A Peer-Based Approach on Analyzing Hacked Twitter Accounts

Social media has become an important part of the lives of their hundreds of millions of users. Hackers make use of the large target audience by sending malicious content, often by hijacking existing accounts. This phenomenon has caused widespread research on how to detect hacked accounts, where different approaches exist. This work sets out to analyze the possibilities of including the reactions of hacked Twitter accounts’ peers into a detection system. Based on a dataset of six million tweets crawled from Twitter over the course of two years, we select a subset of tweets in which users react to alleged hacks of other accounts. We then gather and analyze the responses to those messages to reconstruct the conversations made. A quantitative analysis of these conversations shows that 30% of the users that are allegedly being hacked reply to the accusations, suggesting that these users acknowledge that their account was hacked

Three Essays on Individuals’ Vulnerability to Security Attacks in Online Social Networks: Factors and Behaviors

With increasing reliance on the Internet, the use of online social networks (OSNs) for communication has grown rapidly. OSN platforms are used to share information and communicate with friends and family. However, these platforms can pose serious security threats to users. In spite of the extent of such security threats and resulting damages, little is known about factors associated with individuals’ vulnerability to online security attacks. We address this gap in the following three essays. Essay 1 draws on a synthesis of the epidemic theory in infectious disease epidemiology with the social capital theory to conceptualize factors that contribute to an individual’s role in security threat propagation in OSN. To test the model, we collected data and created a network of hacked individuals over three months from Twitter. The final hacked network consists of over 8000 individual users. Using this data set, we derived individual’s factors measuring threat propagation efficacy and threat vulnerability. The dependent variables were defined based on the concept of epidemic theory in disease propagation. The independent variables are measured based on the social capital theory. We use the regression method for data analysis. The results of this study uncover factors that have significant impact on threat propagation efficacy and threat vulnerability. We discuss the novel theoretical and managerial contributions of this work. Essay 2 explores the role of individuals’ interests in their threat vulnerability in OSNs. In OSNs, individuals follow social pages and post contents that can easily reveal their topics of interest. Prior studies show high exposure of individuals to topics of interest can decrease individuals’ ability to evaluate the risks associated with their interests. This gives attackers a chance to target people based on what they are interested in. However, interest-based vulnerability is not just a risk factor for individuals themselves. Research has reported that similar interests lead to friendship and individuals share similar interests with their friends. This similarity can increase trust among friends and makes individuals more vulnerable to security threat coming from their friends’ behaviors. Despite the potential importance of interest in the propagation of online security attacks online, the literature on this topic is scarce. To address this gap, we capture individuals’ interests in OSN and identify the association between individuals’ interests and their vulnerability to online security threats. The theoretical foundation of this work is a synthesis of dual-system theory and the theory of homophily. Communities of interest in OSN were detected using a known algorithm. We test our model using the data set and social network of hacked individuals from Essay 1. We used this network to collect additional data about individuals’ interests in OSN. The results determine communities of interests which were associated with individuals’ online threat vulnerability. Moreover, our findings reveal that similarities of interest among individuals and their friends play a role in individuals’ threat vulnerability in OSN. We discuss the novel theoretical and empirical contributions of this work. Essay 3 examines the role addiction to OSNs plays in individuals’ security perceptions and behaviors. Despite the prevalence of problematic use of OSNs and the possibility of addiction to these platforms, little is known about the functionalities of brain systems of users who suffer from OSN addiction and their online security perception and behaviors. In addressing these gaps, we have developed the Online addiction & security behaviors (OASB) theory by synthesizing dual-system theory and extended protection motivation theory (PMT). We collected data through an online survey. The results indicate that OSN addiction is rooted in the individual’s brain systems. For the OSN addicted, there is a strong cognitive-emotional preoccupation with using OSN. Our findings also reveal the positive and significant impact of OSN addiction on perceived susceptibility to and severity of online security threats. Moreover, our results show the negative association between OSN addiction and perceived self-efficacy. We discuss the theoretical and practical implications of this work

Introduction to Data Analytics and Data Mining for Social Media Minitrack

N/

Rethinking Privacy and Security Mechanisms in Online Social Networks

With billions of users, Online Social Networks(OSNs) are amongst the largest scale communication applications on the Internet. OSNs enable users to easily access news from local and worldwide, as well as share information publicly and interact with friends. On the negative side, OSNs are also abused by spammers to distribute ads or malicious information, such as scams, fraud, and even manipulate public political opinions. Having achieved significant commercial success with large amount of user information, OSNs do treat the security and privacy of their users seriously and provide several mechanisms to reinforce their account security and information privacy. However, the efficacy of those measures is either not thoroughly validated or in need to be improved. In sight of cyber criminals and potential privacy threats on OSNs, we focus on the evaluations and improvements of OSN user privacy configurations, account security protection mechanisms, and trending topic security in this dissertation. We first examine the effectiveness of OSN privacy settings on protecting user privacy. Given each privacy configuration, we propose a corresponding scheme to reveal the target user\u27s basic profile and connection information starting from some leaked connections on the user\u27s homepage. Based on the dataset we collected on Facebook, we calculate the privacy exposure in each privacy setting type and measure the accuracy of our privacy inference schemes with different amount of public information. The evaluation results show that (1) a user\u27s private basic profile can be inferred with high accuracy and (2) connections can be revealed in a significant portion based on even a small number of directly leaked connections. Secondly, we propose a behavioral-profile-based method to detect OSN user account compromisation in a timely manner. Specifically, we propose eight behavioral features to portray a user\u27s social behavior. A user\u27s statistical distributions of those feature values comprise its behavioral profile. Based on the sample data we collected from Facebook, we observe that each user\u27s activities are highly likely to conform to its behavioral profile while two different user\u27s profile tend to diverge from each other, which can be employed for compromisation detection. The evaluation result shows that the more complete and accurate a user\u27s behavioral profile can be built the more accurately compromisation can be detected. Finally, we investigate the manipulation of OSN trending topics. Based on the dataset we collected from Twitter, we manifest the manipulation of trending and a suspect spamming infrastructure. We then measure how accurately the five factors (popularity, coverage, transmission, potential coverage, and reputation) can predict trending using an SVM classifier. We further study the interaction patterns between authenticated accounts and malicious accounts in trending. at last we demonstrate the threats of compromised accounts and sybil accounts to trending through simulation and discuss countermeasures against trending manipulation

