Application of Big Data Intelligent Algorithms in Enterprise Security Risk Control

Author(s):  
Xiaogang Gong ◽  
Wei Ye ◽  
Yaqiong Guo ◽  
Chao Chen
2022 ◽  
pp. 83-112
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.


2021 ◽  
Author(s):  
zhu rongrong

Abstract Contactless medical equipment AI big data risk control and quasi thinking iterative planning,The tanh equilibrium state of heavy core clustering based on hierarchical fuzzy clustering system based on differential incremental equilibrium theory is adopted. Successfully control the parameter group of CT / MR machine internal data, big data AI mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT / MR in the whole life cycle. It provides help for the predictive maintenance of early pre inspection and orderly maintenance of the medical system. It also puts forward and designs the big data depth statistics of AI risk control medical equipment, and establishes the standardized model software. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance ), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are carried out in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time. Finally, after adding micro vibration quasi thinking iterative planning to the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and images. This kind of AI big data risk control improves the intelligent management ability of medical institutions, establishes the software for predictable maintenance of AI big data, which is cross platform and embedded into the web system.


2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Song He ◽  
Can Zhang ◽  
Wei Guo ◽  
Li-Dong Zhai

The integration of the Internet and Mobile networks results in huge amount of data, as well as security threat. With the fragile capacity of security protection, worms can propagate in the integration network and undermine the stability and integrity of data. The propagation of worm is a great security risk to massive amounts of data in the integration network. We propose a kind of worm propagating in big data environment named BD-Worm. BD-Worm consumes computing resources and gets privacy information of users, which causes huge losses to our working and living. This paper constructs an integration network topology model and designs the BD-Worm propagating in the big data environment. To analyze the propagation of BD-Worm, we conduct a simulation and provide some recommendations to contain the widespread of BD-Worm according to the simulation results.


2021 ◽  
Vol 6 (1) ◽  
pp. 40
Author(s):  
Ziwei Wang

China’s economic development is from high speed to high quality. Developing consumer finance has become a new growth point of China’s economic development and one of the important ways to expand domestic demand. Consumer finance is based on the development of new generation Internet technology and big data, becoming a new trend of China’s financial development and attracting the attention of various industries.


2019 ◽  
Author(s):  
Yannian Hu ◽  
Hui Wang ◽  
Wenge Ma

Abstract With the application and comprehensive development of big data, the need for effective research on cloud workflow management and scheduling is becoming more and more urgent. However, there are currently suitable methods for effective analysis. In order to find out how to effectively manage and schedule smart cloud workflows, the article studies big data from different aspects and draws the following conclusions: Compared with the original JStorm system, the average response time is shortened by up to 58.26%, and the average is shortened. 23.18%; CPU resource utilization increased by 17.96%, an average increase of 11.39%; memory utilization increased by 88.7%, an average increase of 71.16%. In optimizing the dynamic combination of web services, the overall performance of MOACO algorithm and CCA algorithm is better than GA algorithm, and the average performance of MOACO algorithm is better than CCA algorithm. The paper also proposes a cloud workflow scheduling strategy based on intelligent algorithms and adjusting the perceived cloud service resource combination strategy to realize two-layer scheduling of cloud workflow tasks. We have studied three representative intelligent algorithms (ACO algorithm, PSO algorithm and GA algorithm) and designed and improved them for scheduling optimization. It can be clearly seen that in the same scenario, in different test cases the optimal values of the different algorithms vary greatly. However, the optimal solution curve is substantially consistent with the trend of the mean curve.


10.28945/4799 ◽  
2021 ◽  
Vol 18 ◽  
pp. 041-061
Author(s):  
Shannon Block ◽  
Steven Munkeby ◽  
Samuel Sambasivam

Aim/Purpose: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained.


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