國立虎尾科技大學 |

Predicting the Adoption of Big Data Security Analytics for Detecting Insider Threats.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Predicting the Adoption of Big Data Security Analytics for Detecting Insider Threats./
作者:	Lombardo, Gary.
面頁冊數:	1 online resource (121 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
標題:	Information technology. -
電子資源:	click for full text (PQDT)
ISBN:	9780355828849

Predicting the Adoption of Big Data Security Analytics for Detecting Insider Threats.
Lombardo, Gary.

Predicting the Adoption of Big Data Security Analytics for Detecting Insider Threats. - 1 online resource (121 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--Capella University, 2018.

Includes bibliographical references

Increasingly, organizations are at risk of data breaches due to corporate insider threats. Insiders, in fact, are the biggest threat to corporate data assets and are evading traditional cybersecurity countermeasures. The volume of big data makes insider threat detection more difficult. Conversely big data security analytics (BDSA) enables the detection of anomalous behavior patterns within large datasets in real time, offering organizations potentially a more effective cybersecurity countermeasure for detecting insider threats. However, there was a gap in the literature about what was known about information technology (IT) professionals' behavioral intentions (BIs) to adopt BDSA. The overarching management question of this study was whether IT professionals' BIs to adopt BDSA were influenced by perceived usefulness (PU) and perceived ease of use (PEOU). This management question led to the investigation of three research questions: The first was if there was a statistically significant relationship between PU and an IT professional's BI to adopt BDSA. The second was if there was a statistically significant relationship between PEOU and an IT professional's BI to adopt BDSA. And, the third was does an IT professional's PEOU of BDSA influence the PU of BDSA. The study used a quantitative, nonexperimental, research design with the technology acceptance model (TAM) as the theoretical framework. Participants included 110 IT professionals with five or more years of experience in the IT field. A Fast Form Approach to Measuring Technology Acceptance and Other Constructs was used to collect data. The instrument had 12 items that used (a) semantic differential scales that ranged in value from -4 to +4 and (b) bipolar labels to measure the two independent variables, PU and PEOU. Multiple linear regression was used to measure the significance of the relationship between PU and BI, and PEOU and BI. Also measured was the moderating effect of the independent variable, PEOU, on the dependent variable, PU. Finally, multivariate adaptive regression splines (MARS) measured the predictive power of the TAM. The findings of this study indicate a statistically significant relationship between PU and an IT professional's BI to adopt BDSA and a statistically significant relationship between PEOU and PU. However, there was no statistically significant relationship between PEOU and an IT professional's BI to adopt BDSA. The MARS analysis indicated the TAM had strong predictive power. The practical implications of this study inform IT practitioners on the importance of technology usefulness. In the case of BDSA, the computational outcome must be reliable and provide value. Also, given the challenges of developing and effectively using BDSA, addressing the issue of ease of use may be important for IT practitioners to adopt and use BDSA. Moreover, as an IT practitioner gains experience with BDSA, the ability to extract value from big data influences PEOU and strengthens its relationship with PU.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355828849Subjects--Topical Terms:

559429
Information technology.
Index Terms--Genre/Form:

554714
Electronic books.

Predicting the Adoption of Big Data Security Analytics for Detecting Insider Threats.
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