Earticle

초록

영어

Internet worms are malware programs that imitate themselves and spread around the network. Internet worm, a wide spreading malcode exploits vulnerability in the operating system, hard disk, software and web browsers. This paper analyzes and classifies the Internet worm, depending on the training signatures. This work presents the Internet worm detection mechanism, using Principal Component Analysis (PCA) and Support Vector Machine (SVM). A Selective sampling technique is applied to maximize the performance of the classifier and to reduce misleading data instances. The results obtained show improved memory utilization, detection time and detection accuracy for Internet worms.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Proposed Methodology
  3.1. Principal Component Analysis
  3.2. Multi-class Support Vector Machine
  3.3. Selective Sampling
 4. Experimentaion and Results
 5. Conclusion
 References

키워드

저자

• S.Divya [ Research Scholar, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, University, Coimbatore, India. ]
• Dr.G.Padmavathi [ Professor and Head, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, University, Coimbatore, India. ]

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