Pub Date : 2017-01-02DOI: 10.1080/15536548.2017.1279876
Faruk Arslan
As the emerging field of data science permeates many domains inclusive of information security and privacy, understanding the process of turning information into insights continues to be a major endeavor. In this book, Max Shron provides a practice-oriented set of guidelines, which is geared towards expanding the soft skills required for making the most out of the data. As a data strategy consultant, Mr. Shron acknowledges the importance of hard skills such as data cleaning, mathematical modeling, and data visualization; however, he notes that the soft skills have been ignored within the data science field. He emphasizes that these soft skills, which include “translating vague problems into precisely answerable ones, and combining multiple lines or arguments into one useful result,” can be adapted from other disciplines. The author organizes the book into six chapters. Chapter 1 deals with problem scoping and introduces the readers to a practical framework called CoNVO, which stands for context, need, vision, and outcome. Chapter 2 focuses on techniques to refine the original vision and elaborates on the scaffolding strategy, which is geared toward the initial exploration of data to ensure proper understanding of the problem on hand. Chapter 3 deals with building and presenting arguments, while Chapter 4 discusses three groups of reasoning patterns, which facilitate the structuring of useful arguments. Chapter 5 is dedicated to causal reasoning, whereas the final chapter synthesizes the materials covered from Chapter 1 to 5. The author concludes the book with an appendix dedicated to reading materials, which can be used to further investigate the concepts discussed within the book. In the upcoming paragraphs, I will provide a review of the key ideas discussed in each chapter and conclude my review.
{"title":"Thinking with Data: How to Turn Information into Insights, by Max Shron","authors":"Faruk Arslan","doi":"10.1080/15536548.2017.1279876","DOIUrl":"https://doi.org/10.1080/15536548.2017.1279876","url":null,"abstract":"As the emerging field of data science permeates many domains inclusive of information security and privacy, understanding the process of turning information into insights continues to be a major endeavor. In this book, Max Shron provides a practice-oriented set of guidelines, which is geared towards expanding the soft skills required for making the most out of the data. As a data strategy consultant, Mr. Shron acknowledges the importance of hard skills such as data cleaning, mathematical modeling, and data visualization; however, he notes that the soft skills have been ignored within the data science field. He emphasizes that these soft skills, which include “translating vague problems into precisely answerable ones, and combining multiple lines or arguments into one useful result,” can be adapted from other disciplines. The author organizes the book into six chapters. Chapter 1 deals with problem scoping and introduces the readers to a practical framework called CoNVO, which stands for context, need, vision, and outcome. Chapter 2 focuses on techniques to refine the original vision and elaborates on the scaffolding strategy, which is geared toward the initial exploration of data to ensure proper understanding of the problem on hand. Chapter 3 deals with building and presenting arguments, while Chapter 4 discusses three groups of reasoning patterns, which facilitate the structuring of useful arguments. Chapter 5 is dedicated to causal reasoning, whereas the final chapter synthesizes the materials covered from Chapter 1 to 5. The author concludes the book with an appendix dedicated to reading materials, which can be used to further investigate the concepts discussed within the book. In the upcoming paragraphs, I will provide a review of the key ideas discussed in each chapter and conclude my review.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2017-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88811660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1080/15536548.2016.1257680
O. Folorunso, F. E. Ayo, Y. Babalola
ABSTRACT A signature-based system (SBS) is a common approach for intrusion detection and the most preferable by researchers. In spite of the popularity of SBS, it cannot detect new attacks on the network compared to anomaly-based systems (ABS). The most challenging problem of SBS is keeping an up-to-date database of known attack signatures and the setting of a suitable threshold level for intrusion detection. In this article, a network intrusion detection system based on combinatorial algorithm (CA-NIDS) is proposed. The CA-NIDS uses additional databases to enable the SBS to act as an ABS for the purpose of detecting new attacks and to speed up network traffic during traffic analysis by the combinatorial algorithm. A suitable threshold of 12 was also set based on the study of past works to lower the false positive rate. The CA-NIDS was evaluated with similar online schemes and result shows a small false-positive rate of 3% and a better accuracy of 96.5% compared with related online algorithms.
