Feature extraction is an important research topic in the field of image processing.In autonomous driving, it is of great importance to extract the feature information of the picture obtained by the vehicle camera for the agent to better understand the environment information. In order to improve the quality of feature extraction, this paper combines meta-learning and deep learning-based feature extraction methods, and proposes a Meta-VAE-WGAN-GP (MVWP) feature extraction algorithm, and applies it to automatic driving. Firstly, aiming at the problem of parameter centralization in Wasserstein generative adversarial network (WGAN) and the problem of gradient explosion and gradient disappearance caused by improper manual parameter adjustment, a generative adversarial network based on gradient penalty and Wasserstein distance (WGAN-GP) was proposed, and it was combined with VAE. The VAE-WGAN-GP model is formed. Secondly, aiming at the problem that the feature extraction model needs to be trained from scratch every time it is faced with a new task, and the training time is too long, the MVWP model is formed by combining meta-learning with VAE-WGAN-GP (VWP) mentioned above. Finally, the experimental results show that compared with VAE, VAE-WGAN and VWP, the training speed of MVWP model is increased by about 6 times, the reconstruction loss is reduced by 55.9%, 37.8% and 20.2%, respectively, and the reconstructed images are clearer.
With the constant updating of applications and the emergence of various encryption technologies, a large amount of new encrypted network traffic is generated every day. Therefore, it is a challenging task to achieve continual learning of encrypted traffic. Existing encrypted traffic classification techniques can only handle a fixed number of traffic classes, which is not applicable to real network environments. In this paper, we proposed a continual encrypted traffic classification method based on WGAN, called CETC. The method takes advantage of the powerful data generation capabilities of WGAN to model the data distribution of encrypted traffic. When learning from a new traffic class, the samples from the old class is generated by WGAN to train the new classifier. We use the ISCX VPN-nonVPN dataset to test the performance of CETC. Experimental results show that WGAN can generate high-quality samples of encrypted traffic and the accuracy of CETC is higher than 93%. With its efficient and continual learning capability, CETC can be applied to various encrypted traffic detection and management systems.
Despite the variety of cybercrimes, malicious Uniform Resource Locators (URLs) remain one of the most common threats to cybersecurity and bring huge economic losses every year. How to detect malicious URLs accurately has attracted great interests from both academia and industry. However, few focus on the multiclass malicious URL attack type detection and existing methods cannot provide robust performance due to the diversity of obfuscation strategies. In this paper, we propose a capsule-based deep neural network for malicious URL detection and classification, using character-level information from the URL string sequences. To be specific, our method transforms an input URL into character-level embedding representation firstly, then passes it into the designed convolution module to extract local features of different sizes and the local features are fed into the designed capsule module to retain the spatial hierarchical relationship of the URL string, extract accurate feature representation and output the accurate classification result finally. The experimental results on a public dataset constructed by four different classes of URLs show that compared with other baseline methods, our capsule-based method can achieve better detection and classification results, with F1-score of benign URL, malware URL, defacement URL and phishing URL at 98.94%, 95.81%, 99.63% and 94.04%, respectively. Due to the excellent performance of our capsule-based method for the detection of malicious URLs, it could be deployed in the main-stream web browsers to identify URL attack types and intercept malicious URLs effectively to protect vulnerable users against cyberattacks.
KEYWORDS: Convolution, Modeling, Feature extraction, Deep learning, Performance modeling, Machine learning, Design and modelling, Education and training, Data modeling, Data hiding
With the increasing awareness of privacy protection in recent years, various encryption techniques are gradually applied to network traffic, which makes encrypted traffic classification an indispensable part of network management. Recent studies show that the approaches based on deep learning are compelling for the traffic classification task. However, most of them take the encrypted payload as input, which not only requires high computational overhead to make classification, but also limits the performance improvement due to the unavailability of the plaintext. In this paper, we treat the encrypted traffic as sequences and solve the classification task from the perspective of sequence modeling, which only depends on several sequence fields obtained from the traffic header. We properly design a lightweight model and name it TGA by its structure, which consists of a temporal convolutional network (TCN), a gated recurrent unit (GRU) and the attention mechanism. TGA first extracts short-term features from sequences by applying the TCN, and then captures the long-term dependencies by exploiting the GRU, and finally focuses on valuable features through dynamic assignment of attention weights. Through these three steps, TGA is expected to obtain the most effective but lightest temporal features. Experimental results on the public dataset demonstrate that TGA shows superiority in terms of classification accuracy and time efficiency, while the number of parameters is reduced to at most 30% of the state-of-the-art models.
For the rail traffic, feasibility of 5th Generation (5G) mobile communication massive Multiple Input Multiple Output (MIMO) used in the tunnel is studying. In order to effectively set up base station, antenna and intelligent reflecting surface (IRS), the direction of arrival (DOA) must be captured. Aiming at the problem of poor performance of two-dimensional MUltiple SIgnal Classification (2D-MUSIC) algorithm in the environment of low signal-to-noise ratio (SNR), small snapshots and small incident angle interval signals, an improved MUSIC algorithm with uniform rectangular array (URA) based on reconstructive subspace is proposed. By reconstructing subspace, a new spatial spectrum is obtained in terms of subspace eigenvectors. Then, the DOAs are obtained by searching the maximum of the new spatial spectrum. High estimation accuracy and angular resolution are often required in practical applications of rail traffic 5G system, and there exist tunnel scenarios that we need the improved MUSIC algorithm has the ability to conduct two-dimensional DOA estimation. Simulations and the measured data of straight tunnel scenario are used to verify the effectiveness of the proposed algorithm and its higher searching precision in complex signal environments such as low SNR, strong-to-weak proximity, and coherent interference.
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