How users tweet about a cyber attack: An explorative study using machine learning and social network analysis

Cybercrime is a growing threat for firms and customers that emerged with the digitization of business. However, research shows that even though people claim that they are concerned about their privacy online, they do not act correspondingly. This study investigates how prevalent security issues are during a cyber attack among Twitter users. The case under examination is the security breach at the US ticket sales company, Ticketfly, that compromised the information of 26 million users. Tweets related to cybersecurity are detected through the application of automated text classification based on supervised machine learning with support vector machines. Subsequently, the users that wrote security-related tweets are grouped into communities through a social network analysis. The results of this multi-method study show that users concerned about security issues are mostly part of expert communities with already superior knowledge about cybersecurity.

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Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

BioMed Research International ◽

10.1155/2015/810514 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 69

Author(s):

Quan Zou ◽

Jinjin Li ◽

Qingqi Hong ◽

Ziyu Lin ◽

Yun Wu ◽

...

Keyword(s):

Machine Learning ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Cross Validation ◽

Supervised Machine Learning ◽

Rna Molecules ◽

Disease Associations ◽

Endogenous Genes ◽

Leave One Out

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

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Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures

Applied Sciences ◽

10.3390/app10051699 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1699 ◽

Cited By ~ 4

Author(s):

Mateusz Nurek ◽

Radosław Michalski

Keyword(s):

Machine Learning ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Research Question ◽

Graph Algorithm ◽

Organizational Structures ◽

Support Vector ◽

Machine Learning Classification ◽

The Social

Formation of a hierarchy within an organization is a natural way of assigning the duties, delegating responsibilities and optimizing the flow of information. Only for the smallest companies the lack of the hierarchy, that is, a flat one, is possible. Yet, if they grow, the introduction of a hierarchy is inevitable. Most often, its existence results in different nature of the tasks and duties of its members located at various organizational levels or in distant parts of it. On the other hand, employees often send dozens of emails each day, and by doing so, and also by being engaged in other activities, they naturally form an informal social network where nodes are individuals and edges are the actions linking them. At first, such a social network seems distinct from the organizational one. However, the analysis of this network may lead to reproducing the organizational hierarchy of companies. This is due to the fact that that people holding a similar position in the hierarchy possibly share also a similar way of behaving and communicating attributed to their role. The key concept of this work is to evaluate how well social network measures when combined with other features gained from the feature engineering align with the classification of the members of organizational social network. As a technique for answering this research question, machine learning apparatus was employed. Here, for the classification task, Decision Trees, Random Forest, Neural Networks and Support Vector Machines have been evaluated, as well as a collective classification algorithm, which is also proposed in this paper. The used approach allowed to compare how traditional methods of machine learning classification, while supported by social network analysis, performed in comparison to a typical graph algorithm. The results demonstrate that the social network built using the metadata on communication highly exposes the organizational structure.

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Six Degrees of Information: Using Social Network Analysis to Explore the Spread of Information Within Sport Social Networks

International Journal of Sport Communication ◽

10.1123/ijsc.5.1.16 ◽

2012 ◽

Vol 5 (1) ◽

pp. 16-34 ◽

Cited By ~ 42

Author(s):

Marion E. Hambrick

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Online Social Network ◽

Home Pages ◽

Sporting Event ◽

Sport Industry ◽

Twitter Users ◽

Promotional Messages ◽

Share Information

Sport industry groups including athletes, teams, and leagues use Twitter to share information about and promote their products. The purpose of this study was to explore how sporting event organizers and influential Twitter users spread information through the online social network. The study examined two bicycle race organizers using Twitter to promote their events. Using social network analysis, the study categorized Twitter messages posted by the race organizers, identified their Twitter followers and shared relationships within Twitter, and mapped the spread of information through these relationships. The results revealed that the race organizers used their Twitter home pages and informational and promotional messages to attract followers. Popular Twitter users followed the race organizers early, typically within the first 4 days of each homepage’s creation, and they helped spread information to their respective followers. Sporting event organizers can leverage Twitter and influential users to share information about and promote their events.

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A Survey on Machine Learning Methodologies in Social Network Analysis

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) ◽

10.1109/icrito48877.2020.9197984 ◽

2020 ◽

Author(s):

Runa Ganguli ◽

Akash Mehta ◽

Soumya Sen

Keyword(s):

Machine Learning ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis

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Parallel Computing for Machine Learning in Social Network Analysis

2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) ◽

10.1109/ipdpsw.2017.178 ◽

2017 ◽

Cited By ~ 5

Author(s):

George Cybenko

Keyword(s):

Machine Learning ◽

Parallel Computing ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis

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Gragnostics: Fast, Interpretable Features for Comparing Graphs

10.31219/osf.io/hrmq3 ◽

2019 ◽

Cited By ~ 1

Author(s):

Robert Gove

Keyword(s):

Machine Learning ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Linear Time ◽

Interactive Visualization ◽

Visualization Tool ◽

Graph Visualization ◽

Learning Classifier ◽

Visualization Tools

Many analytical tasks, such as social network analysis, depend on comparing graphs. Existing methods are slow, or can be difficult to understand. To address these challenges, this paper proposes gragnostics, a set of 10 fast, layperson-understandable graph-level features. Each can be computed in linear time. To evaluate the ability of these features to discriminate different topologies and types of graphs, this paper compares a machine learning classifier using gragnostics to alternative classifiers, and the evaluation finds that the gragnostics classifier achieves higher performance. To evaluate gragnostics' utility in interactive visualization tools, this paper presents Chiron, a graph visualization tool that enables users to explore the subgraphs of a larger graph. Example usage scenarios of Chiron demonstrate that using gragnostics in a rank-by-feature framework can be effective for finding interesting subgraphs.

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Framework for Providing Security in Private Cloud using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9121.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 7641-7645

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithms ◽

Feature Reduction ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Cyber Attack ◽

Learning Techniques

The advancement in cyber-attack technologies have ushered in various new attacks which are difficult to detect using traditional intrusion detection systems (IDS).Existing IDS are trained to detect known patterns because of which newer attacks bypass the current IDS and go undetected. In this paper, a two level framework is proposed which can be used to detect unknown new attacks using machine learning techniques. In the first level the known types of classes for attacks are determined using supervised machine learning algorithms such as Support Vector Machine (SVM) and Neural networks (NN). The second level uses unsupervised machine learning algorithms such as K-means. The experimentation is carried out with four models with NSL- KDD dataset in Openstack cloud environment. The Model with Support Vector Machine for supervised machine learning, Gradual Feature Reduction (GFR) for feature selection and K-means for unsupervised algorithm provided the optimum efficiency of 94.56 %.

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Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning

Journal of Criminological Research Policy and Practice ◽

10.1108/jcrpp-02-2019-0009 ◽

2019 ◽

Vol 5 (2) ◽

pp. 108-119

Author(s):

Yeslam Al-Saggaf ◽

Amanda Davies

Keyword(s):

Machine Learning ◽

Data Mining ◽

Social Network ◽

Network Analysis ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Data Set ◽

Content Type ◽

The Social ◽

Twitter Users

Purpose The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. Design/methodology/approach To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Practical implications Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. Originality/value In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

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