{"title":"Ca-NIDS: A network intrusion detection system using combinatorial algorithm approach","authors":"O. Folorunso, F. E. Ayo, Y. Babalola","doi":"10.1080/15536548.2016.1257680","DOIUrl":"https://doi.org/10.1080/15536548.2016.1257680","url":null,"abstract":"ABSTRACT A signature-based system (SBS) is a common approach for intrusion detection and the most preferable by researchers. In spite of the popularity of SBS, it cannot detect new attacks on the network compared to anomaly-based systems (ABS). The most challenging problem of SBS is keeping an up-to-date database of known attack signatures and the setting of a suitable threshold level for intrusion detection. In this article, a network intrusion detection system based on combinatorial algorithm (CA-NIDS) is proposed. The CA-NIDS uses additional databases to enable the SBS to act as an ABS for the purpose of detecting new attacks and to speed up network traffic during traffic analysis by the combinatorial algorithm. A suitable threshold of 12 was also set based on the study of past works to lower the false positive rate. The CA-NIDS was evaluated with similar online schemes and result shows a small false-positive rate of 3% and a better accuracy of 96.5% compared with related online algorithms.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87582066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1080/15536548.2016.1243852
Ruth Halperin, Y. Dror
ABSTRACT Over the past decade, the demise of privacy has been repeatedly pronounced by renowned technology executives such as Mark Zuckerberg, who have declared privacy to be passé and anachronistic—“so 20th century”—or the concern of old people. However, there has been relatively little research into privacy perception and behavior among different generations that may relate to how people navigate their private lives in online settings. Furthermore, recent research has revealed the ways in which privacy concerns of young Internet users are enacted, thus challenging overgeneralized claims of a clear-cut generation gap associated with online privacy. As information privacy problems are becoming thornier, unfounded statements voiced by stakeholders with vested interests should be put to one side. Instead, systematic research is needed to understand how privacy is perceived and managed by people of different age groups, and what measures can and should be taken to address current and future concerns of Internet users across generations. We explore these questions and account for the results using a representative sample from Israel.
{"title":"Information privacy and the digital generation gap: An exploratory study","authors":"Ruth Halperin, Y. Dror","doi":"10.1080/15536548.2016.1243852","DOIUrl":"https://doi.org/10.1080/15536548.2016.1243852","url":null,"abstract":"ABSTRACT Over the past decade, the demise of privacy has been repeatedly pronounced by renowned technology executives such as Mark Zuckerberg, who have declared privacy to be passé and anachronistic—“so 20th century”—or the concern of old people. However, there has been relatively little research into privacy perception and behavior among different generations that may relate to how people navigate their private lives in online settings. Furthermore, recent research has revealed the ways in which privacy concerns of young Internet users are enacted, thus challenging overgeneralized claims of a clear-cut generation gap associated with online privacy. As information privacy problems are becoming thornier, unfounded statements voiced by stakeholders with vested interests should be put to one side. Instead, systematic research is needed to understand how privacy is perceived and managed by people of different age groups, and what measures can and should be taken to address current and future concerns of Internet users across generations. We explore these questions and account for the results using a representative sample from Israel.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78417201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1080/15536548.2016.1243849
Faruk Arslan
In Social Physics: How Good Ideas Spread-The Lessons from a New Science, Alex Pentland introduces the readers to a new field of science: social physics. This new field is defined by the author as “a quantitative social science that describes reliable, mathematical connections between information and idea flow on the one hand and people’s behavior on the other” (Pentland, 2014). This science is geared towards developing insights about the “flow of ideas between people” with the objective of creating better social structures such as companies and cities, for example. Pentland notes how the current studies in the social sciences suffer from using static and limited datasets, through which scientists can discover statistical regularities of social phenomena, but can’t exactly build a “causal theory of social structure.” The growing capability of big data technologies, which facilitate the retrieval, storage and analysis of massive amount of individual and peer to peer interaction data, act as the primary enabler of social physics. Through these technologies, the researcher can build a “living laboratory” and mine the “realities” occurring within the social structure using a diverse set of data-rich platforms such as digital sensing platforms and actual individual behavior. It is important to note that social physics rests upon a key premise—a lot of human behavior is habitual and primarily influenced by social learning, which occurs via peer to peer interaction within a social network. Pentland provides sufficient amount of empirical evidence throughout the book in support of this. The author organizes the book into four main parts following an introductory chapter (Chapter 1: From Ideas to Action). The Part I focuses on the field of social physics, whereas the Part II is dedicated to “idea machines”, which discusses the generation of collective intelligence and how social network incentives can be used as a tool to induce organizational change. Parts III and IV are dedicated to data-driven cities and societies respectively. In addition, the author provides four appendices to elaborate on key topics: Reality Mining (Appendix 1), OpenPDS (Appendix 2), Fast, Slow and Free Will (Appendix 3) and Math (Appendix 4). The author concludes this book by providing detailed notes for each chapter, references and index of key terms used in the book. In the upcoming paragraphs, I will provide a review of the key ideas discussed in each part of the book and conclude my review.
