Earticle

IP address spoofing is employed by a lot of DDoS attack tools. Most of the current research on DDoS attack packet filtering depends on cooperation among routers, which is hard to achieve in real campaigns. Therefore, in the paper, we propose a novel filtering scheme based on source information in this paper to defend against various source IP address spoofing. The proposed method works independently at the potential victim side, and accumulates the source information of its clients, for instance, source IP addresses, hops from the server during attacks free period. When a DDoS attack alarm is raised, we can filter out the attack packets based on the accumulated knowledge of the legitimate clients. We divide the source IP addresses into )321(≤≤nn segments in our proposed algorithm; as a result, we can therefore release the challenge storage and speed up the procedure of information retrieval. The system which is proposed by us and the experiments indicated that the proposed method works effectively and efficiently.

This paper addresses an indexing scheme capable of e±- ciently processing range queries in a large-scale trajectory database. Af-ter discussing the drawbacks of previous indexing schemes, we propose a new scheme that divides the temporal dimension into multiple time in-tervals and then, by this interval, builds an index for the line segments. Additionally, a supplementary index is built for the line segments within each time interval. This scheme can make a dramatic improvement in the performance of insert and search operations using a main memory index, particularly for the time interval consisting of the segments taken by those objects which are currently moving or have just completed their movements, as contrast to the legacy schemes that store the index totally on the disk.

The B+-tree was proposed to support sequential processing for the B-tree. To the extent of authors’ knowledge, however, there have been no studies supporting sequential processing in multidimensional point access methods(PAMs). To do this, the cells in a multilevel and multidimensional space managed by a multidimensional PAM must be linearly ordered systematically. In this paper, we present ideas for an approach that linearly orders cells in a multilevel and multidimensional space. Then, we present ideas for a novel sequential processing algorithm for region queries using this approach.

How to grasp the real-time overall situation of the network security is worth to study. An increasing number of network security systems have been deployed in the backbone and the gateways of enterprises, including various Netflow systems, IDS, VDS, VS and firewalls. These products make great contributions in enhancing the network security. However, current network security systems are independent and autonomous. Consequently, such solutions cannot figure out an overview of the network security situation. In another perspective, building a new global monitoring system will suffer from redundant construction and longer deploying time. We propose a novel and high assurance solution called GS-TMS which reuses the log data generated by the existing systems. Based on the data stream and data integration technologies, GS-TMS provides a desirable capability in quickly building a large-scale distributed network monitoring system. Furthermore, GS-TMS has additional notable advantages over current monitoring systems in scalability and flexibility.

Hospitals within a Telemedicine system would like to share their private local database with other hospitals. However, they do not agree to keep a copy of their database into a central server. The central repository (data warehouse) model is not secure because too much control was granted to the central site. In order to fully utilize the distributed and heterogeneous resources, a secure and privacy preserved model should be used. We proposed a secure data sharing model to facilitate this problem.

In this paper we propose a new biometric-based Iris feature extraction system. The system automatically acquires the biometric data in numerical format (Iris Images) by using a set of properly located sensors. We are considering camera as a high quality sensor. Iris Images are typically color images that are processed to gray scale images. Then the Feature extraction algorithm is used to detect “IRIS Effective Region (IER)” and then extract features from “IRIS Effective Region (IER)” that are numerical characterization of the underlying biometrics. Later on this work will be helping to identify an individual by comparing the feature obtained from the feature extraction algorithm with the previously stored feature by producing a similarity score. This score will be indicating the degree of similarity between a pair of biometrics data under consideration. Depending on degree of similarity, individual can be identified.