A privilege flow oriented intrusion detection method based on HSMM (Hidden semi-Markov Model) is discussed. The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS (Intrusi...A privilege flow oriented intrusion detection method based on HSMM (Hidden semi-Markov Model) is discussed. The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS (Intrusion Detection System). Using the dataset of DARPA 1998, our experiment results reveal good detection performance and acceptable computation cost.展开更多
The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always funct...The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always function reliably under complex and variable deployment environment.In many cases,RFID systems provide only probabilistic observations of object states.Thus,an approach to predict,record and track real world object states based upon probabilistic RFID observations is required.Hidden Markov model(HMM) has been used in the field of probabilistic location determination.But the inherent duration probability density of a state in HMM is exponential,which may be inappropriate for modeling of object location transitions.Hence,in this paper,we put forward a hidden semi-Markov model(HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach.The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.展开更多
股市的情绪化倾向是股票市场具有高度不确定性的主要原因,直接利用历史数据的股票趋势预测方法难以适应市场情绪的多变性,在实际应用中效果不理想。文章针对市场情绪的不稳定性导致股市拐点难以预测的问题,提出一种基于情绪向量的隐半...股市的情绪化倾向是股票市场具有高度不确定性的主要原因,直接利用历史数据的股票趋势预测方法难以适应市场情绪的多变性,在实际应用中效果不理想。文章针对市场情绪的不稳定性导致股市拐点难以预测的问题,提出一种基于情绪向量的隐半马尔可夫模型股市拐点预测方法(hidden semi-Markov model stock turning point prediction method based on sentiment vector,SV-HSMM)。针对市场情绪不可观察性,选取与市场情绪相关的主要特征,使用马尔可夫毯融合成市场情绪;利用隐半马尔可夫模型建模市场环境,构建市场情绪、市场状态和状态持续时间之间的结构关系;引入情绪向量平滑情绪的多变性,并利用Kullback-Leibler(KL)距离量化情绪热度;利用隐半马尔可夫模型的动态推理实现股市拐点预测。结果表明情绪向量方法具有更好的预测效果。展开更多
文摘A privilege flow oriented intrusion detection method based on HSMM (Hidden semi-Markov Model) is discussed. The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS (Intrusion Detection System). Using the dataset of DARPA 1998, our experiment results reveal good detection performance and acceptable computation cost.
基金the National High Technology Research and Development Program(863) of China(No. 2006AA04A114)
文摘The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always function reliably under complex and variable deployment environment.In many cases,RFID systems provide only probabilistic observations of object states.Thus,an approach to predict,record and track real world object states based upon probabilistic RFID observations is required.Hidden Markov model(HMM) has been used in the field of probabilistic location determination.But the inherent duration probability density of a state in HMM is exponential,which may be inappropriate for modeling of object location transitions.Hence,in this paper,we put forward a hidden semi-Markov model(HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach.The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.
文摘股市的情绪化倾向是股票市场具有高度不确定性的主要原因,直接利用历史数据的股票趋势预测方法难以适应市场情绪的多变性,在实际应用中效果不理想。文章针对市场情绪的不稳定性导致股市拐点难以预测的问题,提出一种基于情绪向量的隐半马尔可夫模型股市拐点预测方法(hidden semi-Markov model stock turning point prediction method based on sentiment vector,SV-HSMM)。针对市场情绪不可观察性,选取与市场情绪相关的主要特征,使用马尔可夫毯融合成市场情绪;利用隐半马尔可夫模型建模市场环境,构建市场情绪、市场状态和状态持续时间之间的结构关系;引入情绪向量平滑情绪的多变性,并利用Kullback-Leibler(KL)距离量化情绪热度;利用隐半马尔可夫模型的动态推理实现股市拐点预测。结果表明情绪向量方法具有更好的预测效果。
