TY - GEN
T1 - Research on Industrial Control Protocol Clustering Algorithm Based on Convolutional Self-Encoder and Improved K-means
AU - Jin, Xianji
AU - Jia, Yuge
AU - Li, Zhongwei
AU - Su, Changhe
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/10/24
Y1 - 2024/10/24
N2 - The increase in the number of private protocols used in industrial control systems brings challenges to network security maintenance work, among which, the classification and analysis of unknown protocols is a difficult point to overcome. In order to solve the classification problem of unknown protocols, an industrial control protocol clustering algorithm based on convolutional self-encoder and improved K-means is proposed. Firstly, a protocol feature extraction model based on the convolutional self-encoder model is designed, and the local relationship and spatial feature extraction of industrial control messages are realized by introducing the convolutional layer, pooling layer and activation function, and then an industrial control protocol clustering algorithm based on the improved K-means is used to complete the classification of the industrial control protocols, and finally, the method is verified by testing to achieve better results in the clustering of industrial control protocols.
AB - The increase in the number of private protocols used in industrial control systems brings challenges to network security maintenance work, among which, the classification and analysis of unknown protocols is a difficult point to overcome. In order to solve the classification problem of unknown protocols, an industrial control protocol clustering algorithm based on convolutional self-encoder and improved K-means is proposed. Firstly, a protocol feature extraction model based on the convolutional self-encoder model is designed, and the local relationship and spatial feature extraction of industrial control messages are realized by introducing the convolutional layer, pooling layer and activation function, and then an industrial control protocol clustering algorithm based on the improved K-means is used to complete the classification of the industrial control protocols, and finally, the method is verified by testing to achieve better results in the clustering of industrial control protocols.
KW - Convolutional Self-Encoder
KW - Feature extraction
KW - Improved K-means
KW - Protocol clustering
UR - https://www.scopus.com/pages/publications/85212584538
U2 - 10.1145/3690407.3690516
DO - 10.1145/3690407.3690516
M3 - 会议稿件
AN - SCOPUS:85212584538
T3 - ACM International Conference Proceeding Series
SP - 642
EP - 646
BT - Proceedings of 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024
PB - Association for Computing Machinery
T2 - 4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024
Y2 - 21 June 2024 through 23 June 2024
ER -