Accepted Papers

  • Advanced LSB Technique For Audio Stenography
    Mohammed Salem Atoum1, Mohammad M Alnabhan2 and Ahmad Habboush3, 1Irbid National University, Irbid, Jordan, 2Mutah University, Jordan, 3Jerash University, Jordan.
    This work contributes to the multimedia security fields by given that more protected steganography technique which ensures message confidentiality and integrity. An Advanced Least Significant Bit (ALSB) technique is presented in order to meet audio steganography requirements, which are imperceptibility, capacity, and robustness. An extensive evaluation study was conducted measuring the performance of proposed NLSB algorithm. A set of factors were measured and used during evaluation, this includes; Peak Signal to Noise Ratio (PSNR) and Bit Error Rate. MP3 Audio files from five different audio generators were used during evaluation. Results indicated that ALSB outperforms standard Least Significant Bit (SLSB) technique. Moreover, ALSB can be embedding an utmost of 750 kb into MP3 file size less than 2 MB with 30db average achieving enhanced capacity capability.
  • Towards a collaborative architecture of Honeypots
    Abdeljalil AGNAOU1, Anas ABOU EL KALAM1, Abdellah AIT OUAHMAN1, and Mina DE MONTFORT2, 1Cadi Ayyad University, Morocco, 2ARTIMIA, Paris FRANCE.
    with the considerable growing of the information Systems, the number of attacks is increasing; these attacks have as targeted networks and systems of individuals, enterprises, organizations or administrations. This paper is about making Distributed Honeypot-Based architecture within the DMZ of many institutions in Morocco, to evaluate and analyse malicious traffic, study attacker's behaviours and their intentions, etc. First of all, we introduce the project and its objectives; then, we propose an explanation of concepts of honeypots, honeynets, and some honeypot management frameworks. In the third part, we give details of our architecture for the Moroccan Honeypot Project with some results from the implementation of a sensor within ENSA of Marrakech DMZ network; and finally, present our prospects for the project.
  • Securing Online Accounts Via New Handshake Protocol And Granular Access Control
    Securing Online Accounts Via New Handshake Protocol And Granular Access Control
    When we need to make informed financial decisions, we seek out tools in order to assist us with managing and aggregating our finances. Traditionally, money management software packages have been available for personal computers, however, they were expensive and required steep learning curve. With a paradigm shift to cloud-computing users are looking toward the web for an easier and low cost solution. As a result, third-party companies have been formed to fill this gap. However, users have to share their login credentials with the third-party and if that information gets compromised, an attacker can access and perform transactions on their account. We present a novel, holistic model with a new handshake protocol and access control, which authenticates and forms a sandbox around a third-party access. When utilizing these novel techniques, users' original login credentials can remain private and no one would be able to perform transactions on the users' account.
  • Enhanced Face Recognition Based Fractal Code And Deep Belief Networks
    Benouis Mohamed1, Senouci Mohamed1 and Tlmesani redouane2, 1University of Oran, Algeria, 2INTTIC, Algeria.
    An enhanced algorithm is proposed to recognize the human face using bi-dimensional features and deep belief networks. The proposed method is meant to be robust versus all possible variations acquiring in the acquired image of the human face, so to ensure accurate recognition despite the disturbances affecting the measurements such as the occultation, changes in lighting, pose, expression or any structural components. In this scope, this work proposes an approach based on local binary pattern (LBP), fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with deep belief networks (DBN) classifier. A practical evaluation is performed through comparison with probability neural network (PNN) and nearest neighbors (KNN) on three databases namely FERET, ORL and FEI. The results clearly suggest the effectiveness and robustness of the proposed approach.
  • New Efficient Non-Coprime Weighted To RNS For Conjugate-Pair-Moduli Residue Number System
    Mansour Bader1, Andraws Swidan2 and Mazin Al-hadidi3, 1,3Al-Balqa'a Applied University, Jordan, 2Jordan University, Jordan
    In this paper new efficient Binary-to-RNS converters for multi-moduli RNS based on sets { 2n1 - 2, 2n1 + 2, 2n2 - 2, 2n2 + 2, .., 2nN - 2, 2nN + 2 } are presented. The modulies 2n - 2, 2n + 2 are called conjugates of each other. Multi-moduli RNS processors offer these benefits; relying on the sets with pairs of conjugate moduli : 1) Large dynamic ranges. 2) Fast and balanced RNS arithmetic. 3) Simple and efficient RNS processing hardware. 4) Efficient weighted-to-RNS and RNS-to-Weighted converters. The dynamic range achieved by the set above is defined by the least common multiple of the moduli. This new non-coprime conjugate is unique and the only one of its shape as to be shown.
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