A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

纳秒脉冲放电紫外光纤阵列检测与定位方法

峰化电容作为电磁脉冲模拟器中用于陡化脉冲输出的关键部件,在实际工程应用中易发生沿面放电和击穿现象,采用光电检测系统可对绝缘沿面放电现象进行有效分析。针对峰化电容器沿面放电监测的技术难题,研制了一套绝缘沿面放电过程光电检测系统,对绝缘沿面放电现象进行光电检测。首先提出了绝缘介质沿面放电过程光电探测系统的设计方案;其次,对系统的时延性能进行了评价;最后,完成了绝缘介质沿面放电过程定位实验,验证了光电探测系统的可行性。实验表明,该系统能够实现对放电区域的有效定位。     

A sign-function receiving scheme for sine signals enhanced by stochastic resonance

To address the problem that it is difficult to detect an intermediate frequency (IF) signal at the receiving end of a communication system under extremely low signal-to-noise ratio (SNR) conditions, we propose a stochastic resonance (SR)-enhanced sine-signal detection method based on the sign function. By analyzing the SR mechanism of the sine signal and combining it with the characteristics of a dual-sequence frequency-hopping (DSFH) receiver, a periodic stationary solution of the Fokker-Planck equation (FPE) with a time parameter is obtained. The extreme point of the sine signal is selected as the decision time, and the force law of the electromagnetic particles is analyzed. A receiving structure based on the sign function is proposed to maximize the output difference of the system, and the value condition of the sign function is determined. In order to further improve the detection performance, in combination with the central-limit theorem, the sampling points are averaged N times, and the signal-detection problem is transformed into a hypothesis-testing problem under a Gaussian distribution. The theoretical analysis and simulation experiment results confirm that when N is 100 and the SNR is greater than 20 dB, the bit-error ratio (BER) is less than 1.5×10^-2 under conditions in which the signal conforms to the optimal SR parameters.     

Superchiral fields generated by nanostructures and their applications for chiral sensing

Chirality is ubiquitous in natural world. Although with similar physical and chemical properties, chiral enantiomers could play different roles in biochemical processes. Discrimination of chiral enantiomers is extremely important in biochemical, analytical chemistry, and pharmaceutical industries. Conventional chiroptical spectroscopic methods are disadvantageous at a limited detection sensitivity because of the weak signals of natural chiral molecules. Recently, superchiral fields were proposed to effectively enhance the interaction between light and molecules, allowing for ultrasensitive chiral detection. Intensive theoretical and experimental works have been devoted to generation of superchiral fields based on artificial nanostructures and their application in ultrasensitive chiral sensing. In this review, we present a survey on these works. We begin with the introduction of chiral properties of electromagnetic fields. Then, the optical chirality enhancement and ultrasensitive chiral detection based on chiral and achiral nanostructures are discussed respectively. Finally, we give a short summary and a perspective for the future ultrasensitive chiral sensing.     

The effects of pre-equilibrium emission and secondary decay on the determination of freeze-out volume at intermediate energies

The effects of pre-equilibrium emission and secondary decay on the determination of the freeze-out volume are investigated using the isospin-dependent quantum molecular dynamics model accompanied by the statistical decay model GEMINI. Small-mass projectiles and large-mass targets with central collisions are studied at intermediate energies. It is revealed that the proton yields of pre-equilibrium emission are smaller than those of secondary decay. However, the determination of the freeze-out volume from the proton yields is more easily affected by pre-equilibrium emission. Moreover, the percentage of proton yields in the freeze-out stage is found to be approximately 50%.     

Fission fragment mass yields of Th to Rf even-even nuclei

Fission properties of the actinide nuclei are deduced from theoretical analysis. We investigate potential energy surfaces and fission barriers and predict the fission fragment mass yields of actinide isotopes. The results are compared with experimental data where available. The calculations were performed in the macroscopic-microscopic approximation with the Lublin-Strasbourg Drop (LSD) for the macroscopic part, and the microscopic energy corrections were evaluated in the Yukawa-folded potential. The Fourier nuclear shape parametrization is used to describe the nuclear shape, including the non-axial degree of freedom. The fission fragment mass yields of the nuclei considered are evaluated within a 3D collective model using the Born-Oppenheimer approximation.     

一种基于谱线幅值归零的加窗插值FFT谐波检测方法

提出了一种基于谱线幅值归零的加窗插值FFT谐波检测方法用于分析复杂电网信号中的各次谐波.该检测方法将每一次在离散频谱中找到的最大谱线及其左右两侧的数根谱线幅值归零,直到各次谐波分析完毕.通过仿真实验分析了在基波频率稳定时对模拟信号的测量精度,研究了基波频率波动对谐波分析的影响.通过对EAST实验数据的分析,验证了该谐波...     

The AME 2020 atomic mass evaluation (I). Evaluation of input data,and adjustment procedures

This is the first of two articles(Part I and Part II)that presents the results of the new atomic mass evaluation,Ame2020.It includes complete information on the experimental input data that were used to derive the tables of recommended values which are given in Part II.This article describes the evaluation philosophy and procedures that were implemented in the selection of specific nuclear reaction,decay and mass-spectrometric data which were used in a least-squares fit adjustment in order to determine the recommended mass values and their uncertainties.All input data,including both the accepted and rejected ones,are tabulated and compared with the adjusted values obtained from the least-squares fit analysis.Differences with the previous Ame2016 evaluation are discussed and specific examples are presented for several nuclides that may be of interest to Ame users.     

Dual-Domain Fusion Convolutional Neural Network for Contrast Enhancement Forensics

Contrast enhancement forensics techniques have always been of great interest for the image forensics community, as they can be an effective tool for recovering image history and identifying tampered images. Although several contrast enhancement forensic algorithms have been proposed, their accuracy and robustness against some kinds of processing are still unsatisfactory. In order to attenuate such deficiency, in this paper, we propose a new framework based on dual-domain fusion convolutional neural network to fuse the features of pixel and histogram domains for contrast enhancement forensics. Specifically, we first present a pixel-domain convolutional neural network to automatically capture the patterns of contrast-enhanced images in the pixel domain. Then, we present a histogram-domain convolutional neural network to extract the features in the histogram domain. The feature representations of pixel and histogram domains are fused and fed into two fully connected layers for the classification of contrast-enhanced images. Experimental results show that the proposed method achieves better performance and is robust against pre-JPEG compression and antiforensics attacks, obtaining over 99% detection accuracy for JPEG-compressed images with different QFs and antiforensics attack. In addition, a strategy for performance improvements of CNN-based forensics is explored, which could provide guidance for the design of CNN-based forensics tools.     

An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.