Earticle

초록

영어

With varied and widespread attacks on information systems, intrusion detection systems (IDS) have become an indispensable part of security policy for protecting data. IDS monitor event logs and network traffic to uncover suspicious connections that deviate from the regular profile and identify them as threats or attacks. Like most of the cases the dataset used for intrusion detection i.e., KDD99 suffers two problems: imbalanced class distribution and curse of dimensionality. In this work SMOTE has been used for balancing the dataset and once balanced, Principal Component Analysis (PCA) has been used to extract the features. And after that on the transformed dataset Correlation based Feature Selection (CFS) is used to select a subset of important features. The reduced dimension dataset is tested with Support Vector Machines (SVM). Obtained results demonstrate improved detection accuracy, computational efficiency with minimal false alarms and less system resources utilization

목차

Abstract
 1. Introduction
 2. Literature Survey
 3. Techniques Used in Work
  3.1. Support Vector Machine (SVM)
  3.2. Principal Component Analysis
  3.3. Correlation Based Feature Selection
  3.4. Synthetic Minority Oversampling Technique(SMOTE)
 4. Experimental Setup
 5. Results and Discussions
 6. Conclusion
 References

키워드

저자

• Yasir Hamid [ Research Scholar, Dept. of CSE Pondicherry Engineering College ]
• M.Sugumaran [ Professor and Head, Dept. of CSE, Pondicherry Engineering College ]
• Ludovic Journaux [ Associate Professor, University of Burgundy ]

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