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The Advanced Analysis of Digital Traces Recoverability of WhatsApp Program on MacBook

Unquestionably, WhatsApp is one of the most pervasive instant message application programs in contemporary digital era. It could be utilized on mobile smart phones or desktop computing devices including Windows-based personal computers and MacBook with Mac OS X. On account of the tremendous amount of information security incidences occurring concerning the usage of WhatsApp program on a MacBook, this research work constructed several cases to validate the recoverability of digital evidences on the computing device. The recoverability of WhatsApp user ID, username, and the contents of instant message dialogues were discussed based on the physical memory acquisition of the MacBook from digital forensics point of view. In addition, by the inherent characteristics and the executing mechanism of the WhatsApp program, the manufacture and the mode of the smartphone was capable of being disclosed by the digital traces accordingly. All the aforementioned disclosed digital traces could be probative digital evidences in a court of law. Therefore, this research work could be substantively applied and extended to other mushrooming cybercrime investigations regarding instant message incidents in the public sector or the noncompliance of computing resource usages in the private sector. The procedures of physical memory acquisition should be scientifically premeditated and systematically conducted due to the volatility of the physical memory of a computing device with rigorous procedures.

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Design and Implementation of a Novel Location-Aware Wearable Mobile Advertising System

Existing mobile advertising systems face critical challenges in flexibly updating ad contents and responding to distinct consumer groups and regions. In this paper, a novel location-aware wearable mobile advertising system is designed to overcome traditional shortcomings by integrating electronic paper, location technologies and wireless communication techniques. The off-the-shelf electronic paper is modified and implemented as a wearable mobile ad device embedded in a user's bag or clothing. This system was implemented and evaluated using outdoor experiments. Evaluation results showed that our system achieves a noticeable improvement in location perception and enhances the quality of the personalized mobile service.

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A Chaotic Maps Based Key Agreement and User Anonymity Protocol without Using Smart Cards and Symmetric Key En/Decryptions

Password authenticated key agreement protocol allows users to use an easy-to-remember password and establish a secure session key with the help of a trusted server. Recently, Farash and Attari proposed an improved key agreement protocol based on chaotic maps and they pointed out that Gong et al.'s protocol is vulnerable to stolen-verifier attack and password change pitfalls. However, in this paper, we analyze the security of Farash and Attari's protocol and show that it fails to resist known-key attack if the previous session key shared between two parties is compromised. In addition, their protocol is insecure against many logged-in users' attack and the server is not aware of having caused problem. To fill the security gaps, we further design an improved protocol for password authenticated key agreement with user anonymity. To the best of our knowledge, none of the recently proposed password authenticated key agreement protocols can ensure anonymous interactions between the login user and the remote server and this work is the first attempt to provide a secure user anonymity protocol without using smart cards and symmetric key en/decryptions in remote login environments.

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An AP-Based Shilling Attack Detector for Collaborative Filtering Recommendation Systems

Collaborative filtering is a technique widely used in online recommendation systems nowadays. However, it is vulnerable from manipulation by malicious users who often create some fake account (or shilling) profiles to influence the results of recommender systems. To identify the fake users, existing algorithms usually utilize certain characteristics of shilling profiles, of which the drawbacks are the low precision and the requirement of a large size of training set. In this paper, we develop a clustering based method to find the shilling attackers by incorporating the information of user ratings and the attribute of user profiles. The users are firstly self-organizedly clustered into several groups based on the integrated information of the rating features and the attributes of user profile, then the malicious user group is identified through the GRDMA (Group Rating Deviation from Mean Agreement) values of user group. Instead of identifying attacker one by one, the proposed algorithm finds the malicious users at the collective level, which provids a novel way to analyse and detect shilling attack. The experimental results performed on MovieLens dataset demonstrate that the proposed algorithm is effective and robust in three typical kinds of shilling attack models, especially when the attack size and the filler size are sufficiently high.

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A Reliably Dynamic User-Remote Password Authentication Scheme for Medical Environments

In medical domain, different medical environments would be generated with various contexts and regions. In these environments, the protected medical information is likely to be transmitted among systems or users for patient treatments, clinical research, or doctor enquiry. The safe transmission on the network and the guarantee of information privacy and integrity are the critical issues. An authorization mechanism would be used for ensuring the private information not being obtained fraudulently by illegal persons. The password-based authentication scheme is the most widely employed method because of its efficiency. Under such mechanism, each user is allowed to select his password and keep it in mind without any additional assistant devices for further authentication process. Therefore, an ameliorative password-based authentication scheme is proposed in this paper, achieving to resist off-line password guessing attacks, replay attacks, on-line password guessing attacks, ID-theft attacks, stolen-verifier attacks, server spoofing attacks, impersonation attacks, and denial of service attacks. In light of security, the proposed scheme is provided with good practicability for medical environments, even over insecure network.

