• Journal of KIISE:Information Networking
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• v.34 no.1
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• pp.62-72
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• 2007

Mobile Virtual Private Network (MVPN) provides VPN services without geographical restriction to mobile workers using mobile devices. Coexistence of Mobile IP (MIP) protocol for mobility and IPsec-based VPN technology are necessary in order to provide continuous VPN service to mobile users. However, Problems like registration failure or frequent IPsec tunnel re-negotiation occur when IPsec-based VPN Gateway (GW) and MIP are used together. In order to solve these problems, IETF proposes a mechanism which uses external home agent (x-HA) located external to the corporate VPN GW. In addition, based on the IETF proposal, a mechanism that assigns x-HA dynamically in the networks where MN is currently located was also proposed with the purpose to reduce handover latency as well as end-to-end delay. However, this mechanism has problems such as exposure of a session key for dynamic Mobility Security Association (MSA) or a long latency in case of the handover between different networks. In this paper, we propose a new MVPN protocol in order to minimize handover latency, enhance the security in key exchange, and to reduce data losses cause by handover. Through a course of simulation, the performance of proposed protocol is compared with the existing mechanism.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.1-7
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• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.151-164
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• 2009

The importance of sensor networks as a base of ubiquitous computing realization is being highlighted, and espicially the security is recognized as an important research isuue, because of their characteristics.Several efforts are underway to provide security services in sensor networks, but most of them are preventive approaches based on cryptography. However, sensor nodes are extremely vulnerable to capture or key compromise. To ensure the security of the network, it is critical to develop security Intrusion Detection System (IDS) that can survive malicious attacks from "insiders" who have access to keying materials or the full control of some nodes, taking their charateristics into consideration. In this perper, we design a distributed and adaptive IDS architecture on sensor networks, respecting both of energy efficiency and IDS efficiency. Utilizing a modified HEED algorithm, a clustering algorithm, distributed IDS nodes (dIDS) are selected according to node's residual energy and degree. Then the monitoring results of dIDSswith detection codes are transferred to dIDSs in next round, in order to perform consecutive and integrated IDS process and urgent report are sent through high priority messages. With the simulation we show that the superiorities of our architecture in the the efficiency, overhead, and detection capability view, in comparison with a recent existent research, adaptive IDS.