This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoe...This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions within this paper and were selected based on their relation to streaming data and the flexibility of their techniques for use within different domains of streaming data. We discuss the relevance of how combining the techniques of the proposed research works within these compositions can be used to address the anomaly detection problem in streaming cyber datasets. The goal is to show how a combination of techniques from different compositions can solve a prominent problem, anomaly detection.展开更多
In this paper,we propose an approach to formally verify and rigorously validate a simulation system against the specification of the real system.We implement the approach in a verification and validation calculator to...In this paper,we propose an approach to formally verify and rigorously validate a simulation system against the specification of the real system.We implement the approach in a verification and validation calculator tool that takes as input a set of statements that capture the requirements,internal conditions of the system and expected outputs of the real system and produces as output whether the simulation satisfies the requirements,faithfully represents the internal conditions of the system and produces the expected outputs.We provide a use case to show how subject matter experts can apply the tool.展开更多
文摘This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions within this paper and were selected based on their relation to streaming data and the flexibility of their techniques for use within different domains of streaming data. We discuss the relevance of how combining the techniques of the proposed research works within these compositions can be used to address the anomaly detection problem in streaming cyber datasets. The goal is to show how a combination of techniques from different compositions can solve a prominent problem, anomaly detection.
文摘In this paper,we propose an approach to formally verify and rigorously validate a simulation system against the specification of the real system.We implement the approach in a verification and validation calculator tool that takes as input a set of statements that capture the requirements,internal conditions of the system and expected outputs of the real system and produces as output whether the simulation satisfies the requirements,faithfully represents the internal conditions of the system and produces the expected outputs.We provide a use case to show how subject matter experts can apply the tool.
