Abstract
Abnormal traffic and vulnerability attack monitoring play an important role in today’s Internet of Things (IoT) applications. The existing solutions are usually based on machine learning for traffic, and its disadvantage is that a large number of manual operations are needed in the classification process, and the adaptability is poor. Moreover, for unknown attacks, the system cannot make a relative response in time. In this work, we design a monitoring system of IoT based on C5.0 decision tree and time-series analysis. The system transforms time-series into GAF graph, and uses CNN-LSTM combination model to monitor the traffic. The time-series model based on deep learning can also improve the inefficiency and manual intervention caused by data analysis. At the same time, the system introduces LSTM technology, which can solve a series of problems that may be caused during long sequence training. We select KDD Cup 99 data set for simulation experiments and comparison with traditional traffic monitoring methods. The results show that the average error rate of abnormal traffic attack types is 3.22%. The evaluations show that the system can effectively monitor unknown attacks with 96% accuracy. We further use whitelist matching technology to identify IoT device models. After comparison of experiments, it is proved that this method has its superiority in the monitoring of IoT devices.
Highlights
Driven by the rapid development of big data, artificial intelligence and information communication technology, the scale of the Internet of things is growing rapidly
Doshi R et al carry out a distributed denial of service (DDoS) attack on the Internet infrastructure to avoid a series of security risks that may be caused by its connection with insecure IoT devices
We introduce a monitoring system for IoT devices based on decision tree C5.0 and timing analysis
Summary
Driven by the rapid development of big data, artificial intelligence and information communication technology, the scale of the Internet of things is growing rapidly. The key features obtained are combined with the selected features in the decision tree training process to form a new feature set, which is input into LSTM model, and the data monitoring information and abnormal trend are obtained. Use visualization technology to convert time series data into GAF graphs, input them as feature information into the CNN model, combine the key features obtained with the selected features in the decision tree training process to form a new feature set, and input the long and short-term memory network (LSTM) model to obtain data monitoring information and abnormal trends. After preprocessing the obtained data, it is input into the training set of decision tree C5.0 model, which is used as the feature 15 classification module to classify the data information This provides a good data base for the subsequent time-series model to monitor the traffic information of the Internet of things. The experimental results show that the overall detection rate and accuracy of the data samples are improved
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