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

Social media, political polarization, and political disinformation: a review of the scientific literature

The following report is intended to provide an overview of the current state of the literature on the relationship between social media; political polarization; and political “disinformation,” a term used to encompass a wide range of types of information about politics found online, including “fake news,” rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and “hyperpartisan” news. The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known — and unknown — about the relationship between social media, political polarization, and disinformation. The report concludes by identifying key gaps in our understanding of these phenomena and the data that are needed to address them

Social media, political polarization, and political disinformation: a review of the scientific literature

The following report is intended to provide an overview of the current state of the literature on the relationship between social media; political polarization; and political “disinformation,” a term used to encompass a wide range of types of information about politics found online, including “fake news,” rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and “hyperpartisan” news. The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known — and unknown — about the relationship between social media, political polarization, and disinformation. The report concludes by identifying key gaps in our understanding of these phenomena and the data that are needed to address them

A Framework for Identifying Host-based Artifacts in Dark Web Investigations

The dark web is the hidden part of the internet that is not indexed by search engines and is only accessible with a specific browser like The Onion Router (Tor). Tor was originally developed as a means of secure communications and is still used worldwide for individuals seeking privacy or those wanting to circumvent restrictive regimes. The dark web has become synonymous with nefarious and illicit content which manifests itself in underground marketplaces containing illegal goods such as drugs, stolen credit cards, stolen user credentials, child pornography, and more (Kohen, 2017). Dark web marketplaces contribute both to illegal drug usage and child pornography. Given the fundamental goal of privacy and anonymity, there are limited techniques for finding forensic artifacts and evidence files when investigating misuse and criminal activity in the dark web. Previous studies of digital forensics frameworks reveal a common theme of collection, examination, analysis, and reporting. The existence and frequency of proposed frameworks demonstrate the acceptance and utility of these frameworks in the field of digital forensics. Previous studies of dark web forensics have focused on network forensics rather than hostbased forensics. macOS is the second most popular operating system after Windows (Net Marketshare, n.d.); however, previous research has focused on the Windows operating system with little attention given to macOS forensics. This research uses design science methodology to develop a framework for identifying host-based artifacts during a digital forensic investigation involving suspected dark web use. Both the Windows operating system and macOS are included with the expected result being a reusable, comprehensive framework that is easy to follow and assists investigators in finding artifacts that are designed to be hidden or otherwise hard to find. The contribution of this framework will assist investigators in identifying evidence in cases where the user is suspected of accessing the dark web for criminal intent when little or no other evidence of a crime is present. The artifact produced for this research, The Dark Web Artifact Framework, was evaluated using three different methods to ensure that it met the stated goals of being easy to follow, considering both Windows and macOS operating systems, considering multiple ways of accessing the dark web, and being adaptable to future platforms. The methods of evaluation v included experimental evaluation conducted using a simulation of the framework, comparison of a previously worked dark web case using the created framework, and the expert opinion of members of the South Dakota Internet Crimes Against Children taskforce (ICAC) and the Division of Criminal Investigation (DCI). A digital component can be found in nearly every crime committed today. The Dark Web Artifact Framework is a reusable, paperless, comprehensive framework that provides investigators with a map to follow to locate the necessary artifacts to determine if the system being investigated has been used to access the dark web for the purpose of committing a crime. In the creation of this framework, a process itself was created that will contribute to future works. The yes/no, if/then structure of the framework is adaptable to fit with workflows in any area that would benefit from a recurring process

Selected Computing Research Papers Volume 1 June 2012

An Evaluation of Anti-phishing Solutions (Arinze Bona Umeaku) ..................................... 1 A Detailed Analysis of Current Biometric Research Aimed at Improving Online Authentication Systems (Daniel Brown) .............................................................................. 7 An Evaluation of Current Intrusion Detection Systems Research (Gavin Alexander Burns) .................................................................................................... 13 An Analysis of Current Research on Quantum Key Distribution (Mark Lorraine) ............ 19 A Critical Review of Current Distributed Denial of Service Prevention Methodologies (Paul Mains) ............................................................................................... 29 An Evaluation of Current Computing Methodologies Aimed at Improving the Prevention of SQL Injection Attacks in Web Based Applications (Niall Marsh) .............. 39 An Evaluation of Proposals to Detect Cheating in Multiplayer Online Games (Bradley Peacock) ............................................................................................................... 45 An Empirical Study of Security Techniques Used In Online Banking (Rajinder D G Singh) .......................................................................................................... 51 A Critical Study on Proposed Firewall Implementation Methods in Modern Networks (Loghin Tivig) .................................................................................................... 5

Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study

This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives

CPA\u27s handbook of fraud and commercial crime prevention

https://egrove.olemiss.edu/aicpa_guides/1820/thumbnail.jp

CPA\u27s handbook of fraud and commercial crime prevention

https://egrove.olemiss.edu/aicpa_guides/1823/thumbnail.jp

Factitious disorder and its online variant Munchausen by Internet: understanding motivation and its impact on online users to develop a detection method

