Abstract
With the rapid development of the Internet, malware traffic is seriously endangering the security of cyberspace. Convolutional neural networks (CNNs)-based malware traffic classification can automatically learn features from raw traffic, avoiding the inaccuracy of hand-design traffic features. Through the experiments and comparisons of LeNet, AlexNet, VGGNet, and ResNet, it is found that LeNet has good and stable classification ability for malware traffic and normal traffic. Then, a field programmable gate array (FPGA) accelerator for CNNs-based malware traffic classification is designed, which consists of a parameterized hardware accelerator and a fully automatic software framework. By fully exploring the parallelism between CNN layers, parallel computation and pipeline optimization are used in the hardware design to achieve high performance. Simultaneously, runtime reconfigurability is implemented by using a global register list. By encapsulating the underlying driver, a three-layer software framework is provided for users to deploy their pre-trained models. Finally, a typical CNNs-based malware traffic classification model was selected to test and verify the hardware accelerator. The typical application system can classify each traffic image from the test dataset in 18.97 μs with the accuracy of 99.77%, and the throughput of the system is 411.83 Mbps.
Highlights
With the rapid development of computer network technology, the Internet is a part of people’s everyday lives, and it has been widely used in various fields such as the economy, military, education, and so on
Malware traffic classification in this paper mainly aims at the data traffic classification this paper mainly aims theclassic data part of each frame in network Malware traffic, so researched malware in traffic identification with atthe machine learning approach
Since our design fully explores the parallelism of Convolutional neural networks (CNNs) and the 8-bit int format is used for calculation, 205 DSPs are used
Summary
With the rapid development of computer network technology, the Internet is a part of people’s everyday lives, and it has been widely used in various fields such as the economy, military, education, and so on. Internet is a double-edged sword similar to some other technology: it promotes the rapid development of the social economy on one hand, but it brings unprecedented challenges on the other hand. Each one of these layers may be followed by an activation function for nonlinear transformation of the output. Rectified linear unit (RELU) is one of the most common used activation functions, and the computation of RELU can be formulated as shown in Equation (1)
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