Social Network Security Research Articles

DEVELOPMENT OF A METHOD FOR CALCULATING THE PROTECTION OF PERSONAL DATA FROM THE CENTRALITY OF THE NETWORK

A mathematical model has been developed and a study of the model of personal data protection from network clustering coefficient and data transfer intensity in social networks has been carried out. Dependencies of protection of the system from the size of the system (and from the amount of personal data); information security threats from the network clustering factor. A system of linear equations is obtained, which consists of the equation: rate of change of information flow from social network security and coefficients that reflect the impact of security measures, amount of personal data, leakage rate, change of information protection from network clustering factor, its size, personal data protection. As a result of solving the system of differential equations, mathematical and graphical dependences of the indicator of personal data protection in the social network from different components are obtained. Considering three options for solving the equation near the steady state of the system, we can conclude that, based on the conditions of the ratio of dissipation and natural frequency, the attenuation of the latter to a certain value is carried out periodically, with decaying amplitude, or by exponentially decaying law. A more visual analysis of the system behavior is performed, moving from the differential form of equations to the discrete one and modeling some interval of the system existence.Mathematical and graphical dependences of the system natural frequency, oscillation period, attenuation coefficient are presented. Simulation modeling for values with deviation from the stationary position of the system is carried out. As a result of simulation, it is proved that the social network protection system is nonlinear.

Differential Game Analysis of Enterprises Investing in New Infrastructure and Maintaining Social Network Security Under the Digital Innovation Ecosystem

With the new round of industrial and technological revolution in China, the new normal of the Chinese economy is increasingly in the form of a digital economy characterized by continued growth and development potential. The digital economy has become an important engine driving the growth of the Chinese economy. The digital transformation of enterprises is a hot topic and poses a difficult research problem. More importantly, ensuring the security of digitalization has become a common challenge faced by society. This paper analyzes the value composition of the digital innovation ecosystem and uses differential game theory to explore the optimal decision-making of leading enterprises and following enterprises in terms of digital innovation and social responsibility under different circumstances. The results show that in the digital innovation ecosystem, (1) leading enterprises and following enterprises have the least income, the weakest degree of digital innovation, and the weakest ability to maintain social network security under non-cooperative conditions; (2) overall income will increase over time, and digital innovation and the maintenance of social network security have a positive effect on the development of the digital economy; (3) the government encourages enterprises to develop digital innovation and maintain social network security and provides appropriate subsidies to some extent, which boosts the development of enterprises.

Retraction Note: Image detection of mountain soil loss and human avoidance trajectory based on cloud computing and machine learning

With the continuous progress and development of modern social science management and network security technology in China, the wide application of new-generation technologies such as intelligent robotics learning has made it more and more extensive in the field of analysis. At present, it has fully played an important technical leading role in many application fields, such as analysis methods, collection and sorting ability, analysis of various large-scale business computing application statistical data of enterprises, mining theoretical knowledge of natural environment science, image recognition, retrieval of global geographic location information and so on. Through the simulation of soil erosion in mountainous areas in my country, dynamic data analysis describes the basic situation of soil erosion in various mountainous areas in my country and the whole process of its evolution and development and comprehensively analyzes and studies various soil erosion events in mountainous areas in my country. The corresponding solutions can effectively and accurately respond to emergencies of soil erosion in mountainous areas and effectively reduce the loss of soil and water crops and economic losses in natural disasters. In recent years, information technology companies related to computer and machine learning capabilities, especially based on machine cloud computing, learning science and technology based on the theory of human machine artificial intelligence, and combining related professional technologies, have been used in various areas to prevent floods, droughts, risks, and rain in various areas. Disaster reduction and other non-natural engineering construction technology prevention measures have gradually been applied. The main research question of this paper is to design a related human body trajectory image detection system for new human facial image processing based on machine learning, and information collection and detection management system, and conduct in-depth research on its application prospects.

A Graph Mining Approach for Ranking and Discovering the Interesting Frequent Subgraph Patterns

Graph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM framework A RAnked Frequent pattern-growth Framework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.

Audio Information Camouflage Detection for Social Networks

Sending camouflaged audio information for fraud in social networks has become a new means of social networks attack. The hidden acoustic events in the audio scene play an important role in the detection of camouflaged audio information. Therefore, the application of machine learning methods to represent hidden information in audio streams has become a hot issue in the field of network security detection. This study proposes a heuristic mask for empirical mode decomposition (HM-EMD) method for extracting hidden features from audio streams. The method consists of two parts: First, it constructs heuristic mask signals related to the signal’s structure to solve the modal mixing problem in intrinsic mode function (IMF) and obtains a pure IMF related to the signal’s structure. Second, a series of hidden features in environment-oriented audio streams is constructed on the basis of the IMF. A machine learning method and hidden information features are subsequently used for audio stream scene classification. Experimental results show that the hidden information features of audio streams based on HM-EMD are better than the classical mel cepstrum coefficients (MFCC) under different classifiers. Moreover, the classification accuracy achieved with HM-EMD increases by 17.4 percentage points under the three-layer perceptron and by 1.3% under the depth model of TridentResNet. The hidden information features extracted by HM-EMD from audio streams revealed that the proposed method could effectively detect camouflaged audio information in social networks, which provides a new research idea for improving the security of social networks.