{"title":"Social Physics: How Good Ideas Spread-The Lessons from a New Science, by Alex Pentland","authors":"Faruk Arslan","doi":"10.1080/15536548.2016.1243849","DOIUrl":"https://doi.org/10.1080/15536548.2016.1243849","url":null,"abstract":"In Social Physics: How Good Ideas Spread-The Lessons from a New Science, Alex Pentland introduces the readers to a new field of science: social physics. This new field is defined by the author as “a quantitative social science that describes reliable, mathematical connections between information and idea flow on the one hand and people’s behavior on the other” (Pentland, 2014). This science is geared towards developing insights about the “flow of ideas between people” with the objective of creating better social structures such as companies and cities, for example. Pentland notes how the current studies in the social sciences suffer from using static and limited datasets, through which scientists can discover statistical regularities of social phenomena, but can’t exactly build a “causal theory of social structure.” The growing capability of big data technologies, which facilitate the retrieval, storage and analysis of massive amount of individual and peer to peer interaction data, act as the primary enabler of social physics. Through these technologies, the researcher can build a “living laboratory” and mine the “realities” occurring within the social structure using a diverse set of data-rich platforms such as digital sensing platforms and actual individual behavior. It is important to note that social physics rests upon a key premise—a lot of human behavior is habitual and primarily influenced by social learning, which occurs via peer to peer interaction within a social network. Pentland provides sufficient amount of empirical evidence throughout the book in support of this. The author organizes the book into four main parts following an introductory chapter (Chapter 1: From Ideas to Action). The Part I focuses on the field of social physics, whereas the Part II is dedicated to “idea machines”, which discusses the generation of collective intelligence and how social network incentives can be used as a tool to induce organizational change. Parts III and IV are dedicated to data-driven cities and societies respectively. In addition, the author provides four appendices to elaborate on key topics: Reality Mining (Appendix 1), OpenPDS (Appendix 2), Fast, Slow and Free Will (Appendix 3) and Math (Appendix 4). The author concludes this book by providing detailed notes for each chapter, references and index of key terms used in the book. In the upcoming paragraphs, I will provide a review of the key ideas discussed in each part of the book and conclude my review.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72979249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1080/15536548.2016.1243847
Kallol Bagchi, Editor in Chief
This is the last issue of JIPS 2016. It has three articles and one book review. The first article, “Information privacy and the digital generation gap: An exploratory study” by Ruth Halperin and Yuval Dror, observes that more research is needed to understand how privacy is perceived and managed by people of different age groups and what measures are adequate for internet use across generations. They find that younger users are compelled to take privacy risks because of the desire for digital participation, whereas older users are seen to disclose less. They also observe that, “at the heart of the generation gap lies not the issue of awareness but rather the social price and lost opportunities of preserving privacy by restraining self-disclosure.” The next paper authored by O. Folorunso, Femi Ayo and Y. E Babalola introduces a network intrusion detection system based on combinatorial algorithm called CANIDS. The CANIDS uses additional databases for detecting new attacks and speeds up network traffic during traffic analysis. A comparison of CANIDS with similar online schemes was done and results show a better performance in terms of false positive rate and a better accuracy rate. The third article, “Perceived deception: Evaluating source credibility and self-efficacy,” by Dustin Ormond, Merrill Warkentin, Allen C. Johnston, and Samuel C. Thompson deals with scareware messages. They use the responses of 213 university faculty and staff and for analysis use a SEM. The study results show that high levels of deception detection, self-efficacy, and source trustworthiness increase the likelihood of an individual who perceives a scareware message as deceptive. Additionally, the authors observe that technology awareness enhances self-efficacy to detect deception and reduces individual perceptions of source trustworthiness. They also note that behavioral intention to use scareware is lower when the message is perceived as deceptive. In the Book Review section, Faruk Arslan reviews the book titled, Social Physics: How Good Ideas Spread-The Lessons from a New Science, by Alex Pentland. He observes, “Social Physics is an intriguing and well-written book, which contains rich and relevant information for researchers and practitioners, who are interested in computational social science.”
