Towards the problems of existing detection methods,a novel real-time detection method(DMFIF) based on fractal and information fusion is proposed.It focuses on the intrinsic macroscopic characteristics of network,which...Towards the problems of existing detection methods,a novel real-time detection method(DMFIF) based on fractal and information fusion is proposed.It focuses on the intrinsic macroscopic characteristics of network,which reflect not the "unique" abnormalities of P2P botnets but the "common" abnormalities of them.It regards network traffic as the signal,and synthetically considers the macroscopic characteristics of network under different time scales with the fractal theory,including the self-similarity and the local singularity,which don't vary with the topology structures,the protocols and the attack types of P2P botnet.At first detect traffic abnormalities of the above characteristics with the nonparametric CUSUM algorithm,and achieve the final result by fusing the above detection results with the Dempster-Shafer evidence theory.Moreover,the side effect on detecting P2P botnet which web applications generated is considered.The experiments show that DMFIF can detect P2P botnet with a higher degree of precision.展开更多
To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） ,...To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） , the paper presents an adaptive filter algorithm that combines interacting multiple model （IMM） and non linear Kalman filter. The algorithm describes the motion mode of vehicle by using three state spacemode]s. At first, the parallel filter of each model is realized by using multiple nonlinear filters. Then the weight integration of filtering result is carried out by using the model matching likelihood function so as to get the system positioning information. The method has advantages of nonlinear system filter and overcomes disadvantages of single model of filtering algorithm that has poor effects on positioning the maneuvering target. At last, the paper uses IMM and EKF methods to simulate the global positioning system （OPS）/inertial navigation system （INS）/dead reckoning （DR） integrated positioning system, respectively. The results indicate that the IMM algorithm is obviously superior to EKF filter used in the integrated positioning system at present. Moreover, it can greatly enhance the stability and positioning precision of integrated positioning system.展开更多
We propose a novel, lossless compression algorithm, based on the 2D Discrete Fast Fourier Transform, to approximate the Algorithmic (Kolmogorov) Complexity of Elementary Cellular Automata. Fast Fourier transforms are ...We propose a novel, lossless compression algorithm, based on the 2D Discrete Fast Fourier Transform, to approximate the Algorithmic (Kolmogorov) Complexity of Elementary Cellular Automata. Fast Fourier transforms are widely used in image compression but their lossy nature exclude them as viable candidates for Kolmogorov Complexity approximations. For the first time, we present a way to adapt fourier transforms for lossless image compression. The proposed method has a very strong Pearsons correlation to existing complexity metrics and we further establish its consistency as a complexity metric by confirming its measurements never exceed the complexity of nothingness and randomness (representing the lower and upper limits of complexity). Surprisingly, many of the other methods tested fail this simple sanity check. A final symmetry-based test also demonstrates our method’s superiority over existing lossless compression metrics. All complexity metrics tested, as well as the code used to generate and augment the original dataset, can be found in our github repository: ECA complexity metrics<sup>1</sup>.展开更多
基金supported by National High Technical Research and Development Program of China(863 Program)under Grant No.2011AA7031024GNational Natural Science Foundation of China under Grant No.90204014
文摘Towards the problems of existing detection methods,a novel real-time detection method(DMFIF) based on fractal and information fusion is proposed.It focuses on the intrinsic macroscopic characteristics of network,which reflect not the "unique" abnormalities of P2P botnets but the "common" abnormalities of them.It regards network traffic as the signal,and synthetically considers the macroscopic characteristics of network under different time scales with the fractal theory,including the self-similarity and the local singularity,which don't vary with the topology structures,the protocols and the attack types of P2P botnet.At first detect traffic abnormalities of the above characteristics with the nonparametric CUSUM algorithm,and achieve the final result by fusing the above detection results with the Dempster-Shafer evidence theory.Moreover,the side effect on detecting P2P botnet which web applications generated is considered.The experiments show that DMFIF can detect P2P botnet with a higher degree of precision.
基金National Natural Science Foundation of China(No.61663020)Project of Education Department of Gansu Province(No.2016B-036)
文摘To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） , the paper presents an adaptive filter algorithm that combines interacting multiple model （IMM） and non linear Kalman filter. The algorithm describes the motion mode of vehicle by using three state spacemode]s. At first, the parallel filter of each model is realized by using multiple nonlinear filters. Then the weight integration of filtering result is carried out by using the model matching likelihood function so as to get the system positioning information. The method has advantages of nonlinear system filter and overcomes disadvantages of single model of filtering algorithm that has poor effects on positioning the maneuvering target. At last, the paper uses IMM and EKF methods to simulate the global positioning system （OPS）/inertial navigation system （INS）/dead reckoning （DR） integrated positioning system, respectively. The results indicate that the IMM algorithm is obviously superior to EKF filter used in the integrated positioning system at present. Moreover, it can greatly enhance the stability and positioning precision of integrated positioning system.
文摘We propose a novel, lossless compression algorithm, based on the 2D Discrete Fast Fourier Transform, to approximate the Algorithmic (Kolmogorov) Complexity of Elementary Cellular Automata. Fast Fourier transforms are widely used in image compression but their lossy nature exclude them as viable candidates for Kolmogorov Complexity approximations. For the first time, we present a way to adapt fourier transforms for lossless image compression. The proposed method has a very strong Pearsons correlation to existing complexity metrics and we further establish its consistency as a complexity metric by confirming its measurements never exceed the complexity of nothingness and randomness (representing the lower and upper limits of complexity). Surprisingly, many of the other methods tested fail this simple sanity check. A final symmetry-based test also demonstrates our method’s superiority over existing lossless compression metrics. All complexity metrics tested, as well as the code used to generate and augment the original dataset, can be found in our github repository: ECA complexity metrics<sup>1</sup>.