Finding Route Hotspots in Large Labeled Networks

In many advanced network analysis applications, like social networks, e-commerce, and network security, hotspots are generally considered as a group of vertices that are tightly connected owing to the similar characteristics, such as common habits and location proximity. In this article, we investigate the formation of hotspots from an alternative perspective that considers the routes along the network paths as the auxiliary information, and attempt to find the route hotspots in large labeled networks. A route hotspot is a cohesive subgraph that is covered by a set of routes, and these routes correspond to the same sequential pattern consisting of vertices' labels. To the best of our knowledge, the problem of Finding Route Hotspots in Large Labeled Networks has not been tackled in the literature. However, it is challenging as counting the number of hotspots in a network is #P-hard. Inspired by the observation that the sizes of hotspots decrease with the increasing lengths of patterns, we prove several anti-monotonicity properties of hotspots, and then develop a scalable algorithm called FastRH that can use these properties to effectively prune the patterns that cannot form any hotspots. In addition, to avoid the duplicate computation overhead, we judiciously design an effective index structure called RH-Index for storing the hotspot and pattern information collectively, which also enables incremental updating and efficient query processing. Our experimental results on real-world datasets clearly demonstrate the effectiveness and scalability of our proposed methods.

Online social networks security and privacy: comprehensive review and analysis

With fast-growing technology, online social networks (OSNs) have exploded in popularity over the past few years. The pivotal reason behind this phenomenon happens to be the ability of OSNs to provide a platform for users to connect with their family, friends, and colleagues. The information shared in social network and media spreads very fast, almost instantaneously which makes it attractive for attackers to gain information. Secrecy and surety of OSNs need to be inquired from various positions. There are numerous security and privacy issues related to the user’s shared information especially when a user uploads personal content such as photos, videos, and audios. The attacker can maliciously use shared information for illegitimate purposes. The risks are even higher if children are targeted. To address these issues, this paper presents a thorough review of different security and privacy threats and existing solutions that can provide security to social network users. We have also discussed OSN attacks on various OSN web applications by citing some statistics reports. In addition to this, we have discussed numerous defensive approaches to OSN security. Finally, this survey discusses open issues, challenges, and relevant security guidelines to achieve trustworthiness in online social networks.

A Study on the Social Networking Sites Security Awareness Among Users in Tabuk City

Objectives: To analyze security awareness among social media users in Tabuk City and the relationship when grouped according to gender, educational attainment, age, and years in social media. Methods/Statistical analysis: The descriptive correlational research method was utilized to accomplish the study’s objectives. A total of 385 respondents were selected using a random sampling of Social Media Users of Tabuk City, Kalinga, Philippines. Statistical Packages for Social Sciences (SPSS) software was used in assessing the frequency and computing the collected data’s multivariate test, which determines the relationship of the respondent’s response. Findings: The study showed a significant relationship between social networking security among users when grouped into gender and educational attainment, while as to age and years in social media, there is no significant relationship. The results revealed that most respondents are vulnerable to threats due to low social networking security awareness. Novelty: Social networking can be a form of data exchange that can be exploited. Therefore, this study highlighted the issues and challenges in the vulnerability of social networking site users. Keywords: Social Network; Security; Awareness

Evaluating security and privacy issues of social networks based information systems in Industry 4.0

ABSTRACT The present study aimed to analyse the main risks related to security and privacy of social networks based information systems in Industry 4.0. The methodology we used is an innovative exploratory data-driven process divided into three steps. First, we performed sentiment analysis to divide the database composed of 67, 206 tweets into feelings. Second, we applied a topic-modelling algorithm to extract topics. Third, we applied textual analysis to collect insights. A total of 10 topics related to security and privacy issues were identified as results. The paper concludes with a discussion of the challenges and main concerns related to the identified topics.

Aggregation Encryption Method of Social Network Privacy Data Based on Matrix Decomposition Algorithm

Currently, there are some defects in data aggregation encryption methods, such as low accuracy and efficiency. In this regard, this paper proposes an aggregation encryption method of social network privacy data based on matrix decomposition. Shamir threshold segmentation scheme was used to improve the security of matrix decomposition results, and then re-encrypted network user attributes. For codomain and non-codomain users, private file and key generation technology were used to improve the privacy of users in common domain and non-common domain. Considering the results of matrix decomposition, privacy security and efficient user matching in social networks were implemented. The node information was embedded in the process of data encryption and signature, and the interference factors were classified by cluster as a unit, which can resist the internal attack and improve the operation efficiency. The experimental results show that this method has excellent encryption effect and high privacy data protection processing efficiency.

