With the development of Internet of things (IoT) technology and the increasing demand for building energy management, many buildings are adopting intelligent building management systems based on IoT devices. The intelligent building management system not only collect large amounts of real-time data from IoT devices, but also require data analysis to monitor building environment. This paper proposes a data processing framework for IoT-based intelligent building management (IBFRAME). The proposed framework aims to provide data collection, monitoring, processing, routing, and analytics services to support a variety of intelligent building applications. In this framework, an ingress router converts and forwards incoming data to application servers and data processors. To monitor building environment, data enrichers analyze thermal comfort and occupancy detection in each space of the building based on IoT sensor data, such as temperature, humidity, and CO2concentration. Data collectors of the framework gather different types of data from external data sources, such as weather databases and energy meter devices. This framework is used as the foundation for processing and analyzing IoT data in intelligent buildings, and designed to facilitate module extension. It supports the implementation and operation of diverse intelligent building application services. This paper also discusses the structure of the system that is built and operated based on the proposed framework. The system adopts a container-based time series database, which specializes in massive time series data processing, as a data management system.

Retrofit could be still expensive solution to existing building. However, with IoT(Internet of Things) environment sensors, it is possible to build a powerful intelligent building monitoring system with relatively low costs. The IoT environment sensor can analyze the IAQ(Indoor Air Quality), occupancy, and comfort level of the individual space by integrating temperature, humidity, carbon dioxide, sound, illuminance, organic compound, human body detection and IR sensor in the individual space of the building. In addition, because it supports Wifi mesh network, it can be easily installed without performing construction for retrofit in existing building, and real time monitoring can be performed. In this paper, we introduce the result of applied the IoT environment sensors to three buildings and visualization interface based on the result of analysis and real environment sensing

In order to utilize a unmanned aerial device such as a drone in terms of reconnaissance, surveillance and disaster management, a technique capable of performing real-time human detection is needed. In this study, we introduce a method of human detection by constructing environment similar to a drone by installing wide angle lens camera on the ceiling. Tiny-Yolo, which is a kind of Convolutional Neural Network, was used as recognition technology. Camera sensors were installed on ceilings in various places inside the building to collect training data for human detection. The Training was performed based on 4,000 pieces of collected data, and the accuracy of recognition was measured. As a result, 95.37% precision and 96.60% recall were obtained.

In this paper, we propose a proactive security method that estimates distributed denial of service (DDoS) attack volume in order to overcome the limitation of the response time of reactive security systems based on intrusion detection. To that end, we define and discuss network intrusion forecasting and intrusion factors in comparison to intrusion detection. Intrusion factors for a DDoS attack are also analyzed and collected from the Honeynet system. Based on the data from the Honeynet system, we conduct correlation and regression analysis, both statistical approaches, to predict the potential DDoS attack volume for the network security of our university. By combining network intrusion detection with intrusion forecasting, network operators can take active countermeasures based on the forecasting results and strengthen the network security.

The hierarchical network structure is proposed as a solution to the problem of decreased network performance for large-scale multimedia delivery in a single wireless LAN environment. However, the network structure on the Android OS is used for media streaming service; the role of the source device is important because a group owner delay causes a performance problem in which streaming data transmission to all Wi-Fi direct network groups creates a bottleneck. For a solution to these problems, we propose the media stream relay engine architecture and describe sequence diagrams for the development of a relay engine on the Android OS.

In this paper, we propose a Media Distribution Tree (MDT) construction method to provide efficient multimedia streaming service between mobile smart devices in mobile ad hoc networks. An MDT for efficient media streaming is constructed based on a maximum spanning tree, which considers device hardware performance, energy and wireless network state to determine media stream relay nodes. The MDT represents the media streaming paths among mobile smart devices and connects a group owner device and group member devices. Finally, we measure network bandwidth and round-trip time on the media streaming network, which follows the constructed MDT using the proposed method, and analyze the experiment results.

We developed a robust approach that can be synchronized with accuracy to a media streaming group in a network congestion situation. This approach consists of a server-side and client-side algorithm. The server-side algorithm is used to transmit the playback information using a synchronization control packet to clients. The synchronization control packet is transmitted in an event-driven manner. The clientside algorithm synchronizes the playback position by using the information received from the server. The detailed algorithm is used to accurately synchronize the playback position on the client. Finally, we measured the synchronization accuracy according to the network congestion. The performance results of this experiment can improve the completion time and the synchronization accuracy in network congestion.

We report performance measurements and analyses of a variety of wireless networks to arrive at an appropriate network structure configuration for smooth multimedia streaming service with a variety of smart devices in a single wireless network environment. Unlike the usual infrastructure network, configurations such as the IEEE 802.11 ad hoc network and the Wi-Fi direct network use direct connections between devices without going through a wireless AP. Therefore, these configurations prevent concentrating traffic at the wireless AP. We generated three types of wireless network performance measurements and arrived at a suitable network for multimedia streaming service using a variety of smart devices. The wireless network was configured in a structure for efficient multimedia streaming service in a single wireless LAN environment, and subsequently, the network performance was measured.

The method of inferring firewall policy, using Active Probing repeats the process of transmitting TCP/UDP/ICMP packets and receiving ICMP response packets. However, if ICMP response packets cannot be received normally, the accuracy of inferring the firewall policy decreases, and it is necessary to verify the feasibility in real conditions. In this paper, we collect Autonomous System (AS) information to investigate the tolerance of ICMP intended for all AS across the world in addition to DNS server information, which is operational within AS. We confirm whether ICMP response packets are received or not by transmitting probing packets to the DNS server. Finally, we propose the AS information that received ICMP packets as the result of the test.

This paper proposes a proactive security system to forecast Distributed Denial of Service (DDoS) attacks. A reactive system focused on detection after network attacks occur has difficulties responding rapidly to massive distributed attacks, such as DDoS. By forecasting the attack, we can take active countermeasures such as strengthening the power of the security devices and it would also enable us to plan a recovery procedure and countermeasures beforehand, providing a more rapid response. In this paper, we discuss previous studies related to intrusion forecasting, define the concept of intrusion forecasting and propose the Internet Intrusion Forecasting System Architecture. To obtain intrusion factors for DDoS attack forecasts, Honeynet was deployed and we analyze Hflow data gathered from Honeynet.