Intrusion Detection Using Fuzzy Meta-Heuristic Approaches

@article{Bahamida2014IntrusionDU,
  title={Intrusion Detection Using Fuzzy Meta-Heuristic Approaches},
  author={Bachir Bahamida and Dalila Boughaci},
  journal={Int. J. Appl. Metaheuristic Comput.},
  year={2014},
  volume={5},
  pages={39-53}
}
Due to a growing number of intrusion events, organizations are increasingly implementing various intrusion detection systems that classify network traffic data as normal or anomaly. In this paper, three intrusion detection systems based fuzzy meta-heuristics are proposed. The first one is a fuzzy stochastic local search (FSLS). The second one is a fuzzy tabu search (FTS) and the third one is a fuzzy deferential evolution (FDE). These classifiers are built on a knowledge base modelled as a fuzzy… 
4 Citations

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms: Tabular and Graphical Analyses

TLDR
A comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed.

Soccer Game Optimization: An Innovative Integration of Evolutionary Algorithm and Swarm Intelligence Algorithm

TLDR
A new metaheuristic algorithm that elaborates the reproduction process in evolutionary algorithm with the powerful information sharing in the swarm intelligence algorithm and evolutionary algorithm respectively is proposed.

Chapter 4 Synthesis of Object-Oriented Software Structural Models Using Quality Metrics and CoEvolutionary Genetic Algorithms

One of the biggest challenges for the developer of object-oriented software is the modeling and developing of the objects themselves, so that they are easily reusable in complex systems. The final

CSI Based Multiple Relay Selection and Transmit Power Saving Scheme for Underlay CRNs Using FRBS and Swarm Intelligence

TLDR
The proposed algorithm would allow for end-to-end coordination of the SNR network’s end- to-end power-saving methods through an integrated “smart ring” system.

References

SHOWING 1-10 OF 29 REFERENCES

Evolving Fuzzy Classifiers for Intrusion Detection

TLDR
The main idea is to evolve two rules, one for the normal class and other for the abnormal class using a profile data set with information related to the computer network during the normal behavior and during intrusive behavior.

Intrusion Detection Using Fuzzy Stochastic Local Search Classifier

  • B. BahamidaD. Boughaci
  • Computer Science
    2012 11th Mexican International Conference on Artificial Intelligence
  • 2012
TLDR
The proposed classifier works on knowledge base modeled as a fuzzy rule "if-then" and improved by using a stochastic local search and compared with other existing techniques for intrusion detection.

Bayesian event classification for intrusion detection

TLDR
Experimental results show that the accuracy of the event classification process is significantly improved using the proposed Bayesian networks, which improve the aggregation of different model outputs and allow one to seamlessly incorporate additional information.

Fuzzy clustering for intrusion detection

TLDR
This paper presents the preliminary results of the use of fuzzy clustering to detect anomalies within low level kernel data streams and explores how fuzzy data mining and concepts introduced by the semantic Web can operate in synergy to perform distributed intrusion detection.

RETRACTED: A Bayesian Networks in Intrusion Detection Systems

TLDR
The accuracy of the event classification process is significantly improved using the proposed approach for reducing the missing- alarm using the use of recursive Log-likelihood and entropy estimation as a measure for monitoring model degradation related with behavior changes and the associated model update.

A data mining framework for building intrusion detection models

  • Wenke LeeS. StolfoK. Mok
  • Computer Science
    Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344)
  • 1999
TLDR
A data mining framework for adaptively building Intrusion Detection (ID) models is described, to utilize auditing programs to extract an extensive set of features that describe each network connection or host session, and apply data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities.

Naive Bayes vs decision trees in intrusion detection systems

TLDR
It is shown that even if having a simple structure, naive Bayes provide very competitive results, and the good performance of Bayes nets with respect to existing best results performed on KDD'99.

Training a neural-network based intrusion detector to recognize novel attacks

TLDR
An experiment with an IDS composed of a hierarchy of neural networks (NN) that functions as a true anomaly detector is described, showing that using small detectors in a hierarchy gives a better result than a single large detector.

Mining Audit Data to Build Intrusion Detection Models

TLDR
A data mining framework for constructing intrusion detection models to mine system audit data for consistent and useful patterns of program and user behavior, and use the set of relevant system features presented in the patterns to compute classifiers that can recognize anomalies and known intrusions.

An Intrusion-Detection Model

  • D. Denning
  • Computer Science
    IEEE Transactions on Software Engineering
  • 1987
A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that