计算机科学
服务拒绝攻击
数据挖掘
水准点(测量)
特征(语言学)
计算
熵(时间箭头)
人工智能
机器学习
模式识别(心理学)
互联网
算法
万维网
语言学
哲学
物理
大地测量学
量子力学
地理
作者
Lixia Xie,Bangwei Yuan,Hongyu Yang,Ze Hu,Luhua Jiang,Liang Zhang,Xiang Cheng
标识
DOI:10.1016/j.csi.2023.103829
摘要
To address the slow response time of existing detection modules to the Internet of Things (IoT) Distributed Denial of Service (DDoS) attacks, along with their low feature differentiation and poor detection performance, we propose MRFM, a timely detection method with multidimensional reconstruction and function mapping. Firstly, we employ a queue mechanism to capture and store incoming network traffic data within a predefined time frame. Subsequently, we introduce a multidimensional reconstruction neural network model, specifically designed to reconstruct quantitative features based on their respective indices by adjusting the loss function. This process is followed by the computation of multidimensional reconstruction errors and the transformation of vectors into mapping features, thereby augmenting the disparities among various types of traffic data and promoting the similarity within the same category of traffic data. Lastly, we extract frequency information from the qualitative feature matrix using information entropy calculations, enriching the feature profile of individual traffic instances. The experimental results on two benchmark datasets show that MRFM can effectively detect different types of DDoS attacks. Notably, MRFM consistently outperforms other existing methods, exhibiting an average metric improvement of up to 9.61%.
科研通智能强力驱动
Strongly Powered by AbleSci AI