这是JIPS 2016的最后一期。它有三篇文章和一篇书评。第一篇文章，“信息隐私和数字代沟:一项探索性研究”，由Ruth Halperin和Yuval Dror撰写，观察到需要更多的研究来了解不同年龄段的人是如何感知和管理隐私的，以及哪些措施适合跨代使用互联网。他们发现，年轻用户因为渴望数字参与而被迫承担隐私风险，而年长用户则较少披露。他们还观察到，“代沟的核心不在于意识问题，而在于通过限制自我披露来保护隐私的社会代价和失去的机会。”O. Folorunso、Femi Ayo和Y. E . Babalola撰写的下一篇论文介绍了一种基于组合算法CANIDS的网络入侵检测系统。CANIDS使用额外的数据库来检测新的攻击，并在流量分析期间加快网络流量。将CANIDS与类似的在线方案进行了比较，结果表明CANIDS在假阳性率和准确率方面具有更好的性能。第三篇文章，“感知欺骗:评估信息来源的可信度和自我效能”，作者是Dustin Ormond, Merrill Warkentin, Allen C. Johnston和Samuel C. Thompson。他们使用213名大学教职员工的回答，并使用扫描电子显微镜进行分析。研究结果表明，高水平的欺骗检测，自我效能和来源可信度增加了个人认为恐吓信息具有欺骗性的可能性。此外，作者观察到，技术意识提高了自我效能感，以发现欺骗，并降低了个人对来源可信度的看法。他们还注意到，当信息被认为是欺骗性的时候，使用恐吓软件的行为意图就会降低。在书评部分，Faruk Arslan评论了Alex Pentland的《社会物理学:好思想如何传播——新科学的教训》一书。他说:“《社会物理学》是一本有趣的、写得很好的书，它为对计算社会科学感兴趣的研究人员和实践者提供了丰富而相关的信息。”
{"title":"Editorial preface","authors":"Kallol Bagchi, Editor in Chief","doi":"10.1080/15536548.2016.1243847","DOIUrl":"https://doi.org/10.1080/15536548.2016.1243847","url":null,"abstract":"This is the last issue of JIPS 2016. It has three articles and one book review. The first article, “Information privacy and the digital generation gap: An exploratory study” by Ruth Halperin and Yuval Dror, observes that more research is needed to understand how privacy is perceived and managed by people of different age groups and what measures are adequate for internet use across generations. They find that younger users are compelled to take privacy risks because of the desire for digital participation, whereas older users are seen to disclose less. They also observe that, “at the heart of the generation gap lies not the issue of awareness but rather the social price and lost opportunities of preserving privacy by restraining self-disclosure.” The next paper authored by O. Folorunso, Femi Ayo and Y. E Babalola introduces a network intrusion detection system based on combinatorial algorithm called CANIDS. The CANIDS uses additional databases for detecting new attacks and speeds up network traffic during traffic analysis. A comparison of CANIDS with similar online schemes was done and results show a better performance in terms of false positive rate and a better accuracy rate. The third article, “Perceived deception: Evaluating source credibility and self-efficacy,” by Dustin Ormond, Merrill Warkentin, Allen C. Johnston, and Samuel C. Thompson deals with scareware messages. They use the responses of 213 university faculty and staff and for analysis use a SEM. The study results show that high levels of deception detection, self-efficacy, and source trustworthiness increase the likelihood of an individual who perceives a scareware message as deceptive. Additionally, the authors observe that technology awareness enhances self-efficacy to detect deception and reduces individual perceptions of source trustworthiness. They also note that behavioral intention to use scareware is lower when the message is perceived as deceptive. In the Book Review section, Faruk Arslan reviews the book titled, Social Physics: How Good Ideas Spread-The Lessons from a New Science, by Alex Pentland. He observes, “Social Physics is an intriguing and well-written book, which contains rich and relevant information for researchers and practitioners, who are interested in computational social science.”","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90177972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1080/15536548.2016.1243857
Dustin Ormond, Merrill Warkentin, Allen C. Johnston, S. Thompson
ABSTRACT Detecting scareware messages that seek to deceive users with fear-inducing words and images is critical to protect users from sharing their identity information, money, and/or time with bad actors. Through a scenario-based experiment, the present study evaluated factors that aid users in perceiving deceptive communications. An online experiment was administered yielding 213 usable responses. The data from the study indicate high levels of deception detection self-efficacy and source trustworthiness increase the likelihood an individual will perceive a scareware message as deceptive. Additionally, technology awareness enhances self-efficacy to detect deception and reduces individual perceptions of source trustworthiness. Finally, the data significantly illustrate behavioral intention to use scareware is lower when the message is perceived as deceptive.
