| Title | Analysis of the Optimized KNN Algorithm for the Data Security of DR Service |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Shi, Kun, Chen, Songsong, Li, Dezhi, Tian, Ke, Feng, Meiling |
| Conference Name | 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) |
| Keywords | cloud computing, Collaboration, composability, compositionality, Costs, demand response, denial-of-service attack, Human Behavior, human factors, Information security, Internet-scale Computing Security, IoT device, KNN, Memory, Metrics, policy-based governance, pubcrawl, resilience, Resiliency, Scalability, smart energy, supervised learning, supply and demand, system integration |
| Abstract | The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices. |
| DOI | 10.1109/EI256261.2022.10116197 |
| Citation Key | shi_analysis_2022 |
Analysis of the Optimized KNN Algorithm for the Data Security of DR Service