Research on android malware detection and interception based on behavior monitoring

Focusing on the sensitive behaviors of malware, such as privacy stealing and money costing, this paper proposes a new method to monitor software behaviors and detect malicious applications on Android platform. According to the theory and implementation of Android Binder interprocess communication mechanism, a prototype system that integrates behavior monitoring and intercepting, malware detection, and identification is built in this work. There are 50 different kinds of samples used in the experiment of malware detection, including 40 normal samples and 10 malicious samples. The theoretical analysis and experimental result demonstrate that this system is effective in malware detection and interception, with a true positive rate equal to 100% and a false positive rate less than 3%.     

Methods for Increasing Creditability of Anomaly Detection System

Based on Bayes‘ theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.     

Intrusion Detection Method for Program Vulnerability via Library Calls

Library function call sequence is the direct reflection of a program＇s behavior. The relationship between program vulnerability and library calls is analyzed, and an intrusion detection method via library calls is proposed, in which the short sequences of library call are used as signature profile. In this intrusion detection method, library interposition is used to hook library calls, and with the discussion of the features of the library call sequence in detail, an algorithm based on information-theory is applied to determine the appropriate length of the library call sequence. Experiments show good performance of our method against intrusions caused by the popular program vulnerabilities.     

Adaptive interactive genetic algorithms with individual interval fitness

It is necessary to enhance the performance of interactive genetic algorithms in order to apply them to complicated optimization problems successfully. An adaptive interactive genetic algorithm with individual interval fitness is proposed in this paper in which an indi-vidual fitness is expressed by an interval. Through analyzing the fitness, information reflecting the distribution of an evolutionary population is picked up, namely, the difference of evaluating superior individuals and the difference of evaluating a population. Based on these, the adaptive probabilities of crossover and mutation operators of an individual are presented. The algorithm proposed in this paper is applied to a fashion evolutionary design system, and the results show that it can find many satisfactory solutions per generation. The achievement of the paper provides a new approach to enhance the t~erformance of interactive genetic algorithms.     

An Intrusion Detection Method Based on Hierarchical Hidden Markov Models

This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model （HHMM） is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system＇s normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.     

A new complex Duffing oscillator used in complex signal detection

In view of the fact that complex signals are often used in the digital processing of certain systems such as digital communication and radar systems,a new complex Duffing equation is proposed.In addition,the dynamical behaviors are analyzed.By calculating the maximal Lyapunov exponent and power spectrum,we prove that the proposed complex differential equation has a chaotic solution or a large-scale periodic one depending on different parameters.Based on the proposed equation,we present a complex chaotic oscillator detection system of the Duffing type.Such a dynamic system is sensitive to the initial conditions and highly immune to complex white Gaussian noise,so it can be used to detect a weak complex signal against a background of strong noise.Results of the Monte-Carlo simulation show that the proposed detection system can effectively detect complex single frequency signals and linear frequency modulation signals with a guaranteed low false alarm rate.     

An effective PN blind estimation reconstruction strategy to solve data reversions interference

In a direct spectrum （DS） system, the PN code can be estimated by analyzing the singular vectors of the received data matrix in order to blind despread in a non-cooperative context. But as there are informa-tion data reversions in the analyzed data matrix, some parts of the estimated PN code may be invertible to the original PN code, which may bring about problems in the following despreading process. In order to solve this problem, a method to well reconstruct the PN code is proposed. This method is based on power detection. The combination scheme which has the maximum power is the best combination scheme that is most suitable to the original PN code. Simulation results show that the method can reconstruct the PN code very well,even if the signal-to-noise ratio is low.     

Nonlinear fault diagnosis method based on kernel principal component analysis

To ensure the system run under working order, detection and diagnosis of faults play an important role in industrial process. This paper proposed a nonlinear fault diagnosis method based on kernel principal component analysis (KPCA). In proposed method, using essential information of nonlinear system extracted by KPCA, we constructed KPCA model of nonlinear system under normal working condition. Then new data were projected onto the KPCA model. When new data are incompatible with the KPCA model,it can be concluded that the nonlinear system is out of normal working condition. Proposed method was applied to fault diagnosis on rolling bearings. Simulation results show proposed method provides an effective method for fault detection and diagnosis of nonlinear system.     

