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With the deployment of IPv6 and the rising threat of ND security issues, SEND is desiderated to be deployed in subnets. SEND binds the node's identifier to IP address and adds the digital signature to its message to resist hijacking and man-in-the-middle attacks. However, during the stage of transition, the coexistence of ND and SEND may cause security issues. This paper analyzes the specific security vulnerabilities in the mixed environment of SEND and ND, and proposes a method of routing attack in the coexistence environment, which takes advantages of the source address selection algorithm to induce the host to send packets through the unsecure addresses and routers, in order to bypass the protection of router advertisement and the authentication of router identity by SEND option. The research of this paper will help the wide deployment of SEND mechanism and provide protection for the IPv6 subnets.

Crowds, which is a classical P2P anonymous communication protocol, adopts random forwarding to effectively provide a privacy preserving way of accessing the web with good expansibility, without web sites being able to recognize who is browsing. However, it does not provide anonymity against global eavesdroppers. Furthermore, recipient anonymity and relationship anonymity are not implemented by Crowds. This paper utilizes the characteristic that IPv6 protocol allows user-defined IPv6 options to improve Crowds anonymous communication protocol. The contributions of our improved Crowds protocol (Crowds6) are as follows: 1. It makes other relays of the re-routing path correctly get last-hop’s address by adding a self-defined option in sender’s packet to store last-hop’s address; 2. When sender sends packets, it uses last-hop’s public key to encrypt symmetry key which is used to decode the message content, so only the last-hop can get recipient’s address, resolving the key sharing problem between sender and last-hop, and achieving recipient anonymity to some extent. The theoretical analysis and simulation results show that our improved Crowds protocol effectively resolves the problem that other relays of the re-routing path can not correctly get last-hop’s address, and performances better than traditional anonymous protocol on defending predecessor attack by utilizing the key sharing technology, at the same time, recipient anonymity is also realized while the communication delay is reduced.

One of the main privacy problems in network communication is lack of anonymity. Much work has been done on this problem in recent years. However, it is a challenge to improve anti-attack ability and degree of anonymity. A new anonymous multiplexing communication protocol, based on IPv6, is proposed in this paper. It utilizes the secret sharing technique to establish multiplexing communication, solves the secret sharing problem between the sender and the last hop, and increases the reliability and security of the overall system. When a sender communications with a recipient, this novel protocol is able to dynamically adjust other safer links to complete the communication, if a small quantity of communication links is attacked. Various evaluations via present attack models demonstrate that the new protocol, able to achieve recipient anonymity when reducing the communication delay, is superior in security and reliability over the traditional anonymous communications.

Density-based clustering algorithms, which are important algorithms for the task of class identification in spatial database, have many advantages such as no dependence on the number of clusters, ability to discover clusters with arbitrary shapes and handle noise. However, clustering quality of most density-based clustering algorithms degrades when the clusters are of different densities. To address this issue, this paper brings forward a clustering algorithm based on characteristics of density distribution--CCDD algorithm. Firstly, it divides data space into a number of grids. Secondly, it re-divides data space into many smaller partitions, according to each grid's one-dimensional or multi-dimensional characteristics of density distribution. Finally, it uses an improved DBSCAN algorithm, which chooses different parameters according to each partition's local density, to cluster respectively. The experimental results show that CCDD algorithm, which is superior in quality and efficiency to DBSCAN algorithm, can find clusters with arbitrary shapes and different densities in spatial databases with noise.