The overarching aim of the research in this thesis was to develop a method of detecting Munchausen by Internet (MbI) and garner an understanding of the dynamics of online communities faced with MbI. Ground work studies were required to learn more about the disorder, to decide exactly what method of detection would be most appropriate. This involved a review of the existing literature available on MbI (paper 1; Munchausen by Internet). It also involved conducting two studies which focused on experiences from the perspective of those with Factitious Disorder (FD) (paper 2; When the lie is the truth: Grounded theory analysis of an online support group for factitious disorder) and MbI from the perspective of victims (paper 3; Claiming someone else’s pain: A Grounded theory analysis of online community user's experiences of Munchausen by Internet). Both these studies were necessary as FD and its online variant MbI are some of the most poorly understood and under researched pathologies. This is primarily because of the difficulty in obtaining and retaining participants who have experience of the disorder. Therefore, what was previously known about the disorder was largely speculative. The research presented in this thesis overcame the issue of recruitment and retentions of participants, by analysing the first-hand accounts written online by those who have experience of the disorders. The information obtained from the two groundwork studies was used in the third study to decide on and develop an appropriate method of detecting MbI and for interpreting the discriminate attributes (paper 4; Detecting Munchausen by Internet: Development of a Text Classifier through Machine Learning). Beyond applying the findings of these studies to the development of the classifier, they also made new theoretical contributions to the existing literature on FD and MbI. The first two studies provide the very first large-scale studies of FD and MbI, using first-hand accounts from those it directly affects rather than observations that are speculative. Grounded Theory was used to analyse the text as it does not require an a priori theoretical framework but allows the data to build the theoretical framework itself, resulting in more innovative findings. The findings offer a new perspective of FD, one which contrasts with traditional theories and indicates that FD may be closely aligned with addiction. The second study examined the dynamics within an online community faced with MbI. The primary findings were that MbI users were targeting ‘ideal victim’ persona which offered protection from suspicion and increased the level of attention and sympathy they could receive. The presence or possible presence of MbI also resulted in members of online communities using strategies to avoid false accusations or being duped. These strategies had the unfortunate consequence of potentially eroding the therapeutic benefits of online communities, in particular personal empowerment, by restricting opportunities to confer normality and cultivate interpersonal support. In addition, the methods used by online community members and their moderators to detect MbI were uncovered. It typically involved high-level deception cues which raised suspicions and the checking authoritative references to confirm or refute these suspicions. The findings from study one and two, as well as the literature review from paper one, offered no overt cues which could be consistently attributed to MbI and offered no support for the feasibility of psychometric testing to detect MBI. Therefore, it was decided that covert deception required a covert method of detection. To this end the SLP (Social Language Processing) framework, which integrates psychology and computer science, was applied to develop a text classifier through machine learning algorithms. This covert method has already been successfully used to detect written deception online. Two text classifiers were developed in study three using Linguistic Inquiry Word Count (LIWC2105) dimensions and n-grams obtained from a bag-of words model, with respective prediction accuracies of 81.11% and 81.67%. These classifiers added a practical application value to the research conducted in this thesis, by producing a method of detecting MbI that can be used by moderators and as a vetting and investigative tool for internet mediated researchers. There were also theoretical contributions obtained from study three. Some of the discriminate attributes used by the classifiers appeared to be unique to Munchausen’s and were associated with the motivation for the behaviour, which supports the growing move towards domain specificity when interpreting Linguistic Based Cues (LBC) of deception. The remaining LBC’s of deception concurred with established deception theory, particularly reduction of cognitive complexity. Overall the research described in this thesis has made new contributions to the existing theories surrounding Factitious Disorder (FD), MbI and Linguistic Based Cues (LBC’s) of deception. It also has a practical application value by creating a classifier which differentiates between text written by genuine people and those exhibiting Munchausen’s

A framework for cost-sensitive automated selection of intrusion response

In recent years, cost-sensitive intrusion response has gained significant interest due to its emphasis on the balance between potential damage incurred by the intrusion and cost of the response. However, one of the challenges in applying this approach is defining a consistent and adaptable measurement framework to evaluate the expected benefit of a response. In this thesis we present a model and framework for the cost-sensitive assessment and selection of intrusion response. Specifically, we introduce a set of measurements that characterize the potential costs associated with the intrusion handling process, and propose an intrusion response evaluation method with respect to the risk of potential intrusion damage, the effectiveness of the response action and the response cost for a system. The proposed framework has the important quality of abstracting the system security policy from the response selection mechanism, permitting policy adjustments to be made without changes to the model. We provide an implementation of the proposed solution as an IDS-independent plugin tool, and demonstrate its advantages over traditional static response systems and an existing dynamic response system