[
    {
        "id": "osp-16233",
        "type": "article-journal",
        "title": "Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies",
        "author": [
            {
                "family": "Hussain",
                "given": "Bilal"
            },
            {
                "family": "Tang",
                "given": "Xiao"
            },
            {
                "family": "Li",
                "given": "Tan"
            },
            {
                "family": "Azhar",
                "given": "Muhammad"
            },
            {
                "family": "Khan",
                "given": "Danista"
            },
            {
                "family": "Ahmad",
                "given": "Fawad"
            }
        ],
        "URL": "https://omanscience.com/en/articles/diffusion-based-stress-testing-of-overload-monitoring-for-resilient-emergency-cellular-networks-using-internet-cdr-proxies",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Disasters can overload cellular control-plane signaling within minutes, yet fine-grained Radio Resource Control (RRC) or Next Generation (NG) Application Protocol (NGAP) telemetry is privacy-sensitive and costly to collect for analytics. Many emergency monitoring pipelines therefore rely on coarse Call Detail Record (CDR) aggregates. We treat Internet activity in CDR grids as a practical proxy for hidden signaling stress under that constraint. We train a lightweight convolutional neural network (CNN) on stylized overload injections, stress-test it with diffusion-synthesized surges that preserve normal traffic structure, and adapt the detector by retraining on hard synthetic samples. Under stress-test conditions, the default alert threshold fails even though receiver operating characteristic (ROC) curves stay strong: the detector still assigns overloaded cells a larger overload probability than normal cells, but those probabilities fall below the default cutoff 0.5 and are labeled normal, so the F1-maximizing threshold -- selected post hoc on the same stress-test grids (oracle $τ^*$) -- shifts by $0.32 \\pm 0.03$ (operating-point drift). Across three random seeds, hard-sample adaptation raises thresholded performance (F1) from 0% (no alerts at the default cutoff 0.5 on any seed) to $85.67 \\pm 14.37$% and ranking from ROC-AUC $0.886 \\pm 0.040$ to $0.99996 \\pm 0.00007$. Diffusion-synthesized surges expose threshold fragility that matched-condition training -- training and testing on the same stylized injections -- hides, and hard-sample adaptation restores usable alerts at the default cutoff. Together, these steps define a reusable pre-deployment stress test for emergency monitors. Internet-only CDR input further supports lightweight AI-native workflows that combine monitoring, recalibration, and adaptation."
    }
]