arXiv AI

A Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting

The paper introduces a resilience recovery method for complex traffic networks that relies on trend forecasting. It develops a SIRD‑R fault propagation model incorporating risk values and constructs a resilience model combining real‑time and overall resilience. Using LSTM networks, the method forecasts network resilience and proposes a recovery strategy, with experimental results confirming its effectiveness and scalability across diverse traffic networks.

arXiv Machine Learning

Aug 7

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining

arXiv:2608. 05605v1 Announce Type: cross Abstract: Research and Education Networks (RENs) serve as critical infrastructure for scientific discovery, yet they face a unique security paradox: their normal traffic patterns which are characterized by massive, bursty "elephant flows" are statistically indistinguishable from volumetric attacks such as DDoS to conventional monitoring systems.

By Mohammad Arafath Uddin Shariff, Byrav Ramamurthy

agents benchmarks

arXiv AI

Jun 6

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

arXiv:2606. 05710v1 Announce Type: cross Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities.

By B. M. Taslimul Haque, Md. Arifur Rahman, Md. Serajul Kabir Chowdhury Rubel, Md. Iqbal Hossan

safety

arXiv Machine Learning

Jul 22

Adaptive Two-Stage Online Learning for Service-Affecting Failure Detection in Mobile Core Networks

arXiv:2607. 18522v1 Announce Type: new Abstract: Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure.

By J. du Toit, G. Fita, J. Salzwedel, A. Stoltz, R. Wolhuter

arXiv AI

Sep 10

Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection

The paper argues that adversarial robustness of anomaly detectors in industrial control systems (ICS) is a specific form of system resilience. It maps resilience concepts—disturbance class, absorption capacity, recovery trajectory, and degradation function—to adversarial machine learning, deriving a compositional resilience bound that identifies the coupling‑adjusted absorption capacity of nodes along an attack path as the key constraint. Empirical tests on the BATADAL water distribution benchmark reveal operationally significant effects, such as absorption‑degradation divergence under adversarial training and a paradox where hardening the most vulnerable node alone can reduce overall resilience.

By Branka Stojanovi\'c, Andreas Flatscher, Michael Somma

benchmarks safety

arXiv AI

Oct 15

CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection

arXiv:2610.14799v1 Announce Type: new Abstract: Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting model...

By Josef Koumar, Karel Hynek, Kamil Jerabek, Pavel Siska

benchmarks

arXiv Machine Learning

Jul 7

RES-DARE: Failure-Aware Expert Adaptation and Rollback-Safe Self-Repair for Intrusion Detection

arXiv:2607. 02687v1 Announce Type: cross Abstract: Intrusion detection systems are often trained under static benchmark conditions, although deployed network environments are affected by traffic drift, sensor noise, changing workloads, and evolving attack behaviour.

By Rahil Aftab, Anyash Prasad, Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta

benchmarks safety

arXiv Machine Learning

Jul 7

Deep Learning Network-Temporal Models For Traffic Prediction

arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.

By Yufeng Xin, Ethan Fan

llms fine-tuning

arXiv AI

Jun 9

A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems

arXiv:2606. 08849v1 Announce Type: new Abstract: Urban public transport disruptions require rapid response strategies, yet existing studies rarely provide a decision support framework to compare alternative disruption response solutions using a common set of dynamic, passenger, operator, and environment oriented indicators.

By Sara Jaber, S. M. Hassan Mahdavi, Neila Bhouri, Mostafa Ameli

agents multimodal

arXiv AI

Oct 5

Compound AI System Reliability: A Failure Taxonomy and Resilience Pattern Catalog from 150 Production Incidents

arXiv:2610.02503v1 Announce Type: cross Abstract: Deploying compound AI systems reliably and safely requires understanding failure modes that emerge at component boundaries, not within individual mod...

By Rudrendu Kumar Paul, Sourav Nandy

agents

arXiv Machine Learning

Jun 30

In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

arXiv:2606. 28625v1 Announce Type: cross Abstract: Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety.

By Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman, M Sabbir Salek, Mashrur Chowdhury

safety

arXiv Machine Learning

Jun 5

Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018

arXiv:2606. 05714v1 Announce Type: cross Abstract: Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing.

By Md. Iqbal Hossan, Md. Serajul Kabir Chowdhury Rubel, Md. Arifur Rahman, B. M. Taslimul Haque

arXiv AI

Jul 21

Reliable Remediation Impact Prediction for Black-Box Security Ratings

arXiv:2607. 16357v1 Announce Type: cross Abstract: Security rating platforms summarize externally observable cyber exposure and are expected to help organizations prioritize remediation.

By Nada Hanad, Mehdi Acheli, Ali NourEldin, Mohamed Sellami, Walid Gaaloul

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