Xueyao Li

A new intrusion detection method based on behavioral model

Intrusion detection has emerged as an important approach to network security. A new method for anomaly intrusion detection is proposed based on linear prediction and Markov chain model. Linear prediction is employed to extract features from system calls sequences of the privileged processes which are used to make up of the character database of those processes, and then the Markov chain model is founded based on those features. The observed behavior of the system is analyzed to infer the probability that the Markov chain model of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The experiments show this method is effective and efficient, and can be used in practice to monitor the computer system in real time.

Intrusion detection based on hidden Markov model

The intrusion detection technologies of the network security are researched, and the technologies of pattern recognition are used to intrusion detection. Intrusion detection rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. Hidden Markov Model (HMM) has been successfully used in speech recognition and some classification areas. Since Anomaly Intrusion Detection can be treated as a classification problem, some basic ideas have been proposed on using HMM to model normal behavior. The experiments have showed that the method based on HMM is effective to detect anomalistic behaviors.

An new intrusion detection method based on linear prediction

Intrusion detection has emerged as an important approach to network security. A new method for anomaly intrusion detection is proposed based on linear prediction and Markov chain model. Linear prediction is employed to extract features from system calls sequences of the privileged processes, and the Markov chain model is founded based on those features. The observed behavior of the system is analyzed to infer the probability that the Markov chain model of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. Markov information source entropy (MISE) and condition entropy (CE) are used to select parameters. The merits of the model are simple and exact to predict. The experiments show this method is effective and efficient, and can be used in practice to monitor the computer system in real time.

Method of speech enhancement based on Hilbert-Huang transform

A new speech enhancement method of Hilbert-Huang transform (HHT) was proposed. Hilbert-Huang transform is a new and powerful theory for the time-frequency analysis and is efficient for describing the local features of dynamic signals. On the basis of basic speech enhancement methods and HHT algorithm, a new method of speech enhancement is introduced. Using the EMD algorithm, firstly the speech signal is decomposed into several intrinsic mode functions, then speech detection and denoising is done selectively according to their own characters, and lastly the signal is rebuilt. While the SNR of the speech is low, the experimental results show that the denoising effect of the proposed method is better than that of other methods based on wavelet shrinkage, the algorithm is valid on several noise conditions for most of speech signals and is capable to improve the SNR of the speech.

Speech enhancement in short-wave channel based on ICA in empirical mode decomposition domain

It is well known that the non-stationary noise is the most difficult to be removed in speech enhancement. In this paper a novel speech enhancement algorithm based on the empirical mode decomposition (EMD) and then ICA is proposed to suppress the non-stationary noise. The noisy speech is decomposed into components by the EMD and ICA-based vector space, and the components are processed and reconstructed, respectively, by distinguishing between voiced speech and unvoiced speech. There are no requirements of noise whitening and SNR pre-calculating. Experiments show that the proposed method performs well suppressing of the non-stationary noise in short-wave channel for speech enhancement.

Speech stream detection based on higher-order statistics

The aim of speech stream detection is to capture the speech stream whose coming is random. The idea of using Higher Order Statistics (HOS) for speech stream detection is based on exploiting the Gaussian suppression that allows the separation of speech from the noise. HOS have inherent properties that make them well suited when dealing with a mixture of Gaussian and nonGaussian process. In addition, the HOS of speech signals have distinctive features that may be exploited to lead a better estimation and a more accurate discrimination between speech and noise. This paper explores the fourth order cumulants of speech signal and presents a new algorithm for speech stream detection. The considerable experimental results in which data comes from the real recorded on spot, show the method performs well.

Speech Stream Detection in Strong Noise based on Linear Prediction

The speech signal is usually mixed with a great deal of noise, and the noise weakens seriously the performance of the algorithms to detect speech signal. This paper presents a robust algorithm for speech signal detection in low SNR based on the linear prediction technology. The proposed approach firstly decreases noise by using linear predication residual, then decides whether speech signal is contained or not according to the coefficients statistics of LPC. The operation to speech component is only taken in prediction residual by enhancement, which produces little impairment to the formant of the speech. As most of the energy components of speech signals exist in the region between 300 Hz and 3000 Hz, only those LPC coefficients in this region are taken into account, which also reduces the influence from noise. Experiments show that it is particularly immunized for the proposed algorithm to the strong noise, especially in the white noise. At last, the performance of the algorithm is compared to the approach based on short-term energy in various noise condition, and quantified using the probability of correct classification. The results show that the proposed algorithm has an overall better performance than the referred approach, such as white noise and factory noise to low SNR

Speech Hiding Based on Auditory Wavelet

A novel method to embed secret speech into open speech is proposed. The secret speech is coded into binary parameter bits with Mix-Excitation Linear Prediction (MELP) algorithm, and the bits are used to form hiding information sequence. The open speech is automatically divided into voiced frames and unvoiced frame using auditory wavelet transform. One voice frame, the auditory wavelet transform was used to detect pitch, and the pitch is utilized to the current embedding position in open speech. The information hiding procedure is completed by modifying relevant wavelet coefficients. At the receiver, based on the same pitch detection method, the embedding position is found and the hiding bit is recovered. The secret speech can be received after MELP decoding. The experiments show that the method is strongly robust to many attacks such as compression, filter and so on.

Aircraft type recognition based on shortwave speech communication with PLP

This paper studies aircraft type recognition based on shortwave speech communication. Features are extracted based on Perceptual Linear Predictive (PLP) with RASTA and High-order Cumulant, considering auditory perceptual properties, which combines with physical properties of the target acoustic signal at the same time. Support vector machine(SVM) is used as classifier. The simulation results of aircraft type recognition have shown that RASTA-PLP-HOC based on PLP with RASTA, combined with HOC with physical properties of signal, can extract the robust feature of aircraft cabin background sound and identify the six kinds of aircrafts with high recognition rate. RASTA-PLP-HOC-SVM has achieved more excellent performance and higher recognition rate than RASTA-PLP -SVM and MFCC-SVM.

Word Sense Disambiguation method based on probability model improved by information gain

Word sense disambiguation (WSD) has always being a key problem and one of difficult points in natural language processing. WSD is usually considered to be a pattern classification to be research. Feature selection is an important sector of WSD process. We review naive Bayes model (NBM) seriously, and the feature selection method adopted in this paper is directed at Bayesian Assumption to improve NBM. Positional information concealed in the context of ambiguous word is mined via information gain calculation, to increase the knowledge acquisition efficiency of Bayesian model and to improve the effect of word-sense classification. Eight ambiguous words are tested in our experiment; the experimental results of improved Bayesian model are higher 3.5 per cent than the ones of NBM. The accuracy rise is bigger and the improvement effect is outstanding; and these results prove also the method put forward in this paper is efficacious.