A DNA sequence alignment algorithm using quality information and a fuzzy inference method

DNA sequence alignment algorithms in computational molecular biology have been improved by diverse methods. In this paper, we propose a DNA sequence alignment that uses quality information and a fuzzy inference method developed based on the characteristics of DNA fragments and a fuzzy logic system in order to improve conventional DNA sequence alignment methods that uses DNA sequence quality information. In conventional algorithms, DNA sequence alignment scores are calculated by the global sequence alignment algo- rithm proposed by Needleman-Wunsch, which is established by using quality information of each DNA fragment. However, there may be errors in the process of calculating DNA sequence alignment scores when the quality of DNA fragment tips is low, because only the overall DNA sequence quality information are used. In our proposed method, an exact DNA sequence alignment can be achieved in spite of the low quality of DNA fragment tips by improvement of conventional algorithms using quality information. Mapping score param- eters used to calculate DNA sequence alignment scores are dynamically adjusted by the fuzzy logic system utilizing lengths of DNA fragments and frequencies of low quality DNA bases in the fragments. From the experiments by applying real genome data of National Center for Biotechnology Information, we could see that the proposed method is more efficient than conventional algorithms.     

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized; the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.     

On-line Detection of Gas Pipeline Based on the Real-Time Algorithm and Network Technology with Robot

The detection system integrates control technology, network technology, video encoding and decoding, video transmission, multi-single chip microcomputer communication, database technology, computer software and robot technology. The robot can adaptively adjust its status according to diameter (from 400 mm to 650 mm) of pipeline. The maximum detection distance is up to 1 000 m. The method of video coding in the system is based on fractal transformation. The experiments show that the coding scheme is fast and good PSNR. The precision of on-line detection is up to 3% thickness of pipeline wall. The robot can also have a high precision of location up to 0.03 m. The control method is based on network and characterized by on-line and real-time. The experiment in real gas pipelineshows that the performance of the detection system is good.     

Research of EDM Spark Locations in Die-sinking

Detection of 2-dimention spark locations by electromagnetic detection method in electrical discharge machining (EDM) is studied. The method, which is applied and investigated, is based on the fact that the release of energy from a spark is transformed into electromagnetic wave around the workpiece. A new sensor system composed of high precision linear Hall components and cubic ferrite is used to detect the intensity of magnetic field. Relation equation between the output of the sensor system and 2-dimention spark locations experiment under a spiculate electrode is introduced, and its diagram of curve is drawn. As a result, the information that can be achieved by detecting spark‘s location gives new possibilities for an extended analysis of the EDM-process.     

Research on transferable off-line electronic cash system

In this paper, a new cash scheme is proposed for electronic payment system, in which the cash can be transferred several times. When this kind of cash is used, the fraud such as double spending can be found out but the bank and the trusted party needs not be involved online in each transaction. This cash system is anonymous in normal transactions. But if a fraud happens, the trusted party can withdraw the anonymity to find out the cheater. The new cash scheme is transferable, anonymous, off-line and efficient.     

Study on the concentrated SF6 quantitative intelligent ultrasonic detection system

<正> In order to realize on-line quantitative detection on SF_6 and effective control of running state of SF_6high voltage power supply system,a concentrated SF_6 quantitative ultrasonic on-line detection system hasbeen developed based on the actual demand of electric power system consumers.There are four majorcharacteristics in this system.Firstly,the gas of maximum 64 detection points is transferred through thespecific air path to the detection devices to be detected and analyzed,thereby the electrical lines and thecomplicated installation of the collectors can be avoided;secondly,the differential technique is used toshield the influence of environmental factors,which effectively improves the accuracy of the acoustic detection ;thirdly,the SF_6 concentration is determined by the speed and phase in the ultrasonic wave transmissionprocess,therefore there is no secondary pollution for the purely physical means;finally,the matureembedded technique is applied in this system to improve its intelligence and stability.     

An Efficient Real-Time Fault-Tolerant Scheduling Algorithm Based on Multiprocessor Systems

In the context of real-time fault-tolerant scheduling in multiprocessor systems, Primary-backup scheme plays an important role. A backup copy is always preferred to be executed as passive backup copy whenever possible because it can take the advantages of backup copy de-allocation technique and overloading technique to improve schedulability. In this paper, we propose a novel efficient fault-tolerant ratemonotonic best-fit algorithm efficient fault-tolerant rate-monotonic best-fit （ERMBF） based on multiprocessors systems to enhance the schedulability. Unlike existing scheduling algorithms that start scheduling tasks with only one processor. ERMBF pre-allocates a certain amount of processors before starting scheduling tasks, which enlarge the searching spaces for tasks. Besides, when a new processor is allocated, we reassign the task copies that have already been assigned to the existing processors in order to find a superior tasks assignment configuration. These two strategies are all aiming at making as many backup copies as possible to be executed as passive status. As a result, ERMBF can use fewer processors to schedule a set of tasks without losing real-time and fault-tolerant capabilities of the system. Simulation results reveal that ERMBF significantly improves the schedulability over existing, comparable algorithms in literature.     

