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A Dimension Reduction Method for Unsupervised Outlier Detection in High Dimensional Data
http://doi.org/10.5626/JOK.2022.49.7.537
Among various outlier detection methods, Isolation Forest is known to be very effective in detecting outliers. But it is difficult to apply to high dimensional data due to the sparseness of the data and the limitation of the total number of attributes that can be selected for node partitioning. In this paper, we propose a dimension reduction method for unsupervised outlier detection in high dimensional data. Dimension reduction is performed by linear transformation maximizing kurtosis, and in the transformed space, outlier detection by Isolation Forest is applied. Kurtosis is a statistical measure that can be interpreted as the degree of the presence of outliers in the distribution. A linear transformation is found by using a simple one-layer neural network where a subset of features having the highest kurtosis is used as input features and an objective function, which maximizes kurtosis in output nodes, is set. The experimental results using text data demonstrated the high detection performance of Isolation Forest modeled in the space transformed by the proposed dimension reduction method.
Host-Level I/O Scheduler for Achieving Performance Isolation with Open-Channel SSDs
Sooyun Lee, Kyuhwa Han, Dongkun Shin
http://doi.org/10.5626/JOK.2020.47.2.119
As Solid State Drives (SSDs) provide higher I/O performance and lower energy consumption compared to Hard Disk Drives (HDDs), SSDs are currently widening its adoption in areas such as datacenters and cloud computing where multiple users share resources. Based on this trend, there is currently greater research effort being made on ensuring Quality of Service (QoS) in environments where resources are shared. The previously proposed Workload-Aware Budget Compensation (WA-BC) scheduler aims to ensure QoS among multiple Virtual Machines (VMs) sharing an NVMe SSD. However, the WA-BC scheduler has a weakness in that it misuses multi-stream SSDs for identifying workload characteristics. In this paper, we propose a new host-level I/O scheduler, which complements this vulnerability of the WA-BC scheduler. It aims to eliminate performance interference between different users that share an Open-Channel SSD. The proposed scheduler identifies workload characteristics without having to allocate separate SSD streams by observing the sequentiality of I/O requests. Although the proposed scheduler exists within the host, it can reflect the status of device internals by exploiting the characteristics of Open-Channel SSDs. We show that by identifying those that attribute more to garbage collection, a source of I/O interference within SSDs, using workload characteristics and penalizing such users helps to achieve performance isolation amongst different users sharing storage resources.
Efficient Ways of Attack for Network Isolation
Kyu Seok Han, Jiwon Yoon, Taekyu Kim, Young Woo Park, Jungkyu Han
http://doi.org/10.5626/JOK.2018.45.5.489
Many devices and objects have recently been connected to the network using the Internet of Thing technology. In a local area network (LAN) network for small scale, many devices are connected and the complexity of the network topology is greatly increased. Large-scale networks of such small-scale networks are also expanding nationwide. he flow of gathering and spreading data in a concentrated or distributed manner within a large network is being made. This is useful for various industries, financial, telecommunications, military, and power generation facilities in statebased industries use the nationwide Internet network to control and maintain a stream of data that can cope with emergency situations. In a network environment that has such a circumstance, if a critical device (node) or a small range of network (LAN) that is involved in the control, data collection, storage, or data processing is isolated and isolated from the entire network. This paper discusses techniques for isolating critical LANs or Nodes in large networks.
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