A Survey on malware analysis and mitigation techniques

In recent days, malwares are advanced, sophisticatedly engineered to attack the target. Most of such advanced malwares are highly persistent and capable of escaping from the security systems. This paper explores such an advanced malware type called Advanced Persistent Threats (APTs). APTs pave the way for most of the Cyber espionages and sabotages. APTs are highly sophisticated, target specific and operate in a stealthy mode till the target is compromised. The intention of the APTs is to deploy target specific automated malwares in a host or network to initiate an on-demand attack based on continuous monitoring. Encrypted covert communication and advanced, sophisticated attack techniques make the identification of APTs more challenging. Conventional security systems like antivirus, anti-malware systems which depend on signatures and static analysis fail to identify these APTs. The Advanced Evasive Techniques (AET) used in APTs are capable of bypassing the stateful firewalls housed in the enterprise choke points at ease. Hence, this paper presents a detailed study on sophisticated attack and evasion techniques used by the contemporary malwares. Furthermore, existing malware analysis techniques, application hardening techniques and CPU assisted application security schemes are also discussed. Finally, the study concludes by presenting the System and Network Security Design (SNSD) using existing mitigation techniques.     

POLYPHONET: An advanced social network extraction system from the Web

Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, to detect groups of persons, and to obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.

Several studies have used search engines to extract social networks from the Web, but our research advances the following points: first, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social network mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Iterative Social Network Mining is proposed. It utilizes simple modules using Google and is characterized by scalability and relate–identify processes: identification of each entity and extraction of relations are repeated to obtain a more precise social network.     

Forecasting managerial turnover through e-mail based social network analysis

In this study we propose a method based on e-mail social network analysis to compare the communication behavior of managers who voluntarily quit their job and managers who decide to stay. Collecting 18 months of e-mail, we analyzed the communication behavior of 866 managers, out of which 111 left a large global service company. We compared differences in communication patterns by computing social network metrics, such as betweenness and closeness centrality, and content analysis indicators, such as emotionality and complexity of the language used. To study the emergence of managers’ disengagement, we made a distinction based on the period of e-mail data examined. We observed communications during months 5 and 4 before managers left, and found significant variations in both their network structure and use of language. Results indicate that on average managers who quit had lower closeness centrality and less engaged conversations. In addition, managers who chose to quit tended to shift their communication behavior starting from 5 months before leaving, by increasing their degree and closeness centrality, the complexity of their language, as well as their oscillations in betweenness centrality and the number of “nudges” they need to send to peers before getting an answer.     

A social network analysis of ICTD conferences (2006–2017)

ABSTRACT

Information and Communication Technologies for Development is an interdisciplinary area of research associated with engineering, application, and adoption of ICTs in developing regions and/or for development. The International Conference on Information and Communication Technologies and Development (ICTD) was started in 2006 with the objective to build a community of scholars and practitioners from diverse disciplinary backgrounds. In this paper, we examine the social dimension of ICTD as manifest in co-authorship ties in the papers published in conference proceedings. This research community has 1053 unique authors from 302 institutions in 55 countries. Almost 85% of 456 papers are co-authored by two or more people. Initially, the research community displayed small-world characteristics but the social network subsequently displays a distinct core-periphery structure. Further, collaborative ties among academic institutions in developing countries are comparatively less. A key implication is that institutional support is imperative to initiate and maintain collaborative research ties.     

Individuals’ personal network characteristics and patterns of Facebook use: A social network approach

Using the theoretical framework of ego-centric networks, this study examines the associations between the characteristics of both Facebook-specific and pre-existing personal networks and patterns of Facebook use. With data from an ego-network survey of college students, the study discovered that various dimensions of Facebook-specific network characteristics, such as multiplexity, proximity, density, and heterogeneity in race, were positively associated with usage patterns, including time spent on Facebook, posting messages, posting photos, and lurking. In contrast, network characteristics of pre-existing relationships, such as density and heterogeneity in race, were negatively associated with Facebook usage patterns. Theoretical implications and limitations were discussed.     

Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance

Social networks and gamification are having an important and growing role in education. Social networks provide unknown communication and connection possibilities while games have the potential to engage students. This paper analyzes the structure of the social network resulting from a gamified social undergraduate course as well as the influence that student's position has on learning achievement. In a semester long experiment, a social networking site was delivered to students providing gamified activities and enabling social interaction and collaboration. Social network analysis was used to build the network graph and to compute four measures of the overall network and nine measures for each participant. Individual measures were then assessed as predictors of students' achievement using three different methods: correlation, principal component analysis and multiple linear regressions. The resulting social network has 167 actors and 2505 links, and it can be characterized as a small-world. All analyses agreed on the potential of structural metrics as predictors of learning achievement but they differ in the measures considered as significant. A moderate correlation was found between most centrality measures and learning achievement.     

The contribution of social network sites to exposure to political difference: The relationships among SNSs, online political messaging, and exposure to cross-cutting perspectives

The increasing popularity of social network sites (SNSs) has raised questions about the role of social network media in the democratic process. This study explores how use of SNSs influences individuals’ exposure to political difference. The findings show a positive and significant relationship between SNSs and exposure to challenging viewpoints, supporting the idea that SNSs contribute to individuals’ exposure to cross-cutting political points of view. Partisanship was not found to interact with SNS use, suggesting that SNSs contribute to expanding exposure to dissimilar political views across individuals’ partisanship. Online political messaging also has a direct effect on exposure to dissimilar viewpoints, and it mediates the association between SNSs and exposure to cross-cutting political views.     

An assessment of equivalence between paper and social media surveys: The role of social desirability and satisficing

Investigation of the underlying mechanisms responsible for measurement variance has received little attention. The primary objective of this study is to examine whether paper and social media surveys produce convergent results and investigate the underlying psychological mechanisms for the potential measurement nonequivalence. Particularly, we explored the role of social desirability and satisficing on the measurement results. We collected data via five different survey modes, including paper survey, ad hoc Web survey, online forum (message boards)-based, SNS-based and microblog-based surveys. The findings show that socially desirable responding does not lead to inconsistent results. Rather we found that satisficing causes inconsistent results in paper versus online surveys. Sociability reduces the possibility of engaging in satisficing that results in inconsistent results between traditional Web surveys and social media-based Web surveys.     

融合社会关系的社交网络情感分析综述

随着以用户为中心的Web 2.0的发展,社交网络平台以惊人的影响力渗入到生活的方方面面,对社交网络中的内容进行情感分析已经成为热点研究课题.Twitter、新浪微博等在线社交网站吸引了大量用户,通过用户间的交互,产生了许多包含用户间社会关系的信息,并且这些社会关系被广泛应用于社交网络的情感分析.融合社会关系的社交网络情...     

Topic discovery innovations for sustainable ultra-precision machining by social network analysis and machine learning approach

Ultra-precision machining (UPM) is an advanced manufacturing technology that experiences increasing demand. Therefore, it is necessary to minimize the environmental impacts from its enormous consumptions of resources. Achieving sustainable UPM is still a challenge is it involves complicated influencing relationships among relevant factors like energy consumption, and human health, which could affect sustainable performance. And some influencing relationships between two parameters have not been fully studied yet, which are named as undiscussed two-parameter relationships. Therefore, this paper proposed a new topic discovery model based on social network analysis (SNA) and machine learning approach to discover the undiscussed two-parameter relationships with high potential value in the sustainable UPM research field. By using the link prediction metrics obtained by SNA and principal components analysis in this study, the interactive relationships among the parameters of sustainable ultra-precision machining are determined to discover the potential values of undiscussed two-parameter topics. Then, the k-means algorithm is applied to classify the topics based on the similarity of the metrics results to present the potential value distribution of the undiscussed topics in sustainable UPM. From the metrics results, the topic of the relationship between environmental damage and resource waste was found to be the most valuable potential two-parameter topic in the area of sustainable UPM. This paper also contributes to showing the potential value distribution of undiscussed two-parameter relationships and predicting the sustainable development trend in the UPM sectors.     

Flink: Semantic Web technology for the extraction and analysis of social networks

We present the Flink system for the extraction, aggregation and visualization of online social networks. Flink employs semantic technology for reasoning with personal information extracted from a number of electronic information sources including web pages, emails, publication archives and FOAF profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a web-based presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community.     

