Security In Online Social Networks Research Articles

Enhancing Privacy on Online Social Networs: A comparative study of encryption techniques

The ability to connect with people all over the world and share private information has made online social networks (OSNs) indispensable to contemporary communication. Because of the serious privacy and security concerns that come with their fast growth, protecting user data is of the utmost importance. In order to improve OSN privacy, this study examines several encryption techniques. It discusses symmetric encryption (ECC and RSA) and synthetic encryption (AES) as well as hybrid methods that combine the two types of encryption. Using examples from well-known sites such as LinkedIn, Facebook, and Twitter, the research assesses the efficacy and practicality of various strategies. Key management, performance implications, and user experience maintenance are some of the challenges that are addressed while adopting comprehensive encryption. To further improve privacy protection in OSNs, the paper delves into upcoming developments in encryption technology, such as decentralized encryption approaches and algorithms that are resistant to quantum computing. In order to better understand how to improve OSN security, this article will provide a thorough examination of encryption's function in protecting user privacy.

AdverSPAM: Adversarial SPam Account Manipulation in Online Social Networks

In recent years, the widespread adoption of Machine Learning (ML) at the core of complex IT systems has driven researchers to investigate the security and reliability of ML techniques. A very specific kind of threats concerns the adversary mechanisms through which an attacker could induce a classification algorithm to provide the desired output. Such strategies, known as Adversarial Machine Learning (AML), have a twofold purpose: to calculate a perturbation to be applied to the classifier’s input such that the outcome is subverted, while maintaining the underlying intent of the original data. Although any manipulation that accomplishes these goals is theoretically acceptable, in real scenarios perturbations must correspond to a set of permissible manipulations of the input, which is rarely considered in the literature. In this article, we present AdverSPAM , an AML technique designed to fool the spam account detection system of an Online Social Network (OSN). The proposed black-box evasion attack is formulated as an optimization problem that computes the adversarial sample while maintaining two important properties of the feature space, namely statistical correlation and semantic dependency . Although being demonstrated in an OSN security scenario, such an approach might be applied in other context where the aim is to perturb data described by mutually related features. Experiments conducted on a public dataset show the effectiveness of AdverSPAM compared to five state-of-the-art competitors, even in the presence of adversarial defense mechanisms.

Enhancing security in online social networks: introducing the DeepSybil model for Sybil attack detection

Enhancing security in online social networks: introducing the DeepSybil model for Sybil attack detection

User Perceptions of Privacy and Security in Online Social Networks

User Perceptions of Privacy and Security in Online Social Networks

Interpreting Graph-Based Sybil Detection Methods as Low-Pass Filtering

Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on the effectiveness of the low-pass filtering. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

AbstractOver the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

Modern Privacy-Preserving and Security Schemes in Social Networks: A Review

Online Social Network (OSN) is a popular application to exchange messages over the internet. However, millions of users are still under threat because of protection drawbacks. Many papers have proposed security methods, including firewalls, protocols, cryptography, statistical analysis, even learning algorithms. This paper provides an overview of privacy and security issues and describes multiple OSN protection techniques. We present various security schemes in OSNs and outline existing solutions to mitigate those attacks. This paper also discusses future research directions regarding OSN security problems and techniques.

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.

Some Issues Relating to Privacy and Security in Online Social Networks

Some Issues Relating to Privacy and Security in Online Social Networks

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.

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.

DeepOSN: Bringing deep learning as malicious detection scheme in online social network

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

Securing Heterogeneous Privacy Protection in Social Network Records based Encryption Scheme

This survey places of interest the major issues concerning privacy and security in online social networks. Firstly, we discuss investigate that aims to protect user data from the an assortment of attack vantage points together with other users, advertisers, third party request developers, and the online social arrangement provider itself. Next we cover social network supposition of user attributes, locate hubs, and link prediction. Because online social networks are so saturated with sensitive information, network inference plays a major privacy role. Social Networking sites go upwards since of all these reasons. In recent years indicates that for many people they are now the mainstream communication knowledge. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems threats and attacks such as revelation of information, identity thefts etc. Privacy practice in social networking sites often appear convoluted as in sequence sharing stands in discord with the need to reduce disclosure-related abuses. Facebook is one such most popular and widely used Social Networking sites which have its own healthy set of Privacy policy.

