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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 120
Article Submitted : 24205
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Total Authors : 24597
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| Paper Title: | Beyond Static Thresholding: Causal Multi-Agent Decision Control for DDoS Detection |
| Authors Name: | G Dileep Kumar , Dr CV Guru Rao |
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| Author Reg. ID: |
IJRTI_212118
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| Published Paper Id: | IJRTI2604306 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | DDoS attacks have remained a threat to the availability of the contemporary networked systems because of their magnitude, diversity, and the changing attack tactics. Although deep learning-based traffic classifiers have a high detection accuracy, practical implementation is commonly based on predetermined decision thresholds, so they are not as adaptive to the dynamism of networks. The thesis in this paper is that a large classification separability does not always indicate that detection decisions can be controlled. We introduce a causal multi-agent reinforcement learning (MARL) model to detect DDoS, wherein the reason of several monitoring agents to detect DDoS is based on traffic observations and rationalize the selection of detection decisions with the aid of coordination of rewards. It is then trained using the CICDDoS2019 dataset to develop a strong baseline classifier and then performs a threshold sensitivity analysis, which shows the drawbacks of using a fixed thresholding, even when the AUC is almost perfect. The suggested MARL framework presents a formulation of rewards in which all agents are explicitly balanced with recall, false-positive cost, and causal agreement. Massive experiments show that MARL in a causal manner is much more robust to per-attack and more decision controllable than a directly threshold-based detection. The findings represent the significance of decision-level intelligence outside the traditional classification pipelines to viable DDoS defense. |
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| Cite Article: | "Beyond Static Thresholding: Causal Multi-Agent Decision Control for DDoS Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c277-c288, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604306.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604306
Registration ID:212118
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: c277-c288 Country: Medchal malkajgiri, Telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604306 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604306 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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