2021 · Journal of Hardware and Systems Security

The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment

Hossein Abbasi | Naser Ezzati-Jivan | Martine Bellaiche | Chamseddine Talhi | Michel R. Dagenais

Evidence basis: abstract-and-metadata-reviewed · Review status: catalog-reviewed; paper-author approval pending

anomaly-detection machine-learning resource-analysis performance-analysis

DDoS attacks cloud environment CUSUM bandwidth exhaustion application exhaustion connection exhaustion anomaly detection

Core contribution: The record identifies a cloud-DDoS anomaly-detection study that distinguishes multiple exhaustion-oriented attack types.

Catalog abstract summary

The accessible Springer preview associates the paper with a unified anomaly-detection approach for bandwidth-, application-, and connection-exhaustion DDoS groups, using CUSUM-style change detection and representative HTTP, database/application, and TCP SYN-flood attacks.

Source: Springer two-page preview, paraphrased; complete article not obtained.

Problem and motivation

Cloud DDoS attacks can consume different resource layers and require detection methods that distinguish their behavioral signatures.

Method and contribution

The two-page preview describes one inclusive detector that compares normal and potential-attack traffic/resource behavior across bandwidth, application, and connection exhaustion, with CUSUM for change detection. It mentions representative HTTP, database/application, and TCP SYN-flood classes, but does not expose the complete toolchain, feature set, dataset protocol, or baselines.

Findings and evidence

The preview supports the attack-taxonomy and unified-detection framing, but exposes no sample counts, train/test protocol, hardware, detection rate, false-positive value, or other quantitative result.

Limitations and future directions

Limitations: Only the official two-page Springer preview was accessible; the methods, experiments, references, limitations, and future-work section of the complete article remain unavailable.

Future work: The paper-specific future-work section remains unverified because the complete article was not accessible.

Sources and identifiers

When to cite this paper

Cite this paper when its specific method, evidence, or benchmark is directly relevant.

Citation

BibTeX
@article{ezzatiJivan2021theuse,
  author = {Hossein Abbasi and Naser Ezzati-Jivan and Martine Bellaiche and Chamseddine Talhi and Michel R. Dagenais},
  title = {The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment},
  year = {2021},
  journal = {Journal of Hardware and Systems Security},
  volume = {5},
  number = {3-4},
  pages = {208-222},
  publisher = {Springer Science and Business Media LLC},
  issn = {2509-3428, 2509-3436},
  doi = {10.1007/s41635-021-00119-z},
  url = {https://doi.org/10.1007/s41635-021-00119-z}
}
Other citation formats for Word and reference managers
APA 7
Abbasi, H., Ezzati-Jivan, N., Bellaiche, M., Talhi, C., & Dagenais, M. R. (2021). The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment. Journal of Hardware and Systems Security, 5(3-4), 208-222. https://doi.org/10.1007/s41635-021-00119-z
IEEE
H. Abbasi, N. Ezzati-Jivan, M. Bellaiche, C. Talhi, and M. R. Dagenais, "The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment," Journal of Hardware and Systems Security, vol. 5, no. 3-4, pp. 208-222, 2021, doi: 10.1007/s41635-021-00119-z

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