Earticle

초록

영어

Aiming at the low limitation of shilling attack detection technology unsupervised degree, this paper takes the group effect attack profile as the breakthrough point to construct the attack profile groups and the corresponding genetic optimization objective function of quantitative measure of the effects, and prove that the maximum value of the objective function in the ideal state marks the optimum detection effects in ideal situation. On this basis, the combination of genetic optimization process will be adaptive parameter posterior inference and objective function, and proposes the Iterative Bayesian Inference Genetic Detection Algorithm (IBIGDA).Experimental results show that IBIGDA can effectively detect shilling attacks of typical types even in lack of the system or attack-related prior parameters. IBIGDA algorithm can detect common shilling attack, unsupervised degree is high, with the actual application requirements.

목차

Abstract
 1. Introduction
 2. Iterative Bayesian Inference Genetic Detection Algorithm (IBIGDA)
  2.1. The Statistical Characteristics of Exists Attack between the User Profile Attack
  2.2. Generalized Variance Induced Attack Profile Group Effect Metric
  2.3. Genetic Optimization Objective Function
  2.4. Algorithm Description and Interpretation
 3. Experiment Design and Discussion
  4.1. Data Sets and Experimental Setup
  4.2. The Example Analysis of Shilling Attack Detection Process
  4.3. Detection Effect of Shilling Attack
 5. Conclusion
 References

키워드

저자

• Tao Li [ Information Engineering Department, Jilin Police College Jilin 130032, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605