scholarly journals Stopping the Cyberattack in the Early Stage: Assessing the Security Risks of Social Network Users

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Feng ◽  
Qiang Li ◽  
Yuede Ji ◽  
Dong Guo ◽  
Xiangyu Meng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Risk Score ◽  
Personal Information ◽  
Social Behaviors ◽  
Social Engineering ◽  
Security Risk ◽  
Analysis Model ◽  
Detection Techniques ◽  
The Social

Online social networks have become an essential part of our daily life. While we are enjoying the benefits from the social networks, we are inevitably exposed to the security threats, especially the serious Advanced Persistent Threat (APT) attack. The attackers can launch targeted cyberattacks on a user by analyzing its personal information and social behaviors. Due to the wide variety of social engineering techniques and undetectable zero-day exploits being used by attackers, the detection techniques of intrusion are increasingly difficult. Motivated by the fact that the attackers usually penetrate the social network to either propagate malwares or collect sensitive information, we propose a method to assess the security risk of the user being attacked so that we can take defensive measures such as security education, training, and awareness before users are attacked. In this paper, we propose a novel user analysis model to find potential victims by analyzing a large number of users’ personal information and social behaviors in social networks. For each user, we extract three kinds of features, i.e., statistical features, social-graph features, and semantic features. These features will become the input of our user analysis model, and the security risk score will be calculated. The users with high security risk score will be alarmed so that the risk of being attacked can be reduced. We have implemented an effective user analysis model and evaluated it on a real-world dataset collected from a social network, namely, Sina Weibo (Weibo). The results show that our model can effectively assess the risk of users’ activities in social networks with a high area under the ROC curve of 0.9607.

Situation analysis of relationship in social networks based on link entropy

2015 ◽  
Vol 29 (13) ◽  
pp. 1550061 ◽  
Author(s):  
Ke Li ◽  
Hui-Jia Li ◽  
Hao Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Analysis Model ◽  
Situation Analysis ◽  
The Social ◽  
Theoretical View ◽  
Different Types ◽  
The Relationship ◽  
Pair Analysis ◽  
Network Relationship

Since the existence of certain and uncertain characteristics of the relationships between nodes in social network, the study of social features is expanded by combining the set pair analysis and social computing. In this paper, a new method is created to describe nodes relationship situation in social network, i.e. set pair relationship situation, including generalized set pair relationship situation, generalized set pair close situation and generalized set pair loosen situation. In order to analyze the situation in social network, each kind of set pair relation situation are classified. Combining with the complexity of the social network system and the features of connection entropy, generalized connection entropy which used to express the complexity of social networks is proposed. It includes the generalized same entropy, the generalized difference entropy, and the generalized opposite entropy. These different types of entropies can be used to analyze the social network relationship stability from a more theoretical view. Then a situation analysis model and the corresponding algorithm is proposed. Finally the effectiveness of this method in analyzing the relationships in social networks is proved. Thus, our model can be used to reveal the relationship between social network and node state stability efficiently.

Set Pair Social Network Analysis Model

2011 ◽  
Vol 50-51 ◽  
pp. 63-67 ◽  
Author(s):  
Hong Mei Yang ◽  
Chun Ying Zhang ◽  
Rui Tao Liang ◽  
Fang Tian
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Dynamic Problems ◽  
Analysis Model ◽  
Network Information ◽  
The Social ◽  
The Relationship

Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

scholarly journals A psychodiagnostic tool for the structural assessment of the social network in patients with schizophrenia: validity and psychometric properties

2021 ◽  
pp. 77-90
Author(s):  
A. G. Sofronov ◽  
A. E. Dobrovolskaya ◽  
A. V. Trusova ◽  
I. A. Getmanenko ◽  
A. N. Gvozdetckii
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Analysis Model ◽  
Structural Assessment ◽  
Automated Method ◽  
The Social ◽  
Confirmatory Factor ◽  
New Development ◽  
Schizophrenic Patients ◽  
Practical Recommendations

The aim of the study was to develop a new valid psychometric diagnostic tool for a multi-factor social network assessment of schizophrenic patients, called «The structural assessment of the social network of schizophrenic patients». The new development is based on the social network analysis model elaborated by S. L. Phillips (1981) and translated into Russian by Gurovich I. Ya. et al. (2007). The authors of this article additionally developed an algorithm for assessing the activity of patients on social networks of the Internet. Reducing non-informative variables and conducting a confirmatory factor analysis in a sample of 265 observations of schizophrenic patients (F20.0) aged from 18 to 55, recruited in four medical organizations (145 patients admitted to the hospital and 55 outpatients), resulted in determining a four-factor structure of the patients’ social network: «Objective parameters», «Internet activity», «Emotional aspect of social support» and«Reciprocal support». The method has demonstrated high internal and external validity, as well as applicability in the clinical practice in schizophrenia due to the low resource consumption and compact applicability. Measurable factor indicators of the patients’ social networks obtained by using the structural assessment of the social network of schizophrenic patients allow to determine the targets for psychocorrectional interventions and to increase the effectiveness of psychosocial rehabilitation. In addition an automated method for calculating final indicators has been developed, as well as manual, practical recommendations and corresponding printed forms.