QUANTIFYING AND PREDICTING USER REPUTATION IN A NETWORK SECURITY CONTEXT

Reputation has long been an important factor for establishing trust and evaluating the character of others. Though subjective by definition, it recently emerged in the field of cybersecurity as a metric to quantify and predict the nature of domain names, IP addresses, files, and more. Implicit in the use of reputation to enhance cybersecurity is the assumption that past behaviors and opinions of others provides insight into the expected future behavior of an entity, which can be used to proactively identify potential threats to cybersecurity. Despite the plethora of work in industry and academia on reputation in cyberspace, proposed methods are often presented as black boxes and lack scientific rigor, reproducibility, and validation. Moreover, despite widespread recognition that cybersecurity solutions must consider the human user, there is limited work focusing on user reputation in a security context. This dissertation presents a mathematical interpretation of user cyber reputation and a methodology for evaluating reputation in a network security context. A user’s cyber reputation is defined as the most likely probability the user demonstrates a specific characteristic on the network, based on evidence. The methodology for evaluating user reputation is presented in three phases: characteristic definition and evidence collection; reputation quantification and prediction; and reputation model validation and refinement. The methodology is illustrated through a case study on a large university network, where network traffic data is used as evidence to determine the likelihood a user becomes infected or remains uninfected on the network. A separate case study explores social media as an alternate source of data for evaluating user reputation. User-reported account compromise data is collected from Twitter and used to predict if a user will self-report compromise. This case study uncovers user cybersecurity experiences and victimization trends and emphasizes the feasibility of using social media to enhance understandings of users from a security perspective. Overall, this dissertation presents an exploration into the complicated space of cyber identity. As new threats to security, user privacy, and information integrity continue to manifest, the need for reputation systems and techniques to evaluate and validate online identities will continue to grow

A retrospective on state of the art social media research methods: Ethical decisions, big-small data rivalries and the spectre of the 6Vs

This concluding chapter offers critical reflections on some of the key themes covered in the Handbook. Ethics emerged as a concern for many scholars, both for those engaging in quantitative and qualitative approaches. Scholars agree in that there is no overarching set of rules that can be applied to all projects blindly, rather they see ethical decisions as being grounded in the specifics of the data being collected, the social group under study, and the potential repercussions for subjects. A second central theme was the value of qualitative approaches for understanding ‘anomalies’ within larger data sets. Qualitative approaches are seen as valuable and a stand-alone means of collecting, analyzing and making sense of social media data, in particular for projects where context is essential. Finally, as the contributions in this volume demonstrate that many of the challenges posed by the nature of social media data are being tackled and addressed, this chapter ends with a reorientation of the 6Vs which focuses on the primacy of the researcher in the decision-making process. We argue that the provision of technical solutions alone do not entirely address the 6V problem and clarity of thought around research design is still just as important as ever

Social Media Exploitation by Covert Networks: A Case Study of ISIS

Social media has quickly become a dominant mode of professional and personal communication. Unfortunately, groups who intend to perform illegal and/or harmful activities (such as gangs, criminal groups, and terrorist groups) also use it. These covert networks use social media to foster membership, communicate among followers and non-followers, and obtain ideological and financial support. This exploitation of social media has serious political, cultural, and societal repercussions that go beyond stolen identities, hacked systems, or loss of productivity. There are literal life-and-death consequences of the actions of the groups behind these covert networks. However, through tracking and analyzing social media content, government agencies (in particular those in the intelligence community) can mitigate this threat by uncovering these covert networks, their communication, and their plans. This paper introduces common social media analysis techniques and the current approaches of analyzing covert networks. A case study of the Syrian conflict, with particular attention on ISIS, highlights this exploitation and the process of using social media analysis for intelligence gathering. The results of the case study show that covert networks are resilient and continually adapt their social media use and presence to stay ahead of the intelligence community

Cyber Security Concerns in Social Networking Service

Today’s world is unimaginable without online social networks. Nowadays, millions of people connect with their friends and families by sharing their personal information with the help of different forms of social media. Sometimes, individuals face different types of issues while maintaining the multimedia contents like, audios, videos, photos because it is difficult to maintain the security and privacy of these multimedia contents uploaded on a daily basis. In fact, sometimes personal or sensitive information could get viral if that leaks out even unintentionally. Any leaked out content can be shared and made a topic of popular talk all over the world within few seconds with the help of the social networking sites. In the setting of Internet of Things (IoT) that would connect millions of devices, such contents could be shared from anywhere anytime. Considering such a setting, in this work, we investigate the key security and privacy concerns faced by individuals who use different social networking sites differently for different reasons. We also discuss the current state-of-the-art defense mechanisms that can bring somewhat long-term solutions to tackling these threats