TY - GEN
T1 - Hybrid Intrusion Detection Method Based on K-Means and CNN for Smart Home
AU - Liu, Kaijian
AU - Fan, Zhen
AU - Liu, Meiqin
AU - Zhang, Senlin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/4/10
Y1 - 2019/4/10
N2 - This paper reviews the problem of instrusion detection for Smart Home and different approach to detect instrusion. A hybrid instrusion detection method based on Convolutional Neural Networks(CNN)and K-means is proposed in this paper. At smart home device node, K-means is used to generate the rule base by clustering, then Principal Component Analysis(PCA)is used to extract the dimensionality reduced features. During the test process, PCA is also used to extract the dimensionality reduced features, the feature matching is performed with the rule base to determine the intrusion data. At the smart home server side, a CNN model is proposed to detect the specific type of intrusion. Combined with Synthetic Minority Oversampling Technique(SMOTE)and undersampling techniques, the CNN model has great performance in reducing missing report rate(MRR)in minority categories. The results of the experiment conducted in KDD99 dataset show that such a hybrid method can improve the detection rate of smart home intrusion detection system and reduce MRR in minority categories.
AB - This paper reviews the problem of instrusion detection for Smart Home and different approach to detect instrusion. A hybrid instrusion detection method based on Convolutional Neural Networks(CNN)and K-means is proposed in this paper. At smart home device node, K-means is used to generate the rule base by clustering, then Principal Component Analysis(PCA)is used to extract the dimensionality reduced features. During the test process, PCA is also used to extract the dimensionality reduced features, the feature matching is performed with the rule base to determine the intrusion data. At the smart home server side, a CNN model is proposed to detect the specific type of intrusion. Combined with Synthetic Minority Oversampling Technique(SMOTE)and undersampling techniques, the CNN model has great performance in reducing missing report rate(MRR)in minority categories. The results of the experiment conducted in KDD99 dataset show that such a hybrid method can improve the detection rate of smart home intrusion detection system and reduce MRR in minority categories.
KW - Convolutional Neural Networks
KW - Instruction Detection
KW - K-means
KW - Smart Home
UR - https://www.scopus.com/pages/publications/85064965169
U2 - 10.1109/CYBER.2018.8688271
DO - 10.1109/CYBER.2018.8688271
M3 - 会议稿件
AN - SCOPUS:85064965169
T3 - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
SP - 312
EP - 317
BT - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
Y2 - 19 July 2018 through 23 July 2018
ER -