No abstract

As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding skyline uncertain and not unique. This paper investigates the Pr-Skyline problem, i.e., how to compute the skyline with the highest existence probability in a computational and energy-efficient way. We formulate the problem and prove that it is NP-Complete and cannot be approximated in a given expression. However, the proposed algorithm SKY-SEARCH with pruning techniques can guarantee the computational efficiency given relatively large input size, while the filter-based distributed optimization strategy significantly reduces the transmission cost and the required storage space of the sensor nodes. Extensive experiments verify the efficiency and scalability of SKY-SEARCH and the distributed optimizing strategy.

Traditional signature-based detection methods fail to detect unknown malwares, while data mining methods for detection are proved useful to new malwares but suffer for high false positive rate. In this paper, we provide a novel hybrid framework called HRS based on the analysis for 50 millions of malware samples across 20,000 malware classes from our antivirus platform. The distribution of the samples are elaborated and a hybrid framework HRS is proposed, which consists of Hash-based, Rule-based and SVM-based models trained from different classes of malwares according to the distribution. Rule-based model is the core component of the hybrid framework. It is convenient to control false positives by adjusting the factor of a boolean expression in rule-based method, while it still has the ability to detect the unknown malwares. The SVM-based method is enhanced by examining the critical sections of the malwares, which can significantly shorten the scanning and training time. Rigorous experiments have been performed to evaluate the HRS approach based on the massive dataset and the results demonstrate that HRS achieves a true positive rate of 99.84% with an error rate of 0.17%. The HRS method has already been deployed into our security platform.