BMADSN: Big data multi-community anomaly detection in social networks

2019 ◽  
pp. 002072091989106 ◽  
Author(s):  
Manjunatha HC ◽  
Mohanasundaram R
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Detection Techniques ◽  
Graph Metrics ◽  
The People ◽  
Illegal Activities ◽  
The Given

In today's world, most of the people are using social networks for day-to-day activities. The most frequently used social sites are Facebook, Twitter, Google+, etc. These popular social networks are used by some of the users for abnormal or illegal activities. It is very important and necessary to identify and avoid such illegal activities without harming anyone in the society. In recent decades, social networks are becoming a popular research area for most researchers. Many authors are doing research on social network datasets and proposing various anomaly detection mechanisms to identify anomalous activities in both static and dynamic growing social networks. Various anomaly detection techniques are proposed by the authors to investigate malicious activities in social networks. In general, the process of identifying anomaly activities of the users in the given dataset is called anomaly detection. The anomaly detection in social networks is the process of investigating whether the users of the given social networks are involved in illegal activities or not. In this work, we proposed a most elegant approach to identify the anomalous or outlier users in the given social network. The proposed approach is considering the users participated in multiple communities of social networks. The designed algorithms are implemented and tested in a big data environment three node cluster using open source Hadoop ecosystem tools. Algorithm1 is used to investigate the nodes/users who participated in multiple communities of the given social network’s dataset. Algorithm2 takes the set of users participated in multiple communities and apply graph metrics such as degree and community score to predict the users involved in the anomalous activity.

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 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.

scholarly journals Determining the proximity of groups in social networks based on text analysis using big data

2019 ◽  
pp. 521-526
Author(s):  
A S Mukhin ◽  
I A Rytsarev ◽  
R A Paringer ◽  
A V Kupriyanov ◽  
D V Kirsh
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Collection ◽  
Text Analysis ◽  
Software Tool ◽  
Text Data ◽  
The Social ◽  
Information Research ◽  
Definition Of

The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied.

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

scholarly journals Social Network Extraction Unsupervised

2021 ◽  
Vol 12 (3) ◽  
pp. 4443-4449
Author(s):  
Mahyuddin K. M. Nasution Et.al
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Information Technology ◽  
Big Data ◽  
Social Network ◽  
Data Science ◽  
Information Sources ◽  
Scientific Data

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.

scholarly journals Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Nauman Ali Khan ◽  
Wuyang Zhou ◽  
Mudassar Ali Khan ◽  
Ahmad Almogren ◽  
Ikram Ud Din
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Research Direction ◽  
Social Actors ◽  
The Social ◽  
Depth Analysis ◽  
Location Based Social Network ◽  
Triadic Closure ◽  
Location Based Social Networks ◽  
The Given

Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.

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.

Big Data Analysis for Terroristic Behavior Identification and Study Using Social Networks

2019 ◽  
pp. 197-235
Author(s):  
Yuriy V. Kostyuchenko ◽  
Victor Pushkar ◽  
Olga Malysheva ◽  
Maxim Yuschenko
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Stochastic Methods ◽  
Armed Groups ◽  
Conflict Zones ◽  
Analysis Of Dynamics

This chapter aimed to consider of approaches to big data (social network content) utilization for understanding social behavior in the conflict zones, and analysis of dynamics of illegal armed groups. The analysis directed to identify of structure of illegal armed groups, and detection of underage militants. The probabilistic and stochastic methods of analysis and classification of number, composition, and dynamics of illegal armed groups in active conflict areas are proposed. Data of armed conflict in Donbas (Eastern Ukraine) in the period 2014-2015 is used for analysis. The numerical distribution of age, gender composition, origin, social status, and nationality of militants among illegal armed groups has been calculated. Conclusions on the applicability of described method in criminological practice, as well as about the possibilities of interpretation of obtaining results in the context of study of terrorism are proposed.

Sign in / Sign up

Export Citation Format

Share Document