Classifying Database Users for Intrusion Prediction and Detection in Data Security
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The fact that users and applications acquire information using web sites on the internet means that document and information sharing, banking and other operational processes are increasing day by day.Recently however, with the widespread use of the internet, some security problems, such as unauthorized access, data breaches, code infection, malware infections, data leaks and distributed denial of service attacks have emerged.This situation necessitates the protection of the information used in personal and public spaces.In this study, a common model was created to detect user intrusions by taking into account criteria such as the number of transactions performed, their IP addresses, the amount of data they use, the transaction type they perform and the roles they undertake.In this way, the aim was to ensure database security by detecting risky user groups in advance.Keywords:
Database security
Adaptability
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Intrusion Detection System (IDS) is used to detect intrusion and then alert the system administrator about the intrusion. This is what traditional IDS is all about. It is then up to the system administrator to deal with the intrusion. Human intervention is still needed when it comes to dealing with intrusion. This is because traditional IDS could only detect the intrusion but could not, on its own respond towards the intrusion. IDS is only able to alert the system administrator when it detects an intrusion. How and when the intrusion is dealt with is up to the system administrator. Human intervention when dealing with intrusion is not a problem if the person assigned to that task is always reliable. Therefore, this paper analyzes the evolution of IDS and how mobile agents such as SNORT could increase the integrity of traditional systems without human intervention.
System administrator
Network administrator
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It is the biggest security problems of the database that directly accessing to the database bypassing the management system,or using OS tools to steal or tamper with the contents of the database file.Encrypting sensitive data of the database is one of the most effective ways to solve these problems.This paper introduces a method of solving database security problem base on middleware between the inside and outside of the database.
Database security
Base (topology)
Database administrator
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Aiming at some deficiencies of existing network intrusion detection system, the paper proposes a network intrusion detection system model based on data mining, applying data mining technology to network intrusion detection, and constructed the final test results of the system on the basis of Snort design. Experimental results demonstrate that this data mining based on cluster algorithm can effectively establish models of network normal activity and significantly accelerate intrusion detection, whilst its association analyzer can effectively unearth some new intrusion patterns from abnormal logs, and automatically construct intrusion detection rules.
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With the accelerating pace of information technology,database security have become increasingly demanding.How to transplant database companies need to face a major problem.In response to this phenomenon,the article detailed describes how a database will be ported to the new server,including the installation of the database patch,detach the database,add database and resolve orphaned users etc.
Database security
Database server
Pace
Porting
Autocommit
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In today’s world, data is generated at a very rapid speed and final destination of such data is the database. Database securityassures the security of the information stored in the database against threats, be it insider or outsider threats. Data is stored in the database for easy and efficient way to manage these data. Considering the importance of data in an organization, it isabsolutely essential to secure the data stored in the database. A secure database is one which is shielded from different possibleattacks. For data protection, enforcement of access control policies based on data contents, subject qualifications and characteristics and other relevant contextual information, such as time mechanisms are used. Security models are required in thedesign and development of effective and efficient database systems. In this work, some of the attacks and threats that areencountered in database systems and the corresponding counter-measures, as well control methods were discussed. Ensuringsecurity for database is a very critical issue for companies. Hence as the complexity of database increases, we may tend to havemore complex security issues of database. The effectiveness of the developed system in protecting data stored in a database wasdemonstrated by attempting some attacks on the database. The system was able to thwart the attacks.
Database security
Data administration
Intelligent database
Database administrator
Physical data model
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Most traditional techniques in intrusion detection are mining the rule patterns of each attacks' features from the data we have known,then match the new data with these rules.However,the main problem of rule based intrusion detection techniques is that the current rule patterns can not effectively manage the new continuously changing intrusion detection attacks.To deal with the problem,data mining based intrusion detection methods have been the hot fields in intrusion detection research.An outlier detection based adaptive intrusion detection framework is proposed in this paper.In the proposed framework,the outliers are firstly detected by similarity coefficient.And then,the clusters are built on the detected outlier data set and the improved association rule algorithm is employed on the clusters.Finally,the rules generated by association rule algorithm will be adaptively added into the current intrusion detection rule base.The experiment platform was based on IDS Snort and IDS Informer was employed to simulate the attack and test.The experiments performed on simulated data and KDD99 from UCI data set have shown the effectiveness of proposed methods.
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This paper analyzed the application of data mining in intrusion detection system based on a research on IDS,data mining and the types and limitations of traditional detection methods.According to the characteristic of IDS,it was pointed out that these limitations could be overcome by the data mining technology.The intrusion detection technology improved and optimized the association and cluster rules,which resolved its own disadvantages,including its inability to presage the number of the best cluster and its fine classification.Such an improvement reduces omissions and misstatement,and improves the efficiency of IDS.Experimental results show that this algorithm is feasible.
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