The DOI matches its Crossref record; the references it cites are not checked yet.
Application of Large Language Models to DDoS Attack Detection
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
References 20 · Cited by 65
Research works printed but not linked: 22
- Abdullahi, M., et al.: Detecting cybersecurity attacks in internet of things using artificial intelligence methods: a systematic literature review. Electronics 11(2), 198 (2022) · DOI
- Antonakakis, M., et al.: Understanding the Mirai botnet. In: 26th USENIX security symposium (USENIX Security 17), pp. 1093–1110 (2017)
- Biswas, S.S.: Potential use of chat GPT in global warming. Ann. Biomed. Eng. 51(6), 1126–1127 (2023) · DOI
- Biswas, S.S.: Role of chat GPT in public health. Ann. Biomed. Eng. 51(5), 868–869 (2023) · DOI · Paper page
- Ferrag, M.A., Ndhlovu, M., Tihanyi, N., Cordeiro, L.C., Debbah, M., Lestable, T.: Revolutionizing cyber threat detection with large language models. arXiv preprint arXiv:2306.14263 (2023) · arXiv
- Hekmati, A., Grippo, E., Krishnamachari, B.: Dataset: Large-scale urban IoT activity data for DDOS attack emulation. arXiv preprint arXiv:2110.01842 (2021) · DOI
- Hekmati, A., Grippo, E., Krishnamachari, B.: Neural networks for DDOS attack detection using an enhanced urban IoT dataset. In: 2022 International Conference on Computer Communications and Networks (ICCCN), pp. 1–8. IEEE (2022) · DOI
- Hekmati, A., Jethwa, N., Grippo, E., Krishnamachari, B.: Correlation-aware neural networks for DDOS attack detection in IoT systems. arXiv preprint arXiv:2302.07982 (2023) · DOI
- Kurniabudi, Stiawan, D., Darmawijoyo, Bin Idris, M.Y., Bamhdi, A.M., Budiarto, R.: Cicids-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access 8, 132911–132921 (2020). https://doi.org/10.1109/ACCESS.2020.3009843 · DOI
- Liu, Y., et al.: Summary of ChatGPT/GPT-4 research and perspective towards the future of large language models. arXiv preprint arXiv:2304.01852 (2023) · arXiv
- Marin, G.: Network security basics. IEEE Secur. Priv. 3(6), 68–72 (2005). https://doi.org/10.1109/MSP.2005.153 · DOI
- Mubarakali, A., Srinivasan, K., Mukhalid, R., Jaganathan, S.C.B., Marina, N.: Security challenges in internet of things: Distributed denial of service attack detection using support vector machine-based expert systems. Comput. Intell. 36(4), 1580–1592 (2020) · DOI
- Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, classifiaction (1992) · DOI
- Pal, S., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. Neural Netw. 3(5), 683–697 (1992). https://doi.org/10.1109/72.159058 · DOI
- Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1, 108–116 (2018)
- Shaukat, K., Luo, S., Varadharajan, V., Hameed, I.A., Xu, M.: A survey on machine learning techniques for cyber security in the last decade. IEEE Access 8, 222310–222354 (2020). https://doi.org/10.1109/ACCESS.2020.3041951 · DOI
- Sinanović, H., Mrdovic, S.: Analysis of Mirai malicious software. In: 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–5 (2017). https://doi.org/10.23919/SOFTCOM.2017.8115504 · DOI
- Suresh, M., Anitha, R.: Evaluating machine learning algorithms for detecting DDoS attacks. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds.) CNSA 2011. CCIS, vol. 196, pp. 441–452. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22540-6_42 · DOI
- Tariq, U., Ahmed, I., Ali, K.B., Shaukat, K.: A critical cybersecurity analysis and future research directions for the internet of things: a comprehensive review. Sensors 23(8), 4117 (2023) · DOI
- Vishwakarma, R., Jain, A.K.: A survey of DDOS attacking techniques and defence mechanisms in the IoT network. Telecommun. Syst. 73(1), 3–25 (2020) · DOI
- Yu, D., et al.: Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500 (2021) · arXiv
- Zhao, W.X., et al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023) · arXiv
Metadata
- Authors
- Guastalla, M., Li, Y., Hekmati, A., & Krishnamachari, B.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Guastalla, Michael
- Venue
- Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 83-99
- ISSN
- 1867-8211 (ISSN-L)
- 1867-822X
- OpenAlex
- W4391519594
- Cited by
- 65
- References
- 20
- Citations by year
- 2026: 14 · 2025: 32 · 2024: 19
- FWCI
- 28.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- DDoS attack detection, large language models, ChatGPT, network security, fine-tuning, few-shot learning, CICIDS2017, intrusion detection, multilayer perceptron comparison, explainable reasoning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Application of Large Language Models to DDoS Attack Detection. https://app.cochat.ai/papers/10.1007/978-3-031-51630-6_6
- The paper
- Guastalla, M., Li, Y., Hekmati, A., & Krishnamachari, B. (2024). Application of Large Language Models to DDoS Attack Detection. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. https://doi.org/10.1007/978-3-031-51630-6_6