scholarly journals Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

2017 ◽  
Vol 8 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Masoud Al Quhtani

AbstractBackground: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

2011 ◽  
pp. 149-168 ◽  
Author(s):  
Guisseppi A. Forgionne ◽  
Aryya Gangopadhyay ◽  
Monica Adya

There are various forms of fraud in the health care industry. This fraud has a substantial financial impact on the cost of providing healthcare. Money wasted on fraud will be unavailable for the diagnosis and treatment of legitimate illnesses. The rising costs of and the potential adverse affects on quality healthcare have encouraged organizations to institute measures for detecting fraud and intercepting erroneous payments. Current fraud detection approaches are largely reactive in nature. Fraud occurs, and various schemes are used to detect this fraud afterwards. Corrective action then is instituted to alleviate the consequences. This chapter presents a proactive approach to detection based on artificial intelligence methodology. In particular, we propose the use of data mining and classification rules to determine the existence or non-existence of fraud patterns in the available data. The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice.


2014 ◽  
Vol 39 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Paweł Malinowski ◽  
Robert Milewski ◽  
Piotr Ziniewicz ◽  
Anna Justyna Milewska ◽  
Jan Czerniecki ◽  
...  

Abstract The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.


Author(s):  
Nguyen Vinh Nam ◽  
Le Hoai Bac

The  unique properties of spatial data provide challenges  and  opportunities  for  researching  new methods  in  spatial  data  mining.  In  this  article,  we propose  an  interoperable  framework  that  integrates Geographic  Information  System  (GIS)  with  the  spatial data  mining  processto  facilitate  spatial  data preparation,  to  extract  spatial  relationships  that  can take  advantage of traditional data  mining toolkits such as Weka, and to reveal significant spatial patterns. With this approach, it’svery straightforward to adopt spatial access methods and spatial query processing algorithms foran  efficient  data  mining  technique.  Moreover,  our framework  visually  supports  the  complete  spatial  data mining process.


Author(s):  
Stanley R.M. Oliveira

Despite its benefits in various areas (e.g., business, medical analysis, scientific data analysis, etc), the use of data mining techniques can also result in new threats to privacy and information security. The problem is not data mining itself, but the way data mining is done. “Data mining results rarely violate privacy, as they generally reveal high-level knowledge rather than disclosing instances of data” (Vaidya & Clifton, 2003). However, the concern among privacy advocates is well founded, as bringing data together to support data mining projects makes misuse easier. Thus, in the absence of adequate safeguards, the use of data mining can jeopardize the privacy and autonomy of individuals. Privacy-preserving data mining (PPDM) cannot simply be addressed by restricting data collection or even by restricting the secondary use of information technology (Brankovic & V. Estivill-Castro, 1999). Moreover, there is no exact solution that resolves privacy preservation in data mining. In some applications, solutions for PPDM problems might meet privacy requirements and provide valid data mining results (Oliveira & Zaïane, 2004b). We have witnessed three major landmarks that characterize the progress and success of this new research area: the conceptive landmark, the deployment landmark, and the prospective landmark. The Conceptive landmark characterizes the period in which central figures in the community, such as O’Leary (1995), Piatetsky-Shapiro (1995), and others (Klösgen, 1995; Clifton & Marks, 1996), investigated the success of knowledge discovery and some of the important areas where it can conflict with privacy concerns. The key finding was that knowledge discovery can open new threats to informational privacy and information security if not done or used properly. The Deployment landmark is the current period in which an increasing number of PPDM techniques have been developed and have been published in refereed conferences. The information available today is spread over countless papers and conference proceedings. The results achieved in the last years are promising and suggest that PPDM will achieve the goals that have been set for it. The Prospective landmark is a new period in which directed efforts toward standardization occur. At this stage, there is no consensus on privacy principles, policies, and requirements as a foundation for the development and deployment of new PPDM techniques. The excessive number of techniques is leading to confusion among developers, practitioners, and others interested in this technology. One of the most important challenges in PPDM now is to establish the groundwork for further research and development in this area.


Author(s):  
Gadekar Ganesh Bhivsen ◽  
Udayabhanu N P G ◽  
Dange Bapusaheb Jalindar ◽  
Vengatesan K ◽  
Abhishek Kumar

Security of a data system is a significant property, particularly today when PCs are interconnected by means of the internet. Since no system can be totally secure, the opportune and precise detection of intrusions is essential. Cyber security is the region that manages shielding from cyber terrorism. Cyber-attacks incorporate access control infringement, unapproved intrusions, and disavowal of service just as insider risk. For this reason, IDS were planned. The IDS in the mix with DM can give security to the next level. DM is the way toward presenting inquiries and separating designs, frequently already ambiguous from huge amounts of data utilizing design coordinating or other thinking techniques. This Paper gives the IDDMS (Intrusion Detection with Data Mining system) Framework which is a mix of data mining techniques with the Intrusion detection system, this can be utilized in Cyber-security for accomplishing the next level of service.


Sign in / Sign up

Export Citation Format

Share Document