Sustainability of Information Security Investment in Online Social Networks: An Evolutionary Game-Theoretic Approach

With the rapid development of information technologies, security violations in online social networks (OSN) have emerged as a critical issue. Traditional technical and organizational approaches do not consider economic factors, which are increasingly important to sustain information security investment. In this paper, we develop an evolutionary game model to study the sustainability of information security investment in OSN, and propose a quantitative approach to analyze and optimize security investment. Additionally, we examine a contract with an incentive mechanism to eliminate free riding, which helps sustain the security investment. Numerical examples are provided for illustration and simulation purposes, leading to several countermeasures and suggestions. Our analytical results show that the optimal strategy of information security investment not only is correlated with profit growth coefficients and investment costs, but is also influenced significantly by the profits from free riding. If the profit growth coefficients are prohibitively small, both OSN service providers and online platforms will not choose to sustain investment based on small profits. As profit growth coefficients increase, there is a higher probability that game players will invest. Another major finding is that the (Invest, Invest) profile is much less sensitive to the change of profit growth coefficients and the convergent speed of this scenario is faster than the other profiles. The government agency can use the proposed model to determine a proper incentive or penalty to help both parties reach the optimal strategies and thus improve OSN security.

Fractal Intelligent Privacy Protection in Online Social Network Using Attribute-Based Encryption Schemes

While the online social network (OSN) has brought much convenience to users, there are still some serious problems, such as personal privacy leaks. Today, OSN security and privacy protection are one of the most important focuses of the research. In this paper, we present an intelligent privacy protection approach to solve problems of security and privacy protection in OSNs. First, the proposed algorithm combines a neural network with a hybrid hierarchy genetic algorithm and radial basis function, which is used to construct a prediction model of OSN security. Then, a support vector machine is applied to preprocess information of the OSN, and the attribute-based encryption scheme is adopted to encrypt the OSN information. Finally, a particle swarm optimization algorithm is used to improve OSN security and privacy protection. The experimental results demonstrate the effectiveness of the proposed method.

An efficient approach of improving privacy and security in online social networks

SummaryOnline social networks (OSNs) have become popular and widely used by many people nowadays. As those OSNs increase, the virtual social interactions have resulted in increasing privacy and security concern. Many approaches have been proposed in order to provide users with “better” privacy protection, but none of those approaches have “fully succeeded,” due to their conflict with service providers' economic benefits or due to the suggestion of changing the OSNs architecture. In this paper, we propose a new model that would improve privacy and security in OSNs by preventing users from revealing private and or confidential information online as possible. We endeavor to create a balance between privacy and security improvement versus the service provider business model.

Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks

Abstract As the online social network technology is gaining all time high popularity and usage, the malicious behavior and attacks of spammers are getting smarter and difficult to track. The newer spamming approaches using the social engineering concepts are making traditional spam and spammer detection techniques obsolete. Especially, content-based filtering of spam messages and spammer profiles in online social networks is becoming difficult. Newer approaches for spammer detection using topological features are gaining attention. Further, the evaluation of ensemble classifiers for detection of spammers over social networking behavior-based features is still in its infancy. In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features of legitimate users and spammers in online social networks. The proposed method aims to identify topological and community-based features from users’ interaction network and uses popular classifier ensembles – bagging and boosting to identify spammers in online social networks. Experimental evaluation of the proposed method is done over a real-world data set with artificial spammers that follow a behavior as reported in earlier literature. The experimental results reveal that the identified features are highly discriminative to identify spammers in online social networks.

A Framework for Detecting Cloning Attacks in OSN Based on a Novel Social Graph Topology

Online Social Networks (OSN) are considering one of the most popular internet applications which attract millions of users around the world to build several social relationships. Emerging the Web 2.0 technology allowed OSN users to create, share, or exchange types of contents in a popular fashion. The other hand, OSN are considering one of the most popular platforms for the intruders to spread several types of OSN attacks. Creating fake profiles for launching cloning attacks is one of the most risky attacks which target Users' profiles in Online Social Networks, the attacker seek to impersonate user's identity through duplicating user's online presence in the same or across several social networks, therefore, he can deceive OSN users into forming trusting social relations with his created fake profiles. These malicious profiles aim to harvest sensitive user's information or misuse the reputation of the legitimate profile's owner, as well as it may be used as a spy profiles for other criminal parties. Detecting these fake profiles still represent a major problem from OSN Security and Privacy point of view. In this paper we introduced a theoretical framework which depends on a novel topology of a social graph called Trusted Social Graph (TSG) which used to visualize trusted instances of social communications between OSN users. Another contribution is a proposed detection model that based on TSG topology as well as two techniques; Deterministic Finite Automaton (DFA) and Regular Expression. Our proposed detection model used to recognize the stranger instances of communications and social actions that performed using fake profiles in OSN.

A Survey On Securing User Data in Social Networks Using Privacy Preserving Options

Privacy is one of greatest rubbing focuses that rises when interchanges get interceded in Online Social Networks (OSNs).different groups of software engineering analysts have been surrounded the 'OSN security issue' as one of the observation, institutional or social protection. On account of handling these issues they have likewise treated them as though they were free. The principle contends is that the diverse security issues are snared and that the examination on protection in OSNs would profit from a more all encompassing methodology. In this paper, we first give a prologue to the observation and social security viewpoint stressing the account that the educate them, and their suspicions, objectives and techniques. __________________________________________________*****_________________________________________________