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[Online] Cybersecurity

Guest Editors-in-Chief 
Fang, Binxing, Guangzhou University, China
Yu, Philip S., University of Illinois at Chicago, USA
Executive Associate Editors
Ren, Kui, Zhejiang University, China
Jia, Yan, National University of Defense Technology, China
Chen, Yan, Northwestern University, USA
Chen, Zhong, Peking University, China
Cui, Yong, Tsinghua University, China
Gaboardi, Marco, SUNY Buffalo, USA
Han, Yi, China Electronics Corporation, China
Hu, Changzhen, Beijing Institute of Technology, China
Jin, Hai, Huazhong University of Science and Technology, China
Kotagiri, Ramamohanarao, The University of Melbourne, Australia
Li, Zhoujun, Beihang University, China
Lin, Xuemin, University of New South Wales, Australia
Mohaisen, Aziz, University of Central Florida, USA
Pei, Jian, Simon Fraser University, Canada
Upadhyaya, Shambhu, SUNY Buffalo, USA
Wang, Xiaoyang, Fudan University, China
Xu, Jin, Peking University, China
Yang, Xiao-Niu, China Electronics Technology Group Corporation, China
Zhong, Sheng, Nanjing University, China
Zhou, Xiaofang, The University of Queensland, Australia
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The New Frontiers of Cybersecurity
Binxing Fang, Kui Ren, Yan Jia
Engineering    2018, 4 (1): 1-2.
Abstract   PDF (425KB)
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Recent Advances in Passive Digital Image Security Forensics: A Brief Review
Xiang Lin, Jian-Hua Li, Shi-Lin Wang, Alan-Wee-Chung Liew, Feng Cheng, Xiao-Sa Huang
Engineering    2018, 4 (1): 29-39.
Abstract   PDF (1424KB)

With the development of sophisticated image editing and manipulation tools, the originality and authenticity of a digital image is usually hard to determine visually. In order to detect digital image forgeries, various kinds of digital image forensics techniques have been proposed in the last decade. Compared with active forensics approaches that require embedding additional information, passive forensics approaches are more popular due to their wider application scenario, and have attracted increasing academic and industrial research interests. Generally speaking, passive digital image forensics detects image forgeries based on the fact that there are certain intrinsic patterns in the original image left during image acquisition or storage, or specific patterns in image forgeries left during the image storage or editing. By analyzing the above patterns, the originality of an image can be authenticated. In this paper, a brief review on passive digital image forensic methods is presented in order to provide a comprehensive introduction on recent advances in this rapidly developing research area. These forensics approaches are divided into three categories based on the various kinds of traces they can be used to track—that is, traces left in image acquisition, traces left in image storage, and traces left in image editing. For each category, the forensics scenario, the underlying rationale, and state-of-the-art methodologies are elaborated. Moreover, the major limitations of the current image forensics approaches are discussed in order to point out some possible research directions or focuses in these areas.

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