scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

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.

Access Control Models for Online Social Networks

2014 ◽  
pp. 451-484
Author(s):  
Rula Sayaf ◽  
Dave Clarke
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Online Social Networks ◽  
Personal Data ◽  
Security And Privacy ◽  
Open Problems ◽  
Access Control Models ◽  
Control Models ◽  
Underlying Mechanisms ◽  
High Level

Access control is one of the crucial aspects in information systems security. Authorizing access to resources is a fundamental process to limit potential privacy violations and protect users. The nature of personal data in online social networks (OSNs) requires a high-level of security and privacy protection. Recently, OSN-specific access control models (ACMs) have been proposed to address the particular structure, functionality and the underlying privacy issues of OSNs. In this survey chapter, the essential aspects of access control and review the fundamental classical ACMs are introduced. The specific OSNs features and review the main categories of OSN-specific ACMs are highlighted. Within each category, the most prominent ACMs and their underlying mechanisms that contribute enhancing privacy of OSNs are surveyed. Toward the end, more advanced issues of access control in OSNs are discussed. Throughout the discussion, different models and highlight open problems are contrasted. Based on these problems, the chapter is concluded by proposing requirements for future ACMs.

scholarly journals A Game-Theoretical Approach to Multimedia Social Networks Security

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Enqiang Liu ◽  
Zengliang Liu ◽  
Fei Shao ◽  
Zhiyong Zhang
Keyword(s):  
Social Networks ◽  
Nash Equilibrium ◽  
Access Control ◽  
Control Model ◽  
Security Policies ◽  
Access Control Models ◽  
Multimedia Social Networks ◽  
Game Theoretical Approach ◽  
Game Theoretic ◽  
Stable Combination

The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party’s benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders.

scholarly journals An efficient hybrid system for anomaly detection in social networks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Md. Shafiur Rahman ◽  
Sajal Halder ◽  
Md. Ashraf Uddin ◽  
Uzzal Kumar Acharjee
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Security And Privacy ◽  
Support Vector ◽  
The Social ◽  
Wide Range

AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

scholarly journals Access Control Models for Online Social Networks

2013 ◽  
pp. 32-65 ◽  
Author(s):  
Rula Sayaf ◽  
Dave Clarke
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Online Social Networks ◽  
Personal Data ◽  
Security And Privacy ◽  
Open Problems ◽  
Access Control Models ◽  
Control Models ◽  
Underlying Mechanisms ◽  
High Level

Access control is one of the crucial aspects in information systems security. Authorizing access to resources is a fundamental process to limit potential privacy violations and protect users. The nature of personal data in online social networks (OSNs) requires a high-level of security and privacy protection. Recently, OSN-specific access control models (ACMs) have been proposed to address the particular structure, functionality and the underlying privacy issues of OSNs. In this survey chapter, the essential aspects of access control and review the fundamental classical ACMs are introduced. The specific OSNs features and review the main categories of OSN-specific ACMs are highlighted. Within each category, the most prominent ACMs and their underlying mechanisms that contribute enhancing privacy of OSNs are surveyed. Toward the end, more advanced issues of access control in OSNs are discussed. Throughout the discussion, different models and highlight open problems are contrasted. Based on these problems, the chapter is concluded by proposing requirements for future ACMs.

A Survey on Access Control Techniques for Social Networks

2020 ◽  
pp. 319-342
Author(s):  
Yousra Asim ◽  
Ahmad Kamran Malik
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Access Control ◽  
Online Social Networks ◽  
Unauthorized Access ◽  
Related Information ◽  
Access Control Models ◽  
Control Models ◽  
As Relationship ◽  
Day By Day

Online Social Networks (OSN) are getting popular day by day. Users share their information in OSN with others users. Access control is required to prevent unauthorized access to this information. Several studies have been conducted for access control in social networks. This chapter is a survey of available access control models/techniques based on social networks. Available access control models can be categorized as relationship-based, attributes-based, community structure-based and user activity centric model. A number of techniques have been proposed by several authors for access control in social networks. Most of the approaches use Social Network Analysis (SNA) techniques, others use user related information, for example, attributes or activities, the rest use a combination of approaches.

A Survey on Access Control Techniques for Social Networks

2016 ◽  
pp. 1-32 ◽  
Author(s):  
Yousra Asim ◽  
Ahmad Kamran Malik
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Access Control ◽  
Online Social Networks ◽  
Unauthorized Access ◽  
Related Information ◽  
Access Control Models ◽  
Control Models ◽  
As Relationship ◽  
Day By Day

Online Social Networks (OSN) are getting popular day by day. Users share their information in OSN with others users. Access control is required to prevent unauthorized access to this information. Several studies have been conducted for access control in social networks. This chapter is a survey of available access control models/techniques based on social networks. Available access control models can be categorized as relationship-based, attributes-based, community structure-based and user activity centric model. A number of techniques have been proposed by several authors for access control in social networks. Most of the approaches use Social Network Analysis (SNA) techniques, others use user related information, for example, attributes or activities, the rest use a combination of approaches.

scholarly journals An Efficient Anomaly Detection Based on Optimal Deep Belief Network in Big Data

2019 ◽  
Vol 9 (1) ◽  
pp. 708-716
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Anomaly Detection ◽  
Computer Network ◽  
Large Datasets ◽  
Support Vector ◽  
The Internet ◽  
Data Sets ◽  
Data Generation ◽  
Detection Techniques

Nowadays, the internet and network service user’s counts are increasing and the data generation speed also very high. Then again, we see greater security dangers on the internet, enterprise network, websites and the network. Anomaly has been known as one of the effective cyber threats over the internet which increasing exponentially and thus overcomes the commonly used approaches for anomaly detection and classification. Anomaly detection is used in big data analytics to recognize the unexpected behaviour. The most commonly used characteristics in network environment are size and dimensionality, which are big datasets and also impose problems in recognizing useful patterns, For example, to identify the network traffic anomalies from the large datasets. Due to the enormous increase of computer network based facilities it is a challenge to perform fast and efficient anomaly detection. The anomaly recognition in big data sets is more useful to discover fraud and abnormal action. Here, we mainly focus on the problems regarding anomaly detection, so we introduce a novel machine learning based anomaly detection technique. Machine learning approach is used to enhance the anomaly detection speed which is very much useful to detect the anomaly from the large datasets. We evaluate the proposed framework by performing experiments with larger data sets and compare to several existing techniques such as fuzzy, SVM (Support Vector Machine) and PSO (Particle swarm optimization). It has shown 98% percentage of accuracy and the false rate of 0.002 % on proposed classifier. The experimental results illuminate that better performance than existing anomaly detection techniques in big data environment.

A Review on Machine-Learning Based Code Smell Detection Techniquesin Object-Oriented Software System(s)

2020 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur ◽  
Sushma Jain ◽  
Shivani Goel ◽  
Gaurav Dhiman
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Software Quality ◽  
Empirical Studies ◽  
Statistical Testing ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Code Smells ◽  
Detection Techniques ◽  
Code Smell

Context: Code smells are symptoms, that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exists many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality. Objective: This paper presents an up-to-date review of simple and hybrid machine learning based code smell detection techniques and tools. Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like, code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing. Results: Majority of empirical studies have proposed machine- learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as, Xerces, Gantt Project and ArgoUml. Furthermore, researchers paid more attention towards Feature Envy and Long Method code smells. Conclusion: We identified several areas of open research like, need of code smell detection techniques using hybrid approaches, need of validation employing industrial datasets, etc.

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