Asma Ben Letaifa

Forensic investigation in Mobile Cloud environment

Cloud computing is changing the way we use mobile application by offering a powerful, scalable and on-demand computational resources to mobile users. However, this new paradigm is a challenging issue for forensic investigators since it combines two different environments. The adoption of Mobile Cloud computing solutions is increasing rapidly, so it is necessary for digital investigation applications and procedure to be adapted to this new environment. This paper provides an overview of mobile cloud forensics including challenging issues and some existing proposals in order to overcome these challenges.

Energy consumption comparison for mobile video streaming encryption algorithm

Video streaming services over the Internet are being widely used in mobile networks. Currently, service providers are relying more and more on securing data transferred between the service provider, namely the video streaming server, and the end user. However, securing video content includes at least the use of video encryption algorithm requiring processing and memory capabilities, provoking more delay and energy consumption. In any other context, energy consumption may not be a very important issue, but when using a mobile device, it will certainly become one. In this paper, we are dressing a comparison between different symmetric encryption algorithms, in terms of energy consumption, while decrypting and viewing a video stream on mobile device.

ML based QoE enhancement in SDN context: Video streaming case

In todays world, video streaming rose above all other types of traffic. In fact, providing this service with a high quality presents the most challenging task among the advancements in networking technologies. Researchers are trying to help creating a more efficient network where congestion, broadband limitations and skyrocketing number of users present ever-diminishing obstacles. When it comes to us, we present in this paper a machine learning approach combined with adaptive coding in order to provide a better QoE for video streaming services. This solution will be established using SDN architecture. We can justify this choice because we need a centralized architecture, where the totality of the network is known, to predict its status. So, we will implement a machine learning algorithm in the controller: this algorithm, called ML based SSIM, will calculate approximately the quality needed for a video to be streamed. Finally, the quality found by the ML-based SSIM Algorithm will be combined with the network situation to choose the right coding. First part of the paper deals with a brief introduction of SDN networks, QoE requirement and ML algorithms. Secondly, we expose the SSIM approach and explain how our proposed one is based on. The last part of the paper deals with experiments: we describe SDN environment deployment, describe scenarios and give at the end results and values. We highlight at the end the future of our proposition.

Real Time ML-Based QoE Adaptive Approach in SDN Context for HTTP Video Services

Due to the high dynamism of network conditions, operators and service providers are facing the challenge of providing satisfactory user experience during a real-time video streaming session where clients are often suffering from frequent interruptions and significant visual quality degradation. Video parameters such as playback quality, rate switching amplitude/frequency, occupancy, overflow/underflow buffer are the main key factors responsible for affecting the user experience’s quality. Recently, adaptive streaming protocols over HTTP have become widely adopted for providing continuous video streaming services to users with their different heterogeneous devices under dynamic network conditions. In this paper, we leverage the emerging paradigm of software defined networking SDN. Our contribution consists in developing some scenarios on SDN helping to adapt video streaming to the network state. The current work proposes to experience ML algorithms in order to predict user QoE over SDN networks. We present an approach that collects MOS score from users under varying network parameters as well as objective parameters such as SSIM, VQM and PSNR. The MOS scores are collected by playing videos to actual users in an SDN environment. We design an architecture which could use the measured MOS values under varying network conditions to predict the expected MOS based on machine learning algorithms. This work provides an outlook of experiments done for demonstration, by describing SDN environment deployment, detailing the realized scenarios and finally giving the results and values. We highlight, at the end of this paper, the perspectives of our proposition.

A secure data storage based on revocation game-theoretic approaches in cloud computing environments

Cloud computing is an innovative model that is gaining attention and wide popularity among IT professionals and users. It provides a new mode of use of IT resources by offering everything as a service (storage, servers, network, etc.) and provisioning resources on-demand. In cloud computing environments, users outsource their data to distributed data centers in the cloud and entrust cloud providers with its storage and processing. However, data outsourcing and multi-tenants introduce new security issues that have to be addressed. In fact, being stored externally, data are no longer under the control of their owners. The storage and processing mechanisms may not be fully trustworthy and thus data confidentiality and privacy may be at risk. Therefore, in this paper, we will deal with securing stored data in shared cloud computing environment. First, we will present an overview of the main security issues addressed in the literature. Then we will focus on providing a game-theoretic model to secure the stored data from malicious users. To that purpose, we have proposed a sequential revocation game where cloud users have to choose the best strategy from a set of three strategies in order to revoke and eliminate the malicious user while minimizing their costs. At the end, we will discuss the different outcomes of this game using the subgame perfect Nash equilibrium concept.

Optimal scheduling of CIOQ switches resources in virtual network environments

Nowadays, virtualization is an important technique that offers illimited resources to network users. It allows users to share switchs resources in time and space. Sharing in time is done by scheduling algorithms determining at which moment each input queue is matched with an output queue. Regarding sharing in space, it takes place in the queues which are split into multiple virtual queues. Being this, the switches have to support scheduling features and efficient algorithms to match input and output queues. In this paper, we deal with scheduling and resource management in Combined Input Output Queued (CIOQ) Switches. In this paper, we are intended to propose a scheduling scheme that shares switchs resources and guarantees fairness between different flows.