quantiles, reflecting prolonged, complex attack behaviors. Furthermore, detection systems exhibit higher efficacy against long duration sessions, highlighting potential blind spots for transient or stealthy attacks. Temporal analysis further confirms the bursty, clustered nature of malicious activity. By shifting focus from central tendency to distributional tail behavior, this quantile driven approach provides a more nuanced and accurate foundation for severity assessment and anomaly detection. The findings underscore the necessity of integrating robust statistical modeling with machine learning frameworks to enhance cyber defense capabilities. Future research will prioritize real time implementation, model interpretability, and validation across diverse operational networks to ensure scalable and practical deployment in dynamic threat landscapes. Read More Read Less

Abstract The proliferation of Android devices across diverse hardware architectures and software configurations introduces significant variability in cryptographic execution performance, directly impacting security readiness and protocol optimization. Despite extensive research on cloud security and malware detection, device-level computational heterogeneity... The proliferation of Android devices across diverse hardware architectures and software configurations introduces significant variability in cryptographic execution performance, directly impacting security readiness and protocol optimization. Despite extensive research on cloud security and malware detection, device-level computational heterogeneity remains underexplored. This study presents an unsupervised learning framework to characterize Android devices based on their intrinsic cryptographic benchmark profiles. Analyzing a curated dataset of 17 devices across 140 cryptographic timing features, we apply log transformation, standardization, and Principal Component Analysis (PCA), which captures 77.4% of variance in two dimensions. Three clustering algorithms K-means, hierarchical clustering, and DBSCAN are systematically evaluated. Results reveal a natural three-tier performance stratification, with K-means providing the most interpretable partition (Silhouette score: 0.325). Feature importance analysis demonstrates that public-key operations, particularly ECDSA and large-key RSA benchmarks, are the primary performance differentiators due to their computational intensity and sensitivity to hardware acceleration. Hierarchical analysis confirms this macro-structure and highlights gradual performance transitions among boundary devices, while DBSCAN effectively identifies tight local similarities. These findings establish a methodological foundation for performance-aware security assessment, enabling dynamic cryptographic parameter selection, optimized resource allocation, and targeted threat modeling. Furthermore, the framework is extended with proposed anomaly detection techniques for fine grained security monitoring. Ultimately, this research underscores the critical need for hardware aware intelligence in mobile cybersecurity, bridging device-level performance profiling with adaptive security analytics across heterogeneous Android ecosystems. Read More Read Less