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Intrusion detection is an important means to protect system security by
detecting intrusions or intrusion attempts on the system through operational
behaviors, security logs, and data audit. However, existing intrusion
detection systems suffer from incomplete data feature extraction and low
classification accuracy, which affects the intrusion detection effect. To
this end, this paper proposes an intrusion detection model that fuses
residual network(RESNET) and parallel cross-convolutional neural network,
called RESNETCCN. RESNETCNN can efficiently learn various data stream
features through the fusion of deep learning and convolutional neural
network (CNN), which improves the detection accuracy of abnormal data
streams in unbalanced data streams, moreover, the oversampling method into
the data preprocessing, to extract multiple types of unbalanced data stream
features at the same time, effectively solving the problems of incomplete
data feature extraction and low classification accuracy of unbalanced data
streams. Finally, three improved versions of RESNETCNN networks are
designed to meet the requirements of different traffic data processing, and
the highest detection accuracy reaches 99.98% on the CICIDS 2017 dataset
and 99.90% on the ISCXIDS 2012 dataset.
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