Distributed Denial of Service (DDoS) attack is a serious threat to network security. In order to solve this problem, an effective method of tracing DDoS attack was proposed based on Autonomous System (AS) and Dynamic Probabilistic Packet-Marking (DPPM). In the proposed method, a new scheme of packet marking was designed with setting up two markers as the domain marks and routing tags for inter-domain tracing and in-domain tracing. Domain marks and routing tags were set at the same time using dynamic packet marking methods. Finally, through the path reconstruction on in-domain and inter-domain, the attack node was traced back rapidly. The experimental results show that the proposed algorithm is efficient and feasible, which provides an important basis for the DDoS attack prevention.

This paper proposed a moving target detection method based on the background image set and sparse representation. The method combined the Robust Principal Component Analysis (RPCA) and the image block analysis method based on sparse representation. The authors got a series of background images from a video sequence by RPCA, and combined these background images as the background image set, treating image block as basic unit, and moving target was extracted from input frame by image block analysis method based on sparse representation. The simulation results indicate that when the background illumination mutates, the proposed method can effectively eliminate the impact of environment noise and reduce the false detection rate of target detection.

A novel algorithm for mining user navigation pattern with incremental clustering was presented. Firstly, a new method for expressing user interest was introduced to construct user profile object. Based on the basic concept of ant colony clustering, artificial ants were used to pick up or drop down object to implement clustering by analyzing the similarity with other local regional objects and. Then a mechanism of decomposing clusters was used to form new clusters when users'interests changed. Experimental results show that the method can adaptively and efficiently achieve incremental clustering.