scholarly journals Social Networks for Threat Perception and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 5073-5077
Author(s):  
Pragati Dnyaneshwar Bharsakle
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Analytic Solution ◽  
Personal Information ◽  
Identity Theft ◽  
Threat Perception ◽  
Security Risks ◽  
Email Address ◽  
The Social ◽  
Data Analytic

In the current era of massive knowledge, high volumes of valuable knowledge is simply collected and generated. Social networks square measure samples of generating sources of those huge knowledge. Users in these social networks square measure usually coupled by some interdependency like friendly relationship. As these huge social networks continue to grow, there square measure things during which Associate in Nursing individual user needs to seek out common teams of friends so he will suggest a similar teams to alternative users. Many users of social Network are not aware about the number of security risks in networks such as identity theft, privacy violations, sexual harassment etc,. Recent studies says that most of the social network users expose their personal information like their date of birth, email address, phone number, relationship status. If this type of data reached to the wrong person, then person used that information to harm the users. If the children are users of social network, then these risks become serious. In this paper we present an alternative data analytic solution by using pattern matching solution.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

Comparing the social networks of service users with long term mental health needs living in community with those in a general adult in-patient unit

2021 ◽  
pp. 002076402110175
Author(s):  
Roberto Rusca ◽  
Ike-Foster Onwuchekwa ◽  
Catherine Kinane ◽  
Douglas MacInnes
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Patient Group ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Practical Support ◽  
The Social

Background: Relationships are vital to recovery however, there is uncertainty whether users have different types of social networks in different mental health settings and how these networks may impact on users’ wellbeing. Aims: To compare the social networks of people with long-term mental illness in the community with those of people in a general adult in-patient unit. Method: A sample of general adult in-patients with enduring mental health problems, aged between 18 and 65, was compared with a similar sample attending a general adult psychiatric clinic. A cross-sectional survey collected demographic data and information about participants’ social networks. Participants also completed the Short Warwick Edinburgh Mental Well-Being Scale to examine well-being and the Significant Others Scale to explore their social network support. Results: The study recruited 53 participants (25 living in the community and 28 current in-patients) with 339 named as important members of their social networks. Both groups recorded low numbers in their social networks though the community sample had a significantly greater number of social contacts (7.4 vs. 5.4), more monthly contacts with members of their network and significantly higher levels of social media use. The in-patient group reported greater levels of emotional and practical support from their network. Conclusions: People with serious and enduring mental health problems living in the community had a significantly greater number of people in their social network than those who were in-patients while the in-patient group reported greater levels of emotional and practical support from their network. Recommendations for future work have been made.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

Social Networks, Their Basic Factors, as Well as Their Role in Society and the State in the Context of Digital Transformation

2020 ◽  
Vol 144 ◽  
pp. 26-35
Author(s):  
Rem V. Ryzhov ◽  
◽  
Vladimir A. Ryzhov ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Life Support ◽  
Social Institutions ◽  
The State ◽  
Main Task ◽  
Organizational Models ◽  
Control Center ◽  
The Social ◽  
Single Strategy

Society is historically associated with the state, which plays the role of an institution of power and government. The main task of the state is life support, survival, development of society and the sovereignty of the country. The main mechanism that the state uses to implement these functions is natural social networks. They permeate every cell of society, all elements of the country and its territory. However, they can have a control center, or act on the principle of self-organization (network centrism). The web is a universal natural technology with a category status in science. The work describes five basic factors of any social network, in particular the state, as well as what distinguishes the social network from other organizational models of society. Social networks of the state rely on communication, transport and other networks of the country, being a mechanism for the implementation of a single strategy and plan. However, the emergence of other strong network centers of competition for state power inevitably leads to problems — social conflicts and even catastrophes in society due to the destruction of existing social institutions. The paper identifies the main pitfalls using alternative social networks that destroy the foundations of the state and other social institutions, which leads to the loss of sovereignty, and even to the complete collapse of the country.

Factors Affecting Individual Level ERP Assimilation in a Social Network Perspective

2017 ◽  
Vol 25 (3) ◽  
pp. 21-39 ◽  
Author(s):  
Luan Gao ◽  
Luning Liu ◽  
Yuqiang Feng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Interactive Learning ◽  
Influential Factors ◽  
Three Dimensions ◽  
Organizational Level ◽  
Key Factors ◽  
Factors Affecting ◽  
Individual Level ◽  
The Social

Prior research on ERP assimilation has primarily focused on influential factors at the organizational level. In this study, the authors attempt to extend their understanding of individual level ERP assimilation from the perspective of social network theory. They designed a multi-case study to explore the relations between ERP users' social networks and their levels of ERP assimilation based on the three dimensions of the social networks. The authors gathered data through interviews with 26 ERP users at different levels in five companies. Qualitative analysis was used to understand the effects of social networks and interactive learning. They found that users' social networks play a significant role in individual level ERP assimilation through interactive learning among users. They also found five key factors that facilitate users' assimilation of ERP knowledge: homophily (age, position and rank), tie content (instrumental and expressive ties), tie strength, external ties, and centrality.

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