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Journal of Cyber Security and Mobility

Ashutosh Dutta, Johns Hopkins University, USA
Ruby Lee, Princeton University, USA
Neeli R. Prasad, International Technological University, San Jose, USA
Wojciech Mazurczyk, Warsaw University of Technology, Poland

ISSN: 2245-1439 (Print Version),

ISSN: 2245-4578 (Online Version)
Vol: 3   Issue: 3

Published In:   July 2014

Publication Frequency: Quarterly

Articles in 2020

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Forensicloud: An Architecture for Digital Forensic Analysis in the Cloud

Cody Miller, Dae Glendowne, David Dampier, Kendall Blaylock

Distributed Analytics and Security Institute, Mississippi State University, Mississippi State, MS, USA

Abstract: [+]    |    Download File [ 841KB ]    |   Read Article Online

Abstract: The amount of data that must be processed in current digital forensic examinations continues to rise. Both the volume and diversity of data are obstacles to the timely completion of forensic investigations. Additionally, some law enforcement agencies do not have the resources to handle cases of even moderate size. To address these issues we have developed an architecture for a cloud-based distributed processing platform we have named Forensicloud. This architecture is designed to reduce the time taken to process digital evidence by leveraging the power of a high performance computing platform and by adapting existing tools to operate within this environment. Forensicloud’s Software and Infrastructure as a Service service models allow investigators to use remote virtual environments for investigating digital evidence. These environments allow investigators the ability to use licensed and unlicensed tools that they may not have had access to before and allows some of these tools to be run on computing clusters.

Keywords: digital forensics, parallelization, cloud computing, cloud forensics, virtualization, virtual desktop infrastructure, HPC, cluster, infrastructure as a service, software as a service.

Structure Preserving Large Imagery Reconstruction

Ju Shen1, Jianjun Yang2, Sami Taha-abusneineh3, Bryson Payne4 and Markus Hitz4

1Department of Computer Science, University of Dayton, 300 College Park, Dayton, OH 45469, USA
2Department of Computer Science and Information Systems, University of North Georgia, Oakwood, GA 30566, USA
3Computer Science Department, Palestine Polytechnic University (PPU), Ein Sara Street, Hebron, Palestine
4Department of Computer Science and Information Systems, University of North Georgia, Dahlonega, GA 30597, USA

Abstract: [+]    |    Download File [ 5559KB ]    |   Read Article Online

Abstract: With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Furthermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes.

In this paper, we propose a structure-based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing, such as image localization, object retrieval, and scene reconstruction. Our experiments show that this approach achieves favorable results that outperform existing state-of-the-art techniques.

Keywords: Structure Preserving Large Imagery Reconstruction

Evaluation and Analysis of Distributed Graph-Parallel Processing Frameworks

Yue Zhao1, Kenji Yoshigoe1, Mengjun Xie1, Suijian Zhou1, Remzi Seker2 and Jiang Bian3*

1Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
2Department of ECSSE, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
3Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA

Abstract: [+]    |    Download File [ 2893KB ]    |   Read Article Online

Abstract: A number of graph-parallel processing frameworks have been proposed to address the needs of processing complex and large-scale graph structured datasets in recent years. Although significant performance improvement made by those frameworks were reported, comparative advantages of each of these frameworks over the others have not been fully studied, which impedes the best utilization of those frameworks for a specific graph computing task and setting. In this work, we conducted a comparison study on parallel processing systems for large-scale graph computations in a systematic manner, aiming to reveal the characteristics of those systems in performing common graph algorithms with real-world datasets on the same ground. We selected three popular graph-parallel processing frameworks (Giraph, GPS and GraphLab) for the study and also include a representative general data-parallel computing system— Spark—in the comparison in order to understand how well a general data-parallel system can run graph problems. We applied basic performance metrics measuring speed, resource utilization, and scalability to answer a basic question of which graph-parallel processing platform is better suited for what applications and datasets. Three widely-used graph algorithms—clustering coefficient, shortest path length, and PageRank score—were used for benchmarking on the targeted computing systems. We ran those algorithms against three real world network datasets with diverse characteristics and scales on a research cluster and have obtained a number of interesting observations. For instance, all evaluated systems showed poor scalability (i.e., the runtime increases with more computing nodes) with small datasets likely due to communication overhead. Further, out of the evaluated graph parallel computing platforms, Power Graph consistently exhibits better performance than others.

Keywords: Big data, Graph-parallel computing, Distributed processing.

A Cached Registration Scheme for IP Multimedia Subsystem (IMS)

Lava Al-Doski and Seshadri Mohan

1NIKSUN,inc 100 Nassau park Blvd. Princeton, NJ, 08540
2Systems Engineering Department, EIT 519 University of Arkansas at Little Rock 2801 S University Avenue, Little Rock, AR 72204

Abstract: [+]    |    Download File [ 1782KB ]    |   Read Article Online

Abstract: IP Multimedia Subsystem (IMS), an architectural framework for delivering multimedia services, was standardized by 3GPP/3GPP2. It is integrated with 4G and will most likely be with 5G and beyond, so as to enable wireless carriers to provide rich multimedia services, such as IPTV, chat, push to talk, and video conference. To use these services, users need to perform registration procedure with IMS, which will provide user information to the system. Registration is also performed when users move from one network to another. Due to the complexity of IMS and the increasing demands for these services, a key challenge IMS faces is to provide QoS to meet user requirements. One of the key performance factors is delay encountered in registration and service establishment. Also, users’ mobility impacts the rate of registration and consequently the signaling generated that must be handled by the system. This work proposes and analyzes a cached registration scheme to reduce the delay associated with registration. The work also examines the effects of different mobility models on the application layer, in particular IMS. The work studies the impact of user movement patterns on the system and examines the impact of the proposed cached registration scheme on these patterns.

Keywords: IP Multimedia Subsystem, QoS, Mobility Models, IMS cached registration scheme.

Personal Denial of Service Attacks (PDOS) and Online Misbehavior: The Need for Cyber Ethics and Information Security Education on University Campuses

Ashley Podhradsky1, Larry J. LeBlanc2 and Michael R. Bartolacci3

1Dakota State University, Madison, SD, USA;
2Owen Graduate School of Management, Vanderbilt University, Nashville, TN, USA;
3Pennsylvania State University - Berks, Reading, PA, USA;

Abstract: [+]    |    Download File [ 217KB ]    |   Read Article Online

Abstract: The authors examine the need to provide basic information security and cyber ethics training for all university students, not just those pursuing an information security-related degree. The authors also discuss the need to include ethical hacking, as part of an emphasis on cyber ethics, into information security degree programs. Both of these topics are discussed within the context of a new category of cyber crime, a Personal Denial of Service Attack (PDOS) that the authors have identified, along with other types of cyber crime, that are endemic to university campuses.

Keywords: Information security training, Personal Denial of Service Attack, Cyber Ethics.

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