BSN: An automatic generation algorithm of social network data

In recent years, there has been considerable interest in the analysis of social network data. In this paper, we propose a novel automatic generation algorithm of social network data - the Biclustering Algorithm for Social Network Data algorithm. The algorithm introduces biclustering to social network analysis for automatic identification of associations among a group of actors and entities. The algorithm is different from existing ones in that it employs a combination of min-max and pattern searching procedures to construct hierarchical biclusters and discover the relationships among these actors, in order to easily interpret social network data. The algorithm is not subject to convexity limitations, and does not need to use derivatives information.     

The reciprocal effects of social network site use and the disposition for self-disclosure: A longitudinal study

Since the advent of social network sites (SNSs), scholars have critically discussed the psychological and societal implication of online self-disclosure. Does Facebook change our willingness to disclose personal information? The present study proposes that the use of SNSs and the psychological disposition for self-disclosure interact reciprocally: Individuals with a stronger disposition show a higher tendency to use SNSs (selection effect). At the same time, frequent SNS use increases the wish to self-disclose online, because self-disclosing behaviors are reinforced through social capital within the SNS environment (socialization effect). In a longitudinal panel study, 488 users of SNSs were surveyed twice in a 6 months interval. Data were analyzed using structure equation modeling. The proposed reciprocal effects of SNS activities and self-disclosure were supported by the data: The disposition for online self-disclosure had a positive longitudinal effect on SNS use which in turn positively influenced the disposition for online self-disclosure. Both effects were moderated by the amount of social capital users received as a consequence of their SNS use.     

Social network sites influence recovery from social exclusion: Individual differences in social anxiety

Social network sites (SNSs) have been found to be closely associated with responses to social exclusion and to impact people with high or low levels of social anxiety in different ways. Our study tested whether social anxiety, an individual difference variable, affected the association between SNSs and responses to social exclusion. A Cyberball game was conducted to create social exclusion, followed by a waiting period during which participants were observed to see whether they would choose to use SNSs. Afterwards, recovery from participants' negative responses to exclusion was measured. Results showed that using SNSs benefited the highly socially anxious (HSA) group in terms of recovering from disconnection and feeling a sense of meaningful existence more than the low socially anxious (LSA) group. However, recovery from disconnection was lower for LSA individuals who used SNSs than those who did not use SNSs. Our research suggests that SNSs benefited HSA individuals after social exclusion, but hindered the recovery of LSA individuals.     

Predicting teamwork results from social network analysis

Modelling students' behaviours has reached a status that can only be overcome by improving the ability of predicting the results on teamwork. Indeed, teamwork is an important piece on the learning process, but understanding their mechanisms and predicting the results achieved is far from being solved by traditional classifiers. In this paper, we address the problem of predicting teamwork results, and propose a recommender system that suggests new teams, in the context of a given curricular unit. Any student, who is looking for a team, may use the system; in particular, he may ask for the best team to join, either considering all available colleagues or just the set of his previous teammates. Our system makes use of social network analysis and classification methods as the algorithmic core of the decision‐making process. System evaluation is presented through a set of experimental results, which report the performance of social network analysis and classification algorithms over real datasets.     

Digitising the R&D social network: revisiting the technological gatekeeper

This paper examines how the digitisation of the social network, and the resulting interplay between its online and offline components, has impacted the role of the technological gatekeeper in research and development (R&D) settings. Previous studies have firmly established the technological gatekeeper to be a key node in the innovation process – acquiring, translating and disseminating novel information throughout the R&D social network. Drawing on social network analysis and interview evidence from a software R&D group, we find that the gatekeeper role has undergone a division of labour. Theoretically, we contribute to the body of knowledge by developing an updated technological gatekeeper conceptual framework. For practitioners, we identify the competencies exhibited by the small number of communication specialists who are largely responsible for diffusing novel information. We then advise practitioners how to maximise the contribution of these ‘stars’ to the information flow network.     

