Kyeong-Soo Han

The design and implementation of MAC security in EPON

Because Ethernet passive optical network (EPON) is based on shared-medium network and a point-to-multipoint optical tree configuration, frames transmitted to any ONU are actually broadcast simultaneously to all ONUs. In EPON, the security of data transmission is critical in protecting the privacy of users and confidentiality of their communication. The MACsec is a link security method proposed by IEEE 802.1AE WG and under standardization. We implement the MACsec on the basis of Draft suggested by IEEE 802.1AE. Our MAC security (MACsec) provides user data confidentiality, frame data integrity check, and data origin authentication. Also, it provides protection of security threats such as replay attack and DoS attack, etc. These security protections are provided by 128-bit GCM-AES cipher mechanism. We designed the function of MACsec, and implemented it in HDL. Then, we developed two kinds of EPON MAC ASICs using 0.18 mum CMOS process, MACsec is a part of these chips

The Implementation of the Link Security Module in an EPON Access Network

In this paper, the link security module developed by draft standard of IEEE 802.1AE in order to protect all frames from a listening or attacking of a malicious user at an EPON link. And it applied at an EPON OLT (optical line terminal)/ONU (optical network unit) and is provided a link security about the maximum 64 ONUs. It also used a 128 bits GCM-AES (Galois/counter mode-advanced encryption standard) of an authenticated encryption mode that was mandatory-to-implement crypto- algorithm in draft standard. So, link security module can provide the user data confidentiality and frame data integrity, and data origin authenticity to subscribers, and support a link security at the maximum 2 Gbps EPON using the parallel CTR-AES (counter-AES) in the GCM-AES

Realization of anti-collision warning application using V2V communication

Vehicular communications provide new convergence applications by combining vehicles and communication technologies. In this paper, we consider the anti-collision warning application using vehicle-to-vehicle (V2V) communication. First, we introduce the general and special features of the overall system and specifications of the communication prototype. Then, we investigate the application scenarios and communication algorithms including practical measurement results. Some implementation issues of vehicular communications are also addressed.

A DMAC Protocol to Improve Spatial Reuse by Managing the NAV Table of the Nodes in VANET

Several MAC protocols using directional antennas have been proposed to improve capacity in MANET by improving spatial reuse and communication throughput. Although transmissions using the directional MAC protocol are expected to provide significant improvements, they cause problems such as the deafness and fairness problems. High-mobility VANET has a particular problem using the DMAC protocol because vehicles can interfere with each other in contention for transmissions. In this letter, we proposed and evaluated a suitable DMAC protocol for VANET. As a result, spatial reuse increased the average throughput by 40% over omni-MAC, and 20% over general DMAC, while jitter showed a 17% decrease over both protocols. With these results, we confirmed the efficiency of the proposed protocol for use in VANET which requires fast access and reliable data transmission.

Age-Group Classification for Family Members Using Multi-Layered Bayesian Classifier with Gaussian Mixture Model

This paper proposes a TV viewer age-group classification method for family members based on TV watching history. User profiling based on watching history is very complex and difficult to achieve. To overcome these difficulties, we propose a probabilistic approach that models TV watching history with a Gaussian mixture model (GMM) and implements a feature-selection method that identifies useful features for classifying the appropriate age-group class. Then, to improve the accuracy of age-group classification, a multi-layered Bayesian classifier is applied for demographic analysis. Extensive experiments showed that our multi-layered classifier with GMM is valid. The accuracy of classification was improved when certain features were singled out and demographic properties were applied.