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Pub Date : 2016-07-02DOI: 10.1080/15536548.2016.1213997
Julian M. Montaquila, Cierra N. Godwin
ABSTRACT Inarguably, social networking sites have become immensely popular. The proliferation of the type and number of social media venues are well exceeded by the quantity of data they produce. Moreover, these sites engender collateral information previously unavailable to personnel security professionals. While a recent literature review suggests this data is increasingly used in background investigations, its analysis appears devoid of any standardized protocol. The absence of any uniform procedure appears confirmed via a recent review of background investigation manuals from state peace officer accreditation organizations. As a result, specific resources, techniques, and case examples are discussed, and the federal security clearance process is provided as a recommended protocol for investigators.
{"title":"Personnel security and open source intelligence: Employing social media analytics in pre-employment screening and selection","authors":"Julian M. Montaquila, Cierra N. Godwin","doi":"10.1080/15536548.2016.1213997","DOIUrl":"https://doi.org/10.1080/15536548.2016.1213997","url":null,"abstract":"ABSTRACT Inarguably, social networking sites have become immensely popular. The proliferation of the type and number of social media venues are well exceeded by the quantity of data they produce. Moreover, these sites engender collateral information previously unavailable to personnel security professionals. While a recent literature review suggests this data is increasingly used in background investigations, its analysis appears devoid of any standardized protocol. The absence of any uniform procedure appears confirmed via a recent review of background investigation manuals from state peace officer accreditation organizations. As a result, specific resources, techniques, and case examples are discussed, and the federal security clearance process is provided as a recommended protocol for investigators.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89078418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-02DOI: 10.1080/15536548.2016.1206758
Wingyan Chung
ABSTRACT Predicting influence in social media (SM) communities has a strong implication for cybersecurity and public policy setting. However, the rapidly growing volume and large variety of SM have made the prediction difficult. Unfortunately, research that combines the power of simulation, SM networks, and SM community features to predict influence is not widely available. In this research, we developed and validated a simulation-based approach to predicting influence in SM communities. The approach uses a power-law distribution to simulate user interaction and leverages statistical distributions to model SM posting and to predict influence of opinion leaders. We applied the approach to analyzing 1,323,940 messages posted by 380,498 users on Twitter about the U.S. border security and immigration issues. Three models for predicting behavioral responses were developed based on exponential distribution, Weibull distribution, and gamma distribution. Evaluation results show that the simulation-based approach accurately modeled real-world SM community behavior. The gamma model achieved the best prediction performance; the Weibull model ranked second; and the exponential model had a significantly lower performance. The research should contribute to developing a simulation-based approach to characterizing SM community behavior, implementing new models for SM behavior prediction, providing new empirical findings for understanding U.S. border security SM community behavior, and offering insights to SM-based cybersecurity.
