Hyochan Bang

A Combined Data Mining Approach for DDoS Attack Detection

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not provide effective defense against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. It is necessary to analyze the fundamental features of DDoS attacks because these attacks can easily vary the used port/protocol, or operation method. In this paper, we propose a combined data mining approach for modeling the traffic pattern of normal and diverse attacks. This approach uses the automatic feature selection mechanism for selecting the important attributes. And the classifier is built with the theoretically selected attribute through the neural network. And then, our experimental results show that our approach can provide the best performance on the real network, in comparison with that by heuristic feature selection and any other single data mining approaches.

An in-depth analysis on traffic flooding attacks detection and system using data mining techniques

Recently, as network traffic flooding attack such as DoS and DDoS have posed devastating threats on network services, rapid detection, and semantic analysis are the major concern for secure and reliable network services. In addition, in a recent issue of the safety and comfort of vehicles and communication technologies for service is required. We propose a traffic flooding attack detection and an in-depth analysis system that uses data mining techniques. In this paper we (1) designed and implemented a system that detects traffic flooding attacks. Then, it executes classification by attack type and it uses SNMP MIB information based on C4.5 algorithm; (2) conducted a semantic interpretation that extracts and analyzes the rules of execution mechanism that are additionally provided by C4.5; (3) performed an in-depth analysis on the attack patterns and useful knowledge inherent in their data by type, utilizing association rule mining. Classification by attack and attack type based on C4.5 and association rules, automatic rule extraction and semantic in-depth interpretation, which are proposed in this paper, provide a positive possibility to add momentum towards the development of new methodologies for intrusion detection systems as well as to support establishing policies for intrusion detection and response systems.

Mobile middleware platform for secure vessel traffic system in IoT service environment

The Inter-VTS Data Exchange Format IVEF service is the draft standard designed for exchange of information on sea traffic between the vessel traffic systems and between the vessels. Standardization of this service is under way as a part of the next-generation navigation system, called e-Navigation. The International Association of Lighthouse Authorities suggests, on its recommendation V-145, the IVEF service model and the protocol for provisioning of this service. But the detailed configuration of this service must be designed by the users. IVEF service is aimed at establishing a common framework to ensure exchange of ship information between vessel traffic service VTS centers gained via automatic ship identification device, closed-circuit television, radar system and other devices. Robust exchange of marine traffic information between VTS centers through IVEF service will enable related authorities to identify location of domestic and international ships in the coastal waters real-time and predict expected sea route, thus ensure effective sea route control and pre-emptive response to potential disasters or accidents. This paper suggests, based on the basic service model and protocol provided in the recommendation V-145, the implementation of the Jeju-VTS middleware, which will facilitate exchange of information on sea traffic. This paper developed a system enabling IVEF service simulation under an Internet of Things environment made possible by improving IVEF software development kit, which is an open source. For this, a mobile phone loaded with Android platform and desk top PC has been used for emulation of a ship on voyage and VTS center. Copyright

Adaptive Internet of Things and Web of Things convergence platform for Internet of reality services

Recently, Internet of things (IoT) and Web of Things (WoT) lead us to the excellent era of connected everything device. However, the devices hardly show the property of the autonomous connectivity and the self-cooperation for applying in real-world environments. The purpose of this paper was to propose the adaptive IoT and WoT convergence platform that enables things to dynamically implement the smart Web without any controls from users. The adaptive IoT and WoT convergence platform, proposed in this paper, is a new type of platform which provides global inter-compatibility to help users to easily communicate with things by connecting through the webs. Through mashup of the things connected to the Web, coarsely or finely, this proposal can guarantee an efficient IoT or WoT platform management, adaptive synchronization between the things, a stable platform environment, and creating new services. The performance of our proposed platform is tested via experiments which verify that its simulations are satisfactory.

Semantic open IoT service platform technology

This paper focuses on how technologies contributes to improving interoperability between IoT devices, and making easily use of IoT devices. The proposed platform technology provides semantic-based IoT information services, and semantic interoperability of IoT devices. This service platform can be applicable to a lot of semantic IoT services: collecting invisible information in real environment by smart devices, providing smart life services by sharing, participating, distributing open sensing information.

IoT as a applications: cloud-based building management systems for the internet of things

Recently, excellent by Internet of Things (IoT), the era of connected everything device is coming. However, the devices hardly show the manner to autonomous connectivity on it and the self-cooperation for applied to real-world environments. In this paper, we proposed a smart building on IoT and cloud-based technology that can perform collaboration and efficient operation with various sensing devices in building and facilities. The smart building is very important to reduce on a huge amount of building energy is consumed by the management system of buildings. The proposed system selects an optimum device feature subset from the computing resources and storages by our cloud-based building management system. The performance of our proposed system is tested via experiments which verify that its measures are satisfactory.

Symmetric Key-Based Authentication and the Session Key Agreement Scheme in IoT Environment

In this paper, the inter-device authentication and session key distribution system for the device with the encryption module only. In the proposed system, unlike the existing sensor network environment where the key distribution center distributes the key, each sensor node agrees with generation of session keys. In the proposed scheme, the performance is improved so that the authenticated device can calculate the session key in advance. The proposed mutual authentication and session key distribution system can respond against replay attacks, man-in-the-middle attacks and wiretapped secret key attacks.

Semantic open USN service platform architecture

This paper proposes an open software platform architecture to provide semantic information collected from various sensor networks and sensing devices. Semantically expressed sensing information and metadata of sensing devices can improve reusability of sensing data, therefore general consumers as well as the restricted user can observe, monitor and analyze sensing values from pre-installed sensing devices.

Analyzing Risk Dependencies on RFID-driven Global Logistics Processes

In this paper, we try to formally analyze risk dependencies on a global logistics process that is made up of a set of RFID-driven biz-steps2. In enacting a global logistics process, it is very important to control and trace each bizsteps execution as well as to visualize its status. Moreover, for the sake of improving QoS (the quality of services), it ought to be crucial for the system to provide an autonomous error-detection functionality on its running exceptions and very safe self-recovery mechanisms, as well, for the exceptional and risky situations. In resolving the QoS issue, the essential technologies might be RFID and BPM/workflow technologies; workflow is for representing a global logistics process in a natural, and RFID is for implementing each biz-steps application program tagging the logistics information. Also, the essential theory of the self-recovery mechanisms must be on the roll-back mechanism that determines up to where the roll-back operations have to be applied among the previous performed biz-steps on the corresponding global logistics process. As an essential theory of implementing a reasonable roll-back mechanism, the paper particularly formalizes a risk dependency analysis algorithm that is able to produce a set of risk dependency knowledge existing among biz-steps of a RFID-driven global logistics process, and that is eventually used for realizing the self-recovery mechanisms.

Real-time and Parallel Semantic Translation Technique for Large-Scale Streaming Sensor Data in an IoT Environment

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.