A Novel Worm Detection Model Based on Host Packet Behavior Ranking
Traditional behavior-based worm detection can't eliminate the influence of the worm-like P2P traffic effectively, as well as detect slow worms. To try to address these problems, this paper first presents a user habit model to describe the factors which influent the generation of network traffic, then a design of HPBRWD (Host Packet Behavior Ranking Based Worm detection) and some key issues about it are introduced. This paper has three contributions to the worm detection: 1) presenting a hierarchical user habit model; 2) using normal software and time profile to eliminate the worm-like P2P traffic and accelerate the detection of worms; 3) presenting HPBRWD to effectively detect worms. Experiments results show that HPBRWD is effective to detect worms.

title: ASG Automated Signature Generation for Worm-Like P2P Traffic Patterns
abstract: Many P2P software have the similar communication patterns with computer worms, thus they will bring in false positives for behaviour based worm detection. Up to now, little work is done on the research of the similarities between communication patterns of worm and P2P software as well as how to eliminate the worm-like P2P traffic. Based on the analysis of popular P2P software used nowadays and the host process information, this paper presents ASG, which is a novel host based algorithm to generate signatures for worm-like P2P communication patterns. The contribution of our work lies in three aspects: a) Analyzing communication pattern similarities between P2P traffic and worm traffic through examples. b) Designing one practical and simple signature format for worm-like P2P traffic based on the host process information, c) Presenting automated signature generation (ASG) method to extract the signature of worm-like P2P traffic. Experiments with the popular used P2P software show that ASG can effectively extract the signature and reduce the false positives.

title: Performance Analysis of the HLLACF
abstract: With the popular using of anonymous communication systems, security and overhead traffic attract more attention. HLLCAF was presented to improve the performance of anonymous communication systems. This paper analyzes HLLACF's security and an evaluation algorithm for security is presented. In the end, a simulation experiment and result analysis is given. The theoretic analysis and simulation experiment indicate that the HLLACF can prevent AS-Level passive attack and other similar attacks well while decreasing communication delay and HLLACF also scales well.

title: ALHACF: An Anonymity-Level Selected Hierarchical Anonymous Communication Framework
abstract: With the more emphasis paid on network security and privacy, the research on anonymous communication system has developed quickly. In the past, researchers mainly focused on how to improve the anonymity, but few focused on the usability. This paper presents an anonymity-level selected hierarchical anonymous communication framework (ALHACF). In this framework, firstly the anonymity-level is selected by users according to his requirement, and then the path length and set of relay routers will be determined by the anonymity-level. The anonymity and communication performance of ALHACF are analyzed. The analysis indicates that ALHACF inherits the merit of HLLACF, and it can prevent some kinds of attack, keep good anonymity and increase the communication performance markedly.

title: Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images
abstract: Region of interest (ROI) is a region used to extract features. In breast ultrasound (BUS) image, the ROI is a breast tumor region. Because of poor image quality (low SNR (signal/noise ratio), low contrast, blurry boundaries, etc.), it is difficult to segment the BUS image accurately and produce a ROI which precisely covers the tumor region. Due to the requirement of accurate ROI for feature extraction, fully automatic classification of BUS images becomes a difficult task. In this paper, a novel fully automatic classification method for BUS images is proposed which can be divided into two steps: ''ROI generation step'' and ''ROI classification step''. The ROI generation step focuses on finding a credible ROI instead of finding the precise tumor location. The ROI classification step employs a novel feature extraction and classification strategy. First, some points in the ROI are selected as the ''classification checkpoints'' which are evenly distributed in the ROI, and the local texture features around each classification checkpoint are extracted. For each ROI, all the classification checkpoints are classified. Finally, the class of the BUS image is determined by analyzing every classification checkpoint in the corresponding ROI. Both steps were implemented by utilizing a supervised texture classification approach. The experiments demonstrate that the proposed method is very robust to the segmentation of BUS images, and very effective and useful for classifying breast tumors.

title: A refinement approach to search and constraint satisfaction problem

title: Learning to Classify Documents with Only a Small Positive Training Set
abstract: Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training examples be missing, the number of positive examples available for learning may also be fairly limited due to the impracticality of hand-labeling a large number of training examples. Current PU learning techniques have focused mostly on identifying reliable negative instances from the unlabeled set U. In this paper, we address the oft-overlooked PU learning problem when the number of training examples in the positive set Pis small. We propose a novel technique LPLP (Learning from Probabilistically Labeled Positive examples) and apply the approach to classify product pages from commercial websites. The experimental results demonstrate that our approach outperforms existing methods significantly, even in the challenging cases where the positive examples in Pand the hidden positive examples in Uwere not drawn from the same distribution.

title: Filtering Spam in Social Tagging System with Dynamic Behavior Analysis
abstract: Spam in social tagging systems introduced by some malicious participants has become a serious problem for its global popularizing. Some studies which can be deduced to static user data analysis have been presented to combat tag spam, but either they do not give an exact evaluation or the algorithms’ performances are not good enough. In this paper, we proposed a novel method based on analysis of dynamic user behavior data for the notion that users’ behaviors in social tagging system can reflect the quality of tags more accurately. Through modeling the different categories of participants’ behaviors, we extract tag-associated actions which can be used to estimate whether tag is spam, and then present our algorithm that can filter the tag spam in the results of social search. The experiment results show that our method indeed outperforms the existing methods based on static data and effectively defends against the tag spam in various spam attacks.

title: Incorporating with Recursive Model Training in Time Series Clustering
abstract: Model-based clustering is one of the most important ways for time series data mining. However, the process of clustering may encounter several problems. In this paper, a novel clustering algorithm of time-series which incorporates recursive hidden Markov model(HMM) training is proposed. Our contributions are as follows: 1) We recursively train models and use these model information in the process agglomerative hierarchical clustering. 2) We built HMM of time-series clusters to describe clusters. To evaluate the effectiveness of the algorithm, several experiments are conducted on both synthetic data and real world data. The result shows that the proposed approach can achieve better performance in correctness rate than the traditional HMM-based clustering algorithm

title: Mining data records in Web pages
abstract: A large amount of information on the Web is contained in regularly structured objects, which we call data records. Such data records are important because they often present the essential information of their host pages, e.g., lists of products or services. It is useful to mine such data records in order to extract information from them to provide value-added services. Existing automatic techniques are not satisfactory because of their poor accuracies. In this paper, we propose a more effective technique to perform the task. The technique is based on two observations about data records on the Web and a string matching algorithm. The proposed technique is able to mine both contiguous and non-contiguous data records. Our experimental results show that the proposed technique outperforms existing techniques substantially.