434 research outputs found

    Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

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    As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201

    The effects of internet shoppers' trust on their purchasing intention in China

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    Owing to the rapid development of the Internet and information technology in China, the growth of consumers' purchasing activities in Internet shopping malls has been truly phenomenal in recent years. Taobao.com, Ebay.com.cn, and Paipai.com have 67,360,000 customer to customer (C2C) users and 99% of the market share in China's C2C market (www.163.com). Dangdang.com and Joyo.com have occupied 87% of the business to customer (B2C) market with 58,360,000 users (www.sohu.com). Because of these significant numbers of users, it is important to understand what affects Chinese consumers' decisions to purchase in Internet shopping malls. Based on past studies, trust is considered a key factor affecting a Chinese consumer's purchasing intention. The purpose of this study is to investigate the effects of Chinese shoppers' trust on their purchasing intention in Internet shopping malls. In order to accomplish the purpose of this study, we developed a research model. This model suggests that there exists a significant relationship between trust and purchasing intention. According to this model, on purchasing intention, trust also mediates effects of other independent variables such as e-commerce knowledge, perceived reputation, perceived risk, and perceived ease of use. The results of this study show that the relationships between these variables are all significant except that between trust and perceived reputation. This research confirms the significant effects of Chinese shoppers' trust on purchasing intention. Implications of these findings are discussed for researchers and practitioners

    A study on the failure prediction of composite laminates in bending

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    Failure prediction for composite materials under given loading conditions is important for efficient design in structural applications. Over the past several decades, there are numerous failure criteria proposed to more accurately predict the failure composite laminates. A lot of research was conducted to evaluate and validate the failure prediction capability for failure criteria. The most failure criteria are studied for in-plane loading conditions. Mechanical behavior of composite laminates varies depending on the loading conditions. Even if failure criterion is accurate under the in-plane loads, it cannot be accurate for out-of-plane loads such as bending. In many industrial structures, composite laminates is under out-of-plane load as well as in-plane loads. For the structural stability of the composite structures, it is important to accurately predict failure of composite laminates under bending. In this study, the failure prediction of composite laminates under bending is investigated. The non-linear finite element analysis using Arc-length method is performed. 2D strain-based interactive failure theory [1] that is more accurately final failure of composite laminate under multi-axial loading is applied to predict the final failure of composite laminates under bending. In order to compare the accuracy of the failure predictions, a 3-point bending test are performed for un-symmetric cross-ply [0/90]8 and quasi-isotropic [0/±45/90]2s composite laminates. Also, it is compared with the other failure criteria such as maximum strain, maximum stress and Tsai-Wu theories. Finally, the predicted results using 2D strain-based interactive failure theory more agree well with the experiment than other failure theories. Acknowledgements This work was supported under the framework of Aerospace Technology Development Program (No. 10074270, Development of Manufacturing Core Technology for 3-Dimnesional Woven Integrated Composite Wing Structure of 5,000 Pound VLJ Aircraft) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) This work was supported by the New & Renewable Energy Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20143030021130) References [1] S. Y. Lee and J. H. Roh, “Two-dimensional strain-based interactive failure theory for multidirectional composite laminates,” Composite Part B: Engineering, vol. 69, pp.69-75, 2015
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