International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning-Based DDoS Detection for Network Slicing in Next-Generation Networks |
|---|---|
| ABSTRACT | A key feature of 5G and upcoming 6G networks is network slicing, which allows several services with various performance needs to run on shared infrastructure. Nevertheless, it is susceptible to Distributed Denial of Service assaults, which can impair network availability and performance. The CIC-DDoS2019 dataset, which includes both normal and attack traffic records, is used in this investigation. A Random Forest classifier is used to precisely identify fraudulent traffic following data preparation, feature selection, and traffic analysis. A slice-aware Moving Target Defense mechanism finds impacted slices and uses mitigation techniques such traffic rerouting, slice isolation, IP shuffling, port modification, and dynamic resource allocation to improve network resilience and guarantee availability and service continuity. |
| AUTHOR | DESHAPATHI SATHWIK SHARMA, DR. M. DHANALAKSHMI Post Graduate Student, Department of Computer Science and Engineering, Computer Networks and Information Security, Jawaharlal Nehru Technological University, Hyderabad, India Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407028 |
| pdf/28_Machine Learning-Based DDoS Detection for Network Slicing in Next-Generation Networks.pdf | |
| KEYWORDS | |
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