{"title":"A simulation-based approach to predicting influence in social media communities: A case of U.S. border security","authors":"Wingyan Chung","doi":"10.1080/15536548.2016.1206758","DOIUrl":"https://doi.org/10.1080/15536548.2016.1206758","url":null,"abstract":"ABSTRACT Predicting influence in social media (SM) communities has a strong implication for cybersecurity and public policy setting. However, the rapidly growing volume and large variety of SM have made the prediction difficult. Unfortunately, research that combines the power of simulation, SM networks, and SM community features to predict influence is not widely available. In this research, we developed and validated a simulation-based approach to predicting influence in SM communities. The approach uses a power-law distribution to simulate user interaction and leverages statistical distributions to model SM posting and to predict influence of opinion leaders. We applied the approach to analyzing 1,323,940 messages posted by 380,498 users on Twitter about the U.S. border security and immigration issues. Three models for predicting behavioral responses were developed based on exponential distribution, Weibull distribution, and gamma distribution. Evaluation results show that the simulation-based approach accurately modeled real-world SM community behavior. The gamma model achieved the best prediction performance; the Weibull model ranked second; and the exponential model had a significantly lower performance. The research should contribute to developing a simulation-based approach to characterizing SM community behavior, implementing new models for SM behavior prediction, providing new empirical findings for understanding U.S. border security SM community behavior, and offering insights to SM-based cybersecurity.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77241234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-02DOI: 10.1080/15536548.2016.1213994
Wingyan Chung
Social media (SM) have gained widespread usage in public and private domains. Individuals and organizations use SM to express themselves, to shape political agenda, to gain public acceptance, to bu...
{"title":"Social media analytics: Security and privacy issues","authors":"Wingyan Chung","doi":"10.1080/15536548.2016.1213994","DOIUrl":"https://doi.org/10.1080/15536548.2016.1213994","url":null,"abstract":"Social media (SM) have gained widespread usage in public and private domains. Individuals and organizations use SM to express themselves, to shape political agenda, to gain public acceptance, to bu...","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80795811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-02DOI: 10.1080/15536548.2016.1206757
Zhan Liu, Jialu Shan, Y. Pigneur
ABSTRACT The past few years have witnessed an explosive growth in the use of smartphones. Such widespread use brings with it concerns over the protection of privacy. Building upon existing privacy concern literature, this study has developed a theoretical framework that combines a privacy calculus model with a technology acceptance model (TAM) in the mobile application context. Also examined is the role of personalized services and users’ perceived information control in this domain. Based on a study of 308 participants, the results reveal that perceived enjoyment has replaced perceived ease-of-use as a main predictor of perceived behavioral intentions in a mobile TAM. The findings also show that personalized services and users’ perceived information control have a strong effect on both privacy calculus and mobile TAM.
{"title":"The role of personalized services and control: An empirical evaluation of privacy calculus and technology acceptance model in the mobile context","authors":"Zhan Liu, Jialu Shan, Y. Pigneur","doi":"10.1080/15536548.2016.1206757","DOIUrl":"https://doi.org/10.1080/15536548.2016.1206757","url":null,"abstract":"ABSTRACT The past few years have witnessed an explosive growth in the use of smartphones. Such widespread use brings with it concerns over the protection of privacy. Building upon existing privacy concern literature, this study has developed a theoretical framework that combines a privacy calculus model with a technology acceptance model (TAM) in the mobile application context. Also examined is the role of personalized services and users’ perceived information control in this domain. Based on a study of 308 participants, the results reveal that perceived enjoyment has replaced perceived ease-of-use as a main predictor of perceived behavioral intentions in a mobile TAM. The findings also show that personalized services and users’ perceived information control have a strong effect on both privacy calculus and mobile TAM.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77941955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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