Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

DeepDefense: Identifying DDoS Attack via Deep Learning

Abstract: Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal threats to the Internet. Automatically detecting DDoS attack packets is one of the main defense mechanisms. Conventional solutions monitor network traffic and identify attack activities from legitimate network traffic based on statistical divergence. Machine learning is another method to improve identifying performance based on statistical features. However, conventional machine learning techniques are limited by the shallow representation models. In this paper, we propose a deep learning based DDoS attack detection approach (DeepDefense). Deep learning approach can automatically extract high-level features from low-level ones and gain powerful representation and inference. We design a recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities. The experimental results demonstrate a better performance of our model compared with conventional machine learning models. We reduce the error rate from 7.517% to 2.103% compared with conventional machine learning method in the larger data set.

The influence of scope, depth, and orientation of external technology sources on the innovative performance of Chinese firms

Abstract: It is commonly accepted nowadays that external knowledge sources are important for firms’ innovative performance. However, it is still not clear, what dimensions of firms’ external knowledge search strategy are crucial in determining their innovation success and whether these search strategies are contingent on different innovation modes. In this study, we analyze how the innovative performance is affected by the scope, depth, and orientation of firms’ external search strategies. We apply this analysis to firms using STI (science, technology and innovation) and DUI (doing, using and interacting) innovation modes. Based on a survey among firms in China, we find that greater scope and depth of openness for both innovation modes improves innovative performance indicating that open innovation is also relevant beyond science and technology based innovation. Furthermore, we find that decreasing returns in external search strategies, suggested by Laursen and Salter (2006), are not always present and are contingent on the innovation modes. Next, we find that the type of external partners (we label it “orientation of openness”) is crucial in explaining innovative performance and that firms using DUI or STI innovation modes have different sets of relevant innovation partners. This shows that the orientation of openness is an important dimension – in addition to the scope and depth of openness. As respondents are located in China, this study provides evidence that open innovation is also relevant in developing countries.