{
  "abstract": "Distributed Denial of Service (DDoS) attacks can exhaust network resources within seconds, making early and accurate detection a critical requirement. Most existing systems are reactive—they trigger alerts only after a volumetric threshold is already breached. In this paper, we present a feasibility study of a two-component detection architecture, evaluating each component independently on the CICDDoS2019 benchmark. Pillar 1 is a kinematic Early Warning System (EWS) that models mean-centered aggregate traffic using a discrete-time relationship inspired by second-order Ordinary Differential Equations (ODEs), computed through strictly causal backward finite differences at 1-second intervals. Pillar 2 is a flow classifier that takes 58 Lasso-selected features, reshapes them into an 8x8 matrix, applies a deterministic 2D Haar wavelet transform, and classifies the resulting multiscale coefficients using an Extra Trees ensemble. On the evaluated capture, Pillar 1 flagged a pre-injection statistical anomaly 4,956 seconds before documented attack injection—an observation that demonstrates statistical deviation from the benign baseline rather than a validated prediction of an impending attack. Evaluated separately, Pillar 2 achieved 99.242–99.255% accuracy across four test arrangements from the same capture, retaining 99.959% accuracy against a 99.980% raw-feature baseline in a controlled ablation. We detail the mathematical formulation, sensitivity checks, and archival limitations of these experiments, and outline the deduplicated cross-dataset benchmarks and pipeline integration needed for operational validation.",
  "abstract_shown": true,
  "affiliations": [
    "VNR Vignana Jyothi Institute of Engineering and Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Harshith Reddy Madireddy"
    },
    {
      "affs": [
        0
      ],
      "name": "Shanmukhi Kurapati"
    },
    {
      "affs": [
        0
      ],
      "name": "Sai Harsha Vardhan K"
    },
    {
      "affs": [
        0
      ],
      "name": "Hussain M.K"
    },
    {
      "affs": [
        0
      ],
      "name": "RadhaKrishna V"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e0kfrqh8ag6f8.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23671072",
  "fulltext_url": null,
  "id": "e0kfrqh8ag6f8",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23671072",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23671072/files/Manuscript.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e0kfrqh8ag6f8.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:47Z",
      "native_id": "23671072",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23671072"
    }
  ],
  "subjects": [],
  "title": "A Dual-Pillar Framework for DDoS Detection: Kinematic Early Warning Signals and 2D Haar Wavelet Machine Learning",
  "title_text": "A Dual-Pillar Framework for DDoS Detection: Kinematic Early Warning Signals and 2D Haar Wavelet Machine Learning",
  "type": "preprint",
  "updated_at": "2026-11-09T12:20:47Z",
  "url": "https://research.draconitic.com/paper/e0kfrqh8ag6f8",
  "venue": "Zenodo",
  "versions": []
}