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Formally Modeling and Analyzing the Adaptive Resource Management for Cloud Applications

Cloud computing has attracted much interest recently from both industry and academic. However, it is a challenge to enforce resource manages for those applications especially in voluntary-resource cloud due to the heterogeneous and highly dynamic environment. To address this emerging, important problem, we propose a method to model and analyze adaptive resource management for cloud applications. First, resource management process is abstracted as meta-object by using the reflection mechanism, which includes the state assessment, online monitoring, format checking and adaptive recovery. Second, Petri nets are used to construct the base layer model, meta layer model, meta-object protocol and other components, thus forming the resource management model. Third, the operational semantics and related theories of Petri nets help establish the correctness of our proposed method. We have also performed a case study and several simulations to evaluate our proposed approach. Results show that it can help reveal the structural and behavioral characteristics of cloud computing, and manage the resources in cloud application more efficiently.

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A New Ant Algorithm for Optimal Service Selection with End-to-End QoS Constraints

In this paper, we study the dynamic service composition becomes a decision problem on which component services should be selected under the E2E-to-End Quality of Service constraints. This problem is modeled is a nonlinear optimization problem under several constraints. We have two approaches to solve this problem. The first approach is local selection which is an efficient strategy with polynomial time complexity but can not handle global constraints. The second approach is the traditional global selection approach based on integer linear programming can handle global constraints, but its poor performance renders it inappropriate for practical applications with dynamic requirements. To overcome this disadvantage, in this study we proposed a novel Min-Max Ant System algorithm with the new utilities heuristic information to search the global approximate optimal. Our goal focus on its efficiency in terms of execution time and the quality in terms of the obtained best solution fitness value. The experiment results show that our algorithm is efficiency and feasibility more than the recently proposed related algorithms.

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A Selection Mechanism for Multiple Error Concealment Techniques in Scalable Video Coding

To cope with the error problem for scalable video coding, this paper proposes a selection method of error concealment algorithms for whole frame loss in the enhancement layer. By analyzing the relationship between inter-layer and temporal domains, the proposed algorithm decides which error concealment algorithm adopted by H.264/SVC reference software is suitable for concealing the current erroneous macroblock in the enhancement layer. Through the proposed algorithm, different erroneous macroblocks can be properly concealed by different error concealment algorithms by considering the characteristics of each algorithm. Simulation results demonstrate that our proposed algorithm outperforms other error concealment algorithms in terms of PSNR and visual quality measurements.

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Performance Evaluation of Desktop Search Engines Using Information Retrieval Systems Approaches

Effective and easy access to stored information is a fundamental computer users' need. Advancements in storage technology and ubiquity of digital gadgets enabled users to generate tons of information. Ultimately, raise the demand for efficient schemes to search and retrieve the stored information. Desktop search engines (DSEs) are developed for helping users in finding information on their desktops quickly and easily. However, the availability of several DSEs troubled users in their selections for making their entire experience of using personal computers less frustrating. This paper introduces a novel systematic approach for performances evaluations of DSEs by leveraging the scientific methods from information retrieval systems. The proposed methodology is based on Cranfield approach consisting of setting several null hypotheses (H_0), developing of static documents and queries collections, and using scientific measures for relevancy measurements. Statistical analysis is performed to confirm significance of the hypotheses and measurements. Copernic and Google are selected as case tools for executing and confirm validity of the methodology. The methodology is generic and can be applied for performances evaluations of any DSE. Results obtained from the test exercises indicated that the methodology can provide detailed, accurate, and useful information for performance evaluation of any DSE.

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An Efficient Cluster Head Election Algorithm for Client Mesh Network Using Fuzzy Logic Control

Enough attention has not been paid to the client nodes in the wireless mesh networks (WMNs) architecture which tend to improve quality of service of the networks if well managed with a cluster structure. In this paper, a fuzzy logic control clustering algorithm (FLCCA) is proposed for client nodes in WMNs. A detailed process for the fuzzification of client node parameters used in the selection of optimal clusterheads is presented. Three client node parameters considered in our proposal are node mobility speed, traffic delivery capacity and the cost of service. We presented an algorithm which uses fuzzy logic control to produce score value for each client nodes based on the parameters for the clusterheads selection. Simulation experiments were used in evaluating the performance of FLCCA and the results show that FLCCA performs better than Distributed Fuzzy Score based Clustering Algorithm (DFSCA).