Forecasting social CRM adoption in SMEs: A combined SEM-neural network method

The growth of social media usage questions the old-style idea of customer relationship management (CRM). Social CRM strategy is a novel version of CRM empowered by social media technology that offers a new way of managing relationships with customers effectively. This study aims to forecast the predictors of social CRM strategy adoption by small and medium enterprises (SMEs). The proposed model used in this study derived its theoretical support from IT/IS, marketing, and CRM literature. In the proposed Technology-Organization-Environment-Process (TOEP) adoption model, several hypotheses are developed which examine the role of Technological factors, such as Cost of Adoption, Relative Advantages, Complexity, and Compatibility; Organizational factors, such as IT/IS knowledge of employee, and Top management support; Environmental factors such as Competitive Pressure, and Customer Pressure; and Process factors such as Information Capture, Information Use, and Information Sharing; all having a positive relationship with social CRM adoption. This research applied a following two staged SEM-neural network method combining both structural equation modelling (SEM) and neural network analyses. The proposed hypothetical model is examined by using SEM on the collected data of SMEs in Kuala Lumpur, the central city of Malaysia. The SEM approach with a neural network method can be used to investigate the complicated relations involved in the adoption of social CRM. The study finds that compatibility, information capture, IT/IS knowledge of employee, top management support, information sharing, competitive pressure, cost, relative advantage, and customer pressure are the most important factors influencing social CRM adoption. Remarkably, the results of neural network analysis show that compatibility and information capture of social CRM are the most significant factors which affect SMEs' adoption of this form of customer relationship management. The outcomes of this research benefit executives' decision-making by identifying and ranking factors that enable them to discover how they can advance the usage of social CRM in their firms. Furthermore, the findings of this study can help the managers/owners of SMEs assign their resources, according to the ranking of social CRM adoption factors, when they are making plans to adopt social CRM. This study differs from previous studies as it proposes an innovative new approach to determine what influences the adoption of social CRM. By proposing the TOEP adoption model, additional information process factors advance the traditional TOE adoption model.     

On consensus reaching process based on social network analysis in uncertain linguistic group decision making: Exploring limited trust propagation and preference modification attitudes

This paper explores a limited trust propagation-based consensus model considering individual attitude for preference modification in a social networked setting with uncertain preference information. To examine the construction of complete linkages, and the status of decision makers in group decision making, it is assumed that the group size and network density will affect the scale of mediators in the propagation process, then a definition of limited trust propagation is proposed and the propagation efficiency can be introduced. On this basis, we obtain missing trust relationships and individual centrality in network. In the process of consensus reaching, both the decision maker’s original preference and recommendation advice are considered for flexibly modeling the preference modification process: the individual attitude toward modification is determined by a newly introduced measure of comprehensive relative out-degree centrality, showing the degree of willingness to adjust assessments. When the willingness is too low to reach the preset consensus level, a multi-objective programming model is designed to improve the consensus as much as possible. Moreover, the proposed feedback mechanism narrows the individual acceptable modification range based on the previous adjustment rule, so as to simulate the personalized and targeted decision behavior. To guarantee obtaining a collective aggregated preference in a logical and precise manner, a two-stage optimization model composing of comprehensive relative in-degree centrality-based information aggregation and best consistency-based uncertainty elimination, is proposed. A numerical example and comparative analyses are performed to show the validity and feasibility of the proposed model.     

Gratifications and social network service usage: The mediating role of online experience

This paper incorporates dual theories from communication research (uses and gratifications) and psychology research (online flow) to examine consumer behavior in the use of social network services. In particular, the study proposes that consumers’ online experience of interaction and arousal serves as the mediator of the relationship between social motivations and use behaviors. The empirical results indicate that arousal fully mediates the relationship between social gratifications and problematic social network service use. Furthermore, both interaction and arousal are partial mediators of the relationship between social gratifications and the intention to revisit social networking websites.     

Intention to purchase on social commerce websites across cultures: A cross-regional study

This study investigates the moderating effect of culture and the mediating effect of trust in a social network community on the relationship between social interactions and purchase intention. Trust transference theory, social interactions, and Hofstede's cultural dimensions are considered. The findings from the research support the moderating effect of culture and the mediating role of trust in a social network community on the relationship between social interactions (in terms of closeness and familiarity) and intention to purchase in social commerce environments. In addition, the mediating effect of trust in a social network community is conditional on culture. The findings also suggest that trust in a social network community may be attributed to the closeness and familiarity developed among its members resulting from social interactions. The results presented herein are in line with the trust transference theory. The empirical study results also suggest that social commerce market penetration in East Asia may have relatively fewer barriers to overcome and be more likely to succeed than in the Latin America region.