Biblio
Cyber-attacks have been evolved in a way to be more sophisticated by employing combinations of attack methodologies with greater impacts. For instance, Advanced Persistent Threats (APTs) employ a set of stealthy hacking processes running over a long period of time, making it much hard to detect. With this trend, the importance of big-data security analytics has taken greater attention since identifying such latest attacks requires large-scale data processing and analysis. In this paper, we present SEAS-MR (Security Event Aggregation System over MapReduce) that facilitates scalable security event aggregation for comprehensive situation analysis. The introduced system provides the following three core functions: (i) periodic aggregation, (ii) on-demand aggregation, and (iii) query support for effective analysis. We describe our design and implementation of the system over MapReduce and high-level query languages, and report our experimental results collected through extensive settings on a Hadoop cluster for performance evaluation and design impacts.
Cyber-attacks have been evolved in a way to be more sophisticated by employing combinations of attack methodologies with greater impacts. For instance, Advanced Persistent Threats (APTs) employ a set of stealthy hacking processes running over a long period of time, making it much hard to detect. With this trend, the importance of big-data security analytics has taken greater attention since identifying such latest attacks requires large-scale data processing and analysis. In this paper, we present SEAS-MR (Security Event Aggregation System over MapReduce) that facilitates scalable security event aggregation for comprehensive situation analysis. The introduced system provides the following three core functions: (i) periodic aggregation, (ii) on-demand aggregation, and (iii) query support for effective analysis. We describe our design and implementation of the system over MapReduce and high-level query languages, and report our experimental results collected through extensive settings on a Hadoop cluster for performance evaluation and design impacts.
Using heterogeneous clouds has been considered to improve performance of big-data analytics for healthcare platforms. However, the problem of the delay when transferring big-data over the network needs to be addressed. The purpose of this paper is to analyze and compare existing cloud computing environments (PaaS, IaaS) in order to implement middleware services. Understanding the differences and similarities between cloud technologies will help in the interconnection of healthcare platforms. The paper provides a general overview of the techniques and interfaces for cloud computing middleware services, and proposes a cloud architecture for healthcare. Cloud middleware enables heterogeneous devices to act as data sources and to integrate data from other healthcare platforms, but specific APIs need to be developed. Furthermore, security and management problems need to be addressed, given the heterogeneous nature of the communication and computing environment. The present paper fills a gap in the electronic healthcare register literature by providing an overview of cloud computing middleware services and standardized interfaces for the integration with medical devices.