In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux...Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.展开更多
Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
频繁模式挖掘(Frequent Pattern Mining,FPM)是图数据分析、挖掘领域的核心问题之一,其目的是从大规模图数据中发现支持度不低于指定阈值的模式.传统的频繁模式挖掘算法依赖支持度进行剪枝,返回结果往往包含大量“冗余”模式;top-k模式...频繁模式挖掘(Frequent Pattern Mining,FPM)是图数据分析、挖掘领域的核心问题之一,其目的是从大规模图数据中发现支持度不低于指定阈值的模式.传统的频繁模式挖掘算法依赖支持度进行剪枝,返回结果往往包含大量“冗余”模式;top-k模式挖掘算法虽然仅返回k个频繁模式,但该类算法主要依据“客观”指标,如支持度等,对模式进行评估,难以充分反映用户的主观兴趣偏好.针对上述问题,提出一种基于主动学习的模式兴趣评估方法(Pattern Interestingness Evaluation with Active Learning,PIEAL),通过主动学习策略,从采样图上挖掘的频繁模式中选择代表性模式,并利用有限次人机交互收集用户对这些模式的偏好,进而预测模式的兴趣分数,指导算法发现用户感兴趣的模式.在人机交互环节,PIEAL采用基于成对比较的策略来收集用户对模式的偏好反馈,有效降低了用户的主观评价难度.在真实数据集上的实验结果表明,PIEAL仅需要少量的人机交互便可发现用户感兴趣的模式,其测试集准确率最高可达95%.展开更多
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金supported by the National Grand Fundamental Research "973" Program of China (2004CB318109)the National High-Technology Research and Development Plan of China (2006AA01Z452)the National Information Security "242"Program of China (2005C39).
文摘Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
文摘频繁模式挖掘(Frequent Pattern Mining,FPM)是图数据分析、挖掘领域的核心问题之一,其目的是从大规模图数据中发现支持度不低于指定阈值的模式.传统的频繁模式挖掘算法依赖支持度进行剪枝,返回结果往往包含大量“冗余”模式;top-k模式挖掘算法虽然仅返回k个频繁模式,但该类算法主要依据“客观”指标,如支持度等,对模式进行评估,难以充分反映用户的主观兴趣偏好.针对上述问题,提出一种基于主动学习的模式兴趣评估方法(Pattern Interestingness Evaluation with Active Learning,PIEAL),通过主动学习策略,从采样图上挖掘的频繁模式中选择代表性模式,并利用有限次人机交互收集用户对这些模式的偏好,进而预测模式的兴趣分数,指导算法发现用户感兴趣的模式.在人机交互环节,PIEAL采用基于成对比较的策略来收集用户对模式的偏好反馈,有效降低了用户的主观评价难度.在真实数据集上的实验结果表明,PIEAL仅需要少量的人机交互便可发现用户感兴趣的模式,其测试集准确率最高可达95%.
