• Corpus ID: 2824938

An Overview of Database Centred Intrusion Detection Systems

@inproceedings{Adebowale2013AnOO,
  title={An Overview of Database Centred Intrusion Detection Systems},
  author={Ajayi Adebowale and A IdowuS. and Otusile Oluwabukola},
  year={2013}
}
Intrusion detection systems have become a major component of network security infrastructures. Modern day intrusion detection systems are to be reliable, extensible, adaptive to the flow of network traffic and to have a low cost of maintenance. Over the years researchers have looked upon data mining as a means of enhancing the adaptability of an intrusion detection system, as it enables the IDS to discover patterns of intrusions and define valid bounds of network traffic. Despite the… 
No Paper Link Available
6 Citations
Background Citations
4

Figures from this paper

6 Citations

Learning Classifier Systems for Adaptive Learning of Intrusion Detection System

This paper applies accuracy based learning classifier systems to relational database and confirms that adaptive learning is possible and confirmed their practical usability that they close to the best accuracy, though were not the best.

Learning Classifier Systems for Adaptive Learning of Intrusion Detection System

This paper applies accuracy based learning classifier systems to relational database and confirms that adaptive learning is possible and confirmed their practical usability that they close to the best accuracy, though were not the best.

Anomalous query access detection in RBAC-administered databases with random forest and PCA

Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation

A method to learn the bayesian network for group emotion prediction in kindergarten from data through evolutionary computation, and compares the evolution of the best model to analyze the influential factors that determine the structure.

Applying accuracy-based LCS to detecting anomalous database access

In the problem of database intrusion detection which changes the detecting targets, the patterns of rule generation are found and analyzed to show systemically how the adaptive learning of XCS algorithm is working in practical usage.

A Comparison of Data Mining Techniques for Anomaly Detection in Relational Databases

Evaluated feature selection and data mining algorithms in the context of database IDS show that principal components analysis produces a reasonably compact and meaningful subset of features while graphical models like decision trees, random forest, and Bayesian networks yield a consistently high performance in detecting anomalies in databases.

References

SHOWING 1-10 OF 24 REFERENCES

Creation and deployment of data mining-based intrusion detection systems in Oracle Database l0g

  • M. CamposB. Milenova
  • Computer Science
    Fourth International Conference on Machine Learning and Applications (ICMLA'05)
  • 2005
DAID is described, a database-centric architecture that leverages data mining within the Oracle RDBMS to address these challenges and offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability.

A framework for constructing features and models for intrusion detection systems

A novel framework, MADAM ID, for Mining Audit Data for Automated Models for Instrusion Detection, which uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns.

Architecture for Data Collection in Database Intrusion Detection Systems

A new approach to data collection for database IDSs is presented by situating data collecting sensors on the database server and having the data transmitted to the audit server on a physically different site for further processing.

A Real-Time Intrusion Prevention System for Commercial Enterprise Databases

This paper presents an overview of the work in creating a practical database intrusion detection system that detects a wide range of specific and general forms of misuse, provides detailed reports, and has a low false-alarm rate.

Adaptive Model Generation

Adaptive Model Generation (AMG) is presented, a real time architecture for implementing data mining-based intrusion detection systems by automating the collection of data, the generation and deployment of detection models, and the real-time evaluation of data.

Weighted Intra-transactional Rule Mining for Database Intrusion Detection

This paper proposes an intrusion detection algorithm named weighted data dependency rule miner (WDDRM) for finding dependencies among the data items and shows that WDDRM handles the modification of sensitive attributes quite accurately.

Database Intrusion Detection using Weighted Sequence Mining

An algorithm for finding dependencies among important data items in a relational database management system and an extension to the Entity- Relationship (E-R) model to syntactically capture the sensitivity levels of the attributes is proposed.

Intrusion detection in RBAC-administered databases

The preliminary experimental evaluation on both real and synthetic database traces show that the proposed ID mechanisms work well in practical situations, and the use of roles makes the approach usable even for databases with large user population.

Intrusion detection in real-time database systems via time signatures

  • V. LeeJ. StankovicS. Son
  • Computer Science
    Proceedings Sixth IEEE Real-Time Technology and Applications Symposium. RTAS 2000
  • 2000
The authors describe a method for intrusion detection applied to real time database systems that will be tagged with "time semantics" that capture expectations about update rates that are unknown to the intruders.

Intelligent Multi-agent Based Database Hybrid Intrusion Prevention System

A framework for highly distributed real-time monitoring approach to database security using Intelligent Multi-Agents and a combination of both statistical anomaly prevention and rule based misuse prevention in order to detect a misuser.