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- 저자 : Yongxin Feng (검색결과 5 건)
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FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection
( Yongxin Feng ),( Yingyun Kang ),( Hao Zhang ),( Wenbo Zhang ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.1
Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the de-tection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be signifi-cantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.
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Research on the Method for Periodic Estimation of the PN Sequence in the Lower SNR DS/SS Signals
Fan Zhou,Yongxin Feng,Xiaoyu Zhang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.10
The periodic estimation method of the PN sequence in DS Signal is researched. It is hard for traditional methods to estimate PN period of DS/SS accurately in the condition of low Signal-to-noise ratio. On the basis of the traditional spectrum reprocessing and the cepstrum method, a PN period estimation method which has integrated serial average with spectrum reprocessing and the cepstrum are put forward to solve this problem and meet the requirements of lower Signal-to-noise ratio. The simulation results show that, when the period of PN sequence is estimated accurately, the cepstrum method has better estimating performance than the spectrum reprocessing method, and the spectral estimation method has lower Signal-to-noise ratio tolerance after improvements.
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An Improved Synchronization Acquisition Method of Fast Frequency Hopping Signal
Minghao Tian,Fang Liu,Yongxin Feng 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.8
The synchronization technology is one of the key technologies in fast frequency hopping communication system. The performance of synchronous loop will directly affect the whole performance of the fast frequency hopping communication system. In view of the deficiency of synchronization time and synchronization anti-interference in the existing synchronization methods, an improved synchronization acquisition method is put forward, that is double judgment circular correlation method. The test results show that the improved synchronization acquisition method can effectively improve synchronization speed, increase synchronization anti-interference performance and reduce the complexity of synchronization loop.
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Research on the Surface Defect Detection of Magnetic Sheet for Industrial Manufacture
Wenbo Zhang,Rongwei Duan,Yongxin Feng,Deyu Zhang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.1
The research of defect detection technology of magnetic sheet surface has important significance for improvement of production efficiency and product quality in enterprise. A single threshold method is used for binary segmentation of magnetic sheet image in this paper, then the template localization algorithm is used for template position of magnetic sheet image after binary segmentation, the magnetic sheet image with different position should be transformed into a unified coordinate system for detecting, then least square method is used for fitting straight line and fillet edge, then the calculated values are compared with standard value to determine whether are knock edges. Further, the appropriate grayscale threshold is set by the pitting and scratches characteristics of magnetic sheet image, the magnetic sheet image should be segmented according to the determined threshold value, if the defect exists, the defect area or length will be calculated, then the result is compared with standard values to determine whether is pitting or scratches. The final tests of technology in this paper show that the detection rate of knock edge and the detection rate of pitting or scratches are both acceptable.
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A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism
Xiangyu Ma,Yuntao Zhao,Yongxin Feng,Yutao Hu 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.2
Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.
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