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

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

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Insights and Challenges about the use of VNA on Airport/Hinterland Linkages

Airport operators, planners and regulatory agencies to measure the economic contribution of an airport to its local and regional surroundings, frequently use economic impact studies. The most common methods to measure airport economic impacts have been the Input-Output method, the Collection of Benefits method and most recently the Catalytic method. The most used measured variables include employment, wages, local and regional spending and air traffic levels. This paper is a new approach to these impact studies in which is used a new tool to identify the added values generated within airports and surrounding community interactions to better catch real socio-economic impacts. The VNA – Value Network Analysis, is used as an integrated methodology to identify these interactions and added values generated (tangibles and intangibles) in the business system of landside airports. To define the system it is used the matrix key airport performance benchmarking areas of ACI (Airport Council International) that are in the range of landside of the airport. Key words: Social Networks, Airport Landside, Value Network Analysis, Key Performance Indicators, Business System.

Not quite what’s on paper? Comparison between theoretical and actual information-sharing networks in the Ugandan rural water service sector

Under Uganda’s decentralised system, rural water service delivery remains to some extent problematic. Several studies attribute the possible causes of deficiencies in the water sector to governance issues. This article applies social network analysis to map upward and downward water-related information flows between the actors of local government from village to district level. Comparing the actual information-sharing network with what’s on paper reveals a less reciprocal and more centralised network than that theoretically envisaged. Some actors, such as the district water officer, are more central than expected in terms of sending and receiving information, while others seem to underperform. Our findings show, however, that it is not the political–administrative information exchange which is the biggest obstacle, but rather information flows between higher (district and sub-county) and lower (parish and village) levels of the local governance structure. Adding water users to the analysis reveals the village chairperson as the most crucial broker of information upward to duty bearers at district level. The limited role of water user committees also becomes apparent. The authors conclude that information communication technology holds potential to overcome some of the bottlenecks (eg distance) hindering the flow of water-related information between actors at different levels

Social networks and performance in distributed learning communities

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance students' success and performance. We used a monitoring system for social network data gathering. Results from correlation analyses showed that students' social network characteristics are related to their performancePostprint (published version

Social network analysis of knowledge transfer in sustainable office building projects in the UK and Germany

Sustainability is fundamentally transforming construction industries worldwide, resulting in an increased complexity of construction projects with a more divergent set of actors involved. A seamless transfer of knowledge between these actors is required. The gap between the performance of green buildings as designed and as built could be interpreted as an indication that this transfer is not immaculate. Nowadays almost every actor involved in the construction process claims to strive for sustainability. However, the way they perceive and translate it into practice varies widely between different project participants. Therefore a better understanding of how knowledge on sustainable construction is transferred and adopted is needed. A subsequent enhancement of this process could support a certain standard of sustainable building quality. Previous research indicated that social networks influence knowledge transfer (KT), as knowledge is personal and KT takes place through interaction of individuals. Moreover, social network analysis (SNA) provides the means to map the knowledge flow in a project environment and thus enables an understanding of how to enhance it. As a result SNA was used to compare KT practices in construction teams delivering office buildings to sustainable building standards in Germany and the UK. A literature review led to the establishment of a conceptual framework that characterizes the KT process. This was used to inform the research design, data collection and analysis. The research was carried out using a multiple case study approach. The data collection tools were mainly questionnaires with a combination of quantitative, qualitative and social network data. The data was analysed using a combination of descriptive statistics, cross tabulations, content analysis and SNA. The findings were used to revise the conceptual framework. The findings showed a lack of awareness and knowledge of sustainable construction. Moreover, analysis of the data concluded that KT on sustainable construction is influenced by so-called general enhancers/ inhibitors, such as age group and job level, and social network characteristics. Furthermore the results suggest benefits could be derived from employing a sustainability manager as a key contact and to enhance KT on sustainable construction. This research contributes to literature on KT in sustainable construction project teams from a social network perspective. It is the first of its kind comparing KT in construction teams delivering sustainable office buildings in Germany and the UK. The framework is the most important output of this research in terms of both contribution to knowledge and practice and can be used to support the examination of KT in sustainable construction projects. Furthermore this study facilitates the understanding of knowledge contents and types of sustainable construction knowledge