Improved hand tracking algorithm in video sequences for intelligent rehabilitation

Intelligent rehabilitation system is an active research topic. It is motivated by the increased number of limb disabled patients. Human motion tracking is the key technology of intelligent rehabilitation system, because the movement of limb disabled patients needs to be localized and learned so that any undesired motion behavior can be corrected in order to reach an expectation. This paper introduces a realtime tracking system of human hand motion, specifically intent to be used for home rehabilitation. Vision sensor (camera) is employed in this system to track the hand movement, and the improved Camshift algorithm and Kalman filter are used to implement dynamic hand tracking in the video. CAMSHIFT algorithm is able to track any kind of target colors by building a histogram distribution of the H channel in HSV color space from the region of interests selected by users at the initial stage. Kalman filter is able to predict hand location in one image frame based on its location data detected in the previous frame. The experimental results show that this system can track 2D hand motion and has acceptable accuracy by using the two algorithms properly. The new algorithm proposed in this paper can not only deal with the skin color interference problems, but also deal well with the track of complex background.     

Low Complexity MMSE-SQRD Signal Detection Based on Iteration

Aiming at the problem of high computational complexity of Vertical-BLAST(V-BLAST) algorithm in Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing(MIMO-OFDM) system signal detection, this paper first uses Sorted QR Decomposition(SQRD) iterative operation instead of matrix inversion to reduce the computational complexity of the algorithm, and then considering that the algorithm is greatly affected by noise, Minimum Mean Square Error(MMSE) criterion is used to weaken the noise effect. At the same time, in order to reduce the noise and computational complexity, MMSE and SQRD are combined, which can not only reduce the noise and computational complexity, but also obtain the sub-optimal detection order, thus improving the detection performance of the MIMO-OFDM system. Finally, the numerical simulation of the MMSE-SQRD detection algorithm is carried out. The results show that the Eb/No of MMSE-SQRD algorithm is 2 dB greater than that of the MMSE algorithm and the computational complexity is O(N_T~3) under the conditions that N_T =N_R=2 and the BER is 10~(–2). The detection algorithm satisfies the demand of short wave and wideband wireless communication.     

A new method for automatically constructing convexity-preserving interpolatory splines

Constructing a convexity-preserving interpolating curve according to the given planar data points is a problem to be solved in computer aided geometric design (CAGD). So far, almost all methods must solve a system of equations or recur to a complicated iterative process, and most of them can only generate some function-form convexity-preserving interpolating curves which are unaccommodated with the parametric curves, commonly used in CAGD systems. In order to overcome these drawbacks, this paper proposes a new method that can automatically generate some parametric convexity-preserving polynomial interpolating curves but dispensing with solving any system of equations or going at any iterative computation. The main idea is to construct a family of interpolating spline curves first with the shape parameter a as its family parameter; then, using the positive conditions of Bernstein polynomial to respectively find a range in which the shape parameter a takes its value for two cases of global convex data points and piecewise convex data points so as to make the corresponding interpolating curves convexity-preserving and C2(or G1) continuous. The method is simple and convenient, and the resulting interpolating curves possess smooth distribution of curvature. Numerical examples illustrate the correctness and the validity of theoretical reasoning.     

Improved Minimum Detectable Velocity in Bistatic Space-Based Radar

Single orbit bistatic space-based radar （SBR） is composed of two radars in the same orbit. The characteristics of the clutter Doppler-angle spectrum of a single orbit bistatic SBR show that the slope of the mainbeam clutter spectrum is highly sensitive to the cone angles. Therefore, the minimum detectable velocity of the bistatic system is dependent on the cone angle. Then a new combined working mode of singleorbit bistatic SBR system was developed in which one radar will act as the transmitter and another as the receiver to improve detection performance for all angles. Simulation results by space-time adaptive processing verify the improved detection performance. The new design also reduces the average power of each radar system and the size and weight of the on-board solar array-battery system.     

Predictor-based error correction method in short-term climate prediction

In terms of the basic idea of combining dynamical and statistical methods in short-term climate prediction, a new prediction method of predictor-based error correction （PREC） is put forward in order to effectively use statistical experiences in dynamical prediction. Analyses show that the PREC can reasonably utilize the significant correlations between predictors and model prediction errors and correct prediction errors by establishing statistical prediction model. Besides, the PREC is further applied to the cross-validation experiments of dynamical seasonal prediction on the operational atmosphere-ocean coupled general circulation model of China Meteorological Administration/National Climate Center by selecting the sea surface temperature index in Nifio3 region as the physical predictor that represents the prevailing ENSO-cycle mode of interannual variability in climate system. It is shown from the prediction results of summer mean circulation and total precipitation that the PREC can improve predictive skills to some extent. Thus the PREC provides a new approach for improving short-term climate prediction.