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International Journal of Multimedia and Ubiquitous Engineering

간행물 정보
  • 자료유형
    학술지
  • 발행기관
    보안공학연구지원센터(IJMUE) [Science & Engineering Research Support Center, Republic of Korea(IJMUE)]
  • pISSN
    1975-0080
  • 간기
    월간
  • 수록기간
    2008 ~ 2016
  • 등재여부
    SCOPUS
  • 주제분류
    공학 > 컴퓨터학
  • 십진분류
    KDC 505 DDC 605
Vol.5 No.3 (2건)
No
1

Covert Channel Communication by Betterment Steganography

Amiruzzaman Md, Hassan Peyravi, M. Abdullah-Al-Wadud, Yoojin Chung

보안공학연구지원센터(IJMUE) International Journal of Multimedia and Ubiquitous Engineering Vol.5 No.3 2010.07 pp.1-14

※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

This paper is presenting a betterment steganographic method for covert channel communication. Two distinct methods are combined to achieve possibly high data hiding capability with high visual quality. The proposed method shifts the last n nonzero AC coefficients from S JPEG block, and changes the magnitude values of the first n nonzero AC coefficients from T JPEG blocks. S and T blocks are determined by the number of nonzero JPEG coefficients in the block. Zero run-length modification method improves the robustness against statistical attack based on magnitude histogram. Magnitude modification method improves the visual quality. This combination complements each other.

2

Dynamically Self-adapting and Growing Intrusion Detection System

Longy O. Anyanwu, Jared Keengwe, Gladys A. Arome

보안공학연구지원센터(IJMUE) International Journal of Multimedia and Ubiquitous Engineering Vol.5 No.3 2010.07 pp.15-22

※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

The ever-growing use of the Internet comes with a surging escalation of communication and data access. Most existing intrusion detection systems have assumed the one -size-fits-all solution model. Such IDS is not as economically sustainable for all organizations. Furthermore, studies have found that Recurrent Neural Network out-performs Feed-forward Neural Network, and Elman Network. This paper, therefore, proposes a scalable application-based model for detecting attacks in a communication network using recurrent neural network architecture. Its suitability for online real-time applications and its ability to self-adjust to changes in its input environment cannot be over-emphasized.

 

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