KEYWORDS: Feature extraction, Data modeling, Machine learning, Deep learning, Performance modeling, Data transmission, Education and training, Detection and tracking algorithms, Network security, Matrices
Encrypted traffic classification can effectively supervise and manage the data traffic transmitted in the network. Most deep learning-based encrypted traffic classification models focus on modeling only one characteristic of the network, but the network traffic has both spatial and temporal characteristics, and part of the research adopts the recurrent neural network training method to grasp the temporal characteristics of the traffic, but there is a low efficiency problem when training the model on sequential data. The model is not efficient. In this paper, we propose ASTNet, an encrypted traffic classification model based on spatio-temporal features. Firstly, the session stream is cut according to the length of 784 bytes and then converted into a grayscale map as the input to the spatial feature extraction sub-module of the ASTNet model. Take out the first 8 data packets in the session stream, then intercept 256 bytes in each data packet, sort them according to the timestamp of the data packets, and use the processed time series as the input of the time feature extraction sub-module of the ASTNet model. Then the output features of the two feature modules are feature fused to obtain the spatiotemporal features of the encrypted traffic, and finally the final classification result is output by the classifier. We conducted on the public dataset ISCXVPN2016 (VPN-nonVPN dataset) experiments were conducted to compare the experimental results with the baseline method, and Experimental results show that our model achieves better results in encrypted traffic classification.
At present, artificial intelligence and big data are flourishing, driving wireless communication services to a more efficient and intelligent direction, communication devices are increasing dramatically, and the communication environment is becoming increasingly complex, so the decision-making link is crucial to ensure communication performance as much as possible. To address the problems that existing waveform parameter decision algorithms rely on high a priori knowledge, lack of compatibility, and low decision efficiency, a reinforcement learning-based waveform parameter decision method is proposed. The method introduces a dynamic ε mechanism based on the hill-climbing strategy (PHC) under the architecture of the reinforcement learning algorithm and proposes a dynamic ε Q-learning intelligent decision algorithm, which enables the decision model to select ε values more optimally according to the state of the decision network and improves the convergence speed and decision success rate. The algorithm makes full use of the interaction between reinforcement learning and the environment and generates waveform parameter combinations which are suitable for the current channel environment in real-time through online learning. The decision model is based on a multi-carrier spread spectrum (MC-SS) communication system. The simulation results show that the new decision algorithm does not rely on a priori knowledge and has higher decision efficiency, which not only gives suitable decision results in Gaussian channels but also adapts to various fading channels and outperforms the mapping results provided by the Modulation and Coding Scheme (MCS) index table.
KEYWORDS: Modulation, Time-frequency analysis, Interference (communication), Signal processing, Detection and tracking algorithms, Signal to noise ratio, Computer simulations, Monte Carlo methods, Feature extraction, Wireless communications
In the field of modern wireless communication, electromagnetic spectrum resources are increasingly tight, and it is becoming more and more common for one single antenna to receive multiple communication signals. High-order cumulant can suppress noise well, it has been widely used for modulation recognition of single signal in single-channel but seldom used for time-frequency aliasing signals in single-channel. This is because we need to use estimated carrier frequency for down-conversion to obtain the baseband signal before calculating high-order cumulant. But there will be more than one carrier frequencies when they are time-frequency aliasing signals. So it focuses to select the suitable carrier frequency to improve the calculation accuracy of high-order cumulant and recognition performance. In this paper, an algorithm of selecting suitable carrier frequency which uses the nature of high-order cumulant is proposed. And also two high-order features have been analyzed and constructed. Then they are used to classify seven kinds of timefrequency aliasing signals randomly combined by the digital modulated signals including BPSK, QPSK, 8PSK and 4ASK. The simulation results show that the algorithm has better recognition performance.
KEYWORDS: Mathematical modeling, Signal generators, Telecommunications, Independent component analysis, Interference (communication), Signal processing, Data conversion, Signal analyzers, Signal to noise ratio, Communication engineering
This paper designs a new communication jamming signal waveform generation model and the novel signal waveform separation method respectively based on blind source separation mathematical model and algorithm. The proposed signal waveforms have the characteristic of diversity which can ensure the security of communication. Simultaneously, the new form of jamming signal can be applied in the field of communication jamming to complete both deceptive jamming and repressive jamming. Simulation results of jamming on 2ASK, 4QAM and 16QAM communication systems show that the new jamming signal has larger jamming to signal ratio (JSR) range than the spread spectrum jamming signal and the monophonic jamming signal when effective interference is accomplish. This confirms its innovation and practicability in the field of communication jamming.
The quantity of radio communication service is growing up with the development of wireless communication technology. While limited spectrum allocation, inefficient utilization of frequency band and plenty of unused spectrum have led to spectrum scarcity. To solve this issue, we study dynamic spectrum access technique to maximize the utilization of multi-channel wireless network. we assume that N users sharing K channels, and the users can choose any channel to transmit. each user selects a channel and transmits a packet with a certain attempt probability. After each time slot, every user receives a binary observation which indicates the result of their transmission. We aim to find an optimal strategy for spectrum access, which maximizes channel utilization in a partial observation scheme without message exchange or online coordination between users. Because of the larger scale of state space and incomplete partial observation, acquiring the optimal solution is computationally expensive in general. To solve this issue, we develop a partial observation dynamic spectrum access algorithm based on deep reinforcement leaning. We take experiments to show the strong performance of the algorithm which can increase the channel utilization to 90% in partial observation scheme.
KEYWORDS: Modulation, Monte Carlo methods, Wavelet transforms, Detection and tracking algorithms, Computer simulations, Wavelets, Signal to noise ratio, Continuous wavelet transforms, Signal attenuation, Superposition
The research of the technology of modulation identification of digital communication signals is one of the key technologies of receivers in non-cooperative communication systems, and it also has important application value in civil and military fields. In the actual wireless communication environment, the phenomenon of multipath transmission exists all the time. However, most of the existing modulation identification methods are based on the ideal environment, which ignore the influence of multipath interference, and the performance of these algorithms is seriously degraded in multipath fading channels. In order to solve this problem, in this paper we proposed a new algorithm for modulation identification in multipath channels, which based on wavelet transform, higher order cyclic cumulant and high-order cumulant. The simulation results show that the algorithm proposed can effectively eliminate the influence of multipath channel and performs well for the identification of signal modulation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.