The customer population of entities potentially requesting to join a queue for service often have identical structure, i.e., the same state set and isomorphic transitions. The state size of the automaton modeling a qu...The customer population of entities potentially requesting to join a queue for service often have identical structure, i.e., the same state set and isomorphic transitions. The state size of the automaton modeling a queue will grow rapidly with increase of the size of this population. However, by relabeling the queue arrival events and service events to the same symbols respectively, the automaton model of a queue will be converted to the structure of a buffer, which is proved to be independent of the total size of the customer population, as long as the queue size is held fixed. We propose the model of a dynamic buffer to embody order and shift of a queue. The result is applied to a manufacturing facility with a dynamic buffer to manage the repair of broken down machines.展开更多
Detection of unknown attacks like a zero-day attack is a research field that has long been studied.Recently,advances in Machine Learning(ML)and Artificial Intelligence(AI)have led to the emergence of many kinds of att...Detection of unknown attacks like a zero-day attack is a research field that has long been studied.Recently,advances in Machine Learning(ML)and Artificial Intelligence(AI)have led to the emergence of many kinds of attack-generation tools developed using these technologies to evade detection skillfully.Anomaly detection and misuse detection are the most commonly used techniques for detecting intrusion by unknown attacks.Although anomaly detection is adequate for detecting unknown attacks,its disadvantage is the possibility of high false alarms.Misuse detection has low false alarms;its limitation is that it can detect only known attacks.To overcome such limitations,many researchers have proposed a hybrid intrusion detection that integrates these two detection techniques.This method can overcome the limitations of conventional methods and works better in detecting unknown attacks.However,this method does not accurately classify attacks like similar to normal or known attacks.Therefore,we proposed a hybrid intrusion detection to detect unknown attacks similar to normal and known attacks.In anomaly detection,the model was designed to perform normal detection using Fuzzy c-means(FCM)and identify attacks hidden in normal predicted data using relabeling.In misuse detection,the model was designed to detect previously known attacks using Classification and Regression Trees(CART)and apply Isolation Forest(iForest)to classify unknown attacks hidden in known attacks.As an experiment result,the application of relabeling improved attack detection accuracy in anomaly detection by approximately 11%and enhanced the performance of unknown attack detection in misuse detection by approximately 10%.展开更多
文摘The customer population of entities potentially requesting to join a queue for service often have identical structure, i.e., the same state set and isomorphic transitions. The state size of the automaton modeling a queue will grow rapidly with increase of the size of this population. However, by relabeling the queue arrival events and service events to the same symbols respectively, the automaton model of a queue will be converted to the structure of a buffer, which is proved to be independent of the total size of the customer population, as long as the queue size is held fixed. We propose the model of a dynamic buffer to embody order and shift of a queue. The result is applied to a manufacturing facility with a dynamic buffer to manage the repair of broken down machines.
基金This work was supported by the Research Program through the National Research Foundation of Korea,NRF-2018R1D1A1B07050864,and was supported by another the Agency for Defense Development,UD200020ED.
文摘Detection of unknown attacks like a zero-day attack is a research field that has long been studied.Recently,advances in Machine Learning(ML)and Artificial Intelligence(AI)have led to the emergence of many kinds of attack-generation tools developed using these technologies to evade detection skillfully.Anomaly detection and misuse detection are the most commonly used techniques for detecting intrusion by unknown attacks.Although anomaly detection is adequate for detecting unknown attacks,its disadvantage is the possibility of high false alarms.Misuse detection has low false alarms;its limitation is that it can detect only known attacks.To overcome such limitations,many researchers have proposed a hybrid intrusion detection that integrates these two detection techniques.This method can overcome the limitations of conventional methods and works better in detecting unknown attacks.However,this method does not accurately classify attacks like similar to normal or known attacks.Therefore,we proposed a hybrid intrusion detection to detect unknown attacks similar to normal and known attacks.In anomaly detection,the model was designed to perform normal detection using Fuzzy c-means(FCM)and identify attacks hidden in normal predicted data using relabeling.In misuse detection,the model was designed to detect previously known attacks using Classification and Regression Trees(CART)and apply Isolation Forest(iForest)to classify unknown attacks hidden in known attacks.As an experiment result,the application of relabeling improved attack detection accuracy in anomaly detection by approximately 11%and enhanced the performance of unknown attack detection in misuse detection by approximately 10%.
文摘美国橙皮书收录的参比制剂(reference listed drug,RLD)是我国仿制药参比制剂的一个重要来源,因此其可及性对企业顺利开展仿制药质量和疗效一致性评价工作具有重要意义。本文针对美国橙皮书收录的药品参比制剂的3种可能来源:重包装、重贴标和自有品牌经销,结合橙皮书目录、国家药品编码(national drug code,NDC)目录和DailyMed产品标签库的支持数据,以我国仿制药参比制剂目录(第一批)中收录的源于美国橙皮书的参比制剂品种为例,进行了详细的介绍。参比制剂的这3种可能来源符合我国仿制药一致性评价过程中参比制剂的相关要求,可以为企业研发时选择和购买参比制剂提供帮助,也可以为药监当局进行技术审评审批时提供参考。
