Items where author is affiliated with Chinese Academy of Sciences

Image spam is a new obfuscating method which spammers invented to more effectively bypass conventional text based spam filters. In this paper, we extract local invariant features of images and run a one-class SVM classifier which uses the pyramid match kernel as the kernel function to detect image spam. Experimental results demonstrate that our algorithm is effective for fighting image spam.

Robust statistical learning based web spam detection sys- tem often requires large amounts of labeled training data. However, labeled samples are more difficult, expensive and time consuming to obtain than unlabeled ones. This pa- per proposed link based semi-supervised learning algorithms to boost the performance of a classifier, which integrates the traditional Self-training with the topological dependency based link learning. The experiments with a few labeled samples on standard WEBSPAM-UK2006 benchmark showed that the algorithms are effective.

This paper presents an end-user-oriented programming environment called Mashroom. Major contributions herein include an end-user programming model with an expressive data structure as well as a set of formally-defined mashup operators. The data structure takes advantage of nested table, and maintains the intuitiveness while allowing users to express complex data objects. The mashup operators are visualized with contextual menu and formula bar and can be directly applied on the data. Experiments and case studies reveal that end users have little difficulty in effectively and efficiently using Mashroom to build mashup applications.

In this poster, based on our previous work in building a lightweight DDoS (Distributed Denial-of-Services) attacks detection mechanism for web server using TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) and genetic algorithm based instance selection methods, we further propose a more efficient and effective instance selection method, named E-FCM (Extend Fuzzy C-Means). By using this method, we can obtain much cheaper training time for TCM-KNN while ensuring high detection performance. Therefore, the optimized mechanism is more suitable for lightweight DDoS attacks detection in real network environment. In our previous work, we proposed an effective anomaly detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm to fulfill DDoS attacks detection task towards ensuring the QoS of web server. The method is good at detecting network anomalies with high detection rate, high confidence and low false positives than traditional methods, because it combines “strangeness” with “p-values” measures to evaluate the network traffic compared to the conventional ad-hoc thresholds based detection and particular definition based detection. Secondly, we utilize the new objective measurement as the input feature spaces of TCM-KNN, to effectively detect DDoS attack against web server. Finally, we introduce Genetic Algorithm (GA) based instance selection method to boost the real-time detection performance of TCM-KNN and thus make it be an effective and lightweight mechanism for DDoS detection for web servers [4, 5]. However, we found the computational cost for GA is expensive, which results in high training time for TCM-KNN.