The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A framework of hiberarchy trusted network based on the grade division was put forward, and the partition rules of trusted attributes as well as the methods of grade division were explained in detail. Moreover, the potential applications of this framework in trusted network were discussed, and the access procedure of terminals in hiberarchy trusted network was given based on the existing research results...
In recent studies, the lifetime of wireless sensor network (WSN) has been paid more attention to in order to evaluate the network performance. In this paper, service-oriented lifetime is proposed to provide a new method of assessment. The deployment of network can be guided by this assessment through placing more nodes where higher service quality is needed. In this way, the wasted energy can be reduced...
Formal concept analysis was creatively used in policy based network management in this study. It takes much time for a conflict detection routine to search every policy in policy repository with conventional policy access models to see if conflict occurs before a new dynamic policy is added to the policy repository. A novel access model for dynamic policies was proposed based on classification concept...
The trusted network connect (TNC) is based on the double concepts of integrity and identity. The policy enforcement point (PEP) in TNC architecture is deeply studied and powerful broadband remote access server (BRAS) is also applied into the architecture. The new BRAS model is able to set up access control rules in accordance with the level of trust, filter traffic according to filtering rules and...
Peer-to-peer systems and applications are the hotspot of research of network applications. As peer-to-peer system has no central system and is deployed on an open network, new concerns regarding security have been raised. As an additional security measure, the intrusion detection system would help determine whether unauthorized users are attempting to access, have already accessed, or have compromised...
A novel structure learning algorithm for fuzzy neural networks (SLNN) is presented in this paper. The neurons of SLNN are created and adapted as online learning proceeds. The learning rule of SLNN is based on Hebbian learning and a kernel winner-take-all algorithm - KWTA. KWTA not only can let SLNN be able to learn from new data but also can prevent losing the knowledge which has been learned earlier...
The diameter protocol is recommended by IETF as AAA (authentication, authorization and accounting) protocol criterion for the next generation network. Because the IPv6 protocol will be widely applied in the intending all-IP network, mobile IPv6 application based on diameter protocol will play more important role in authentication, authorization and accounting. In this paper, the implementation of...
In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule...
A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN is called dynamic growing because it is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.