A Framework of Social Networks Control in Higher Educational Contexts

Educationists are urged to investigate the effect of social networks control. There is a lack of multidisciplinary studies with a focus on coercive social network control in the higher education sector. This exploratory and descriptive study presents a theoretical contemplation of social network control based on ten key elements that influence teaching and learning in higher educational environments. The paper provides a critical analysis of the impact of an economic model on social networks control and examines social engineering through the tenets of digital presence, psychological strategies and tactics. Insights into user-generated content and the learning process of manipulative assimilation that influence current ways of thinking in higher educational contexts are offered through the lens of social networks control mechanisms. A framework for social network control was derived, signifying the flow and dynamics between key elements which might impact on various aspects of social network security and its applications in Higher Education.

Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning

In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution.

METHOD OF CALCULATING THE PROTECTION OF PERSONAL DATA FROM THE NETWORK CLUSTERING FACTOR

A mathematical model has been developed and a study of the model of personal data protection from network clustering coefficient and data transfer intensity in social networks has been carried out. Dependencies of protection of the system from the size of the system (and from the amount of personal data); information security threats from the network clustering factor. A system of linear equations is obtained, which consists of the equation: rate of change of information flow from social network security and coefficients that reflect the impact of security measures, amount of personal data, leakage rate, change of information protection from network clustering factor, its size, personal data protection. As a result of solving the system of differential equations, mathematical and graphical dependences of the indicator of personal data protection in the social network from different components are obtained. Considering three options for solving the equation near the steady state of the system, we can conclude that, based on the conditions of the ratio of dissipation and natural frequency, the attenuation of the latter to a certain value is carried out periodically, with decaying amplitude, or by exponentially decaying law. A more visual analysis of the system behavior is performed, moving from the differential form of equations to the discrete one and modeling some interval of the system existence. Mathematical and graphical dependences of the system natural frequency, oscillation period, attenuation coefficient are presented. Simulation modeling for values with deviation from the stationary position of the system is carried out. As a result of simulation, it is proved that the social network protection system is nonlinear.

Some Issues Relating to Privacy and Security in Online Social Networks

Some Issues Relating to Privacy and Security in Online Social Networks

Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Detection of compromised accounts for online social networks based on a supervised analytical hierarchy process

In recent years, the security of online social networks (OSNs) has become an issue of widespread concern. Searching and detecting compromised accounts in OSNs is crucial for ensuring the security of OSN platforms. In this study, the authors proposed a new method of detecting compromised accounts based on a supervised analytical hierarchy process (SAHP). First, they considered the expression habits of a user to present the profile features of a user more comprehensively than previous research. Next, the information gain ratio was combined with the analytical hierarchy process algorithm to calculate the weight of each feature. Finally, a detection decision was taken, and varying thresholds were used to obtain different detection results. The experimental results showed that the accuracy and precision of the SAHP were 81.7 and 96.4%, respectively. The results indicated that the new method improved upon the previously established COMPA (detecting compromised accounts on social networks) methods for detecting compromised accounts.

Social Networking Security

Information protection is very essential these days to anyone using a pc or to any company that employs computers and networking in their day to day operations. That is almost everyone. Information protection need to be at the forefront of everyone’s thinking in view that so a lot of our personal information is out there on the Internet. On this paper, the thinking of protection and privateness in social media, or social networking can be mentioned. First, a short history and the notion of social networking will be introduced. Many of the safety dangers related with the use of social media are presented. Also, the trouble of privateness and how it relates to security are described.

A crowdsourcing method for online social networks security assessment based on human-centric computing

Crowdsourcing and crowd computing are a trend that is likely to be increasingly popular, and there remain a number of research and operational challenges that need to be addressed. The human-centric computational abstraction called situation may be used to cope with these difficulties. In this paper, we focus on one such challenge, which is how to assign crowd assessment tasks about security and privacy in online social networks to the most appropriate users efficiently, effectively and accurately. Specifically, here we propose a novel task assignment method to facilitate crowd assessment, which improves the security and trustworthiness of social networking platforms, as well as a task assignment algorithm based on SocialSitu, which is a social-domain-focused situational analytics. Findings from our crowd assessment experiments on a real world social network Shareteches show that the precision and recall of the proposed method and algorithm are 0.491 and 0.538 higher than those of a random algorithm’s, as well as 0.336 and 0.366 higher than users’ theme-aware algorithm’s, respectively. Moreover, these results further suggest that our experimental evaluation enhance the security and privacy of online social networks.

Comparison of patient-centric algorithms for health information security in health social networks and cloud environments

Comparison of patient-centric algorithms for health information security in health social networks and cloud environments

Recent Advances in Social Network Analysis, Management and Security

Recent Advances in Social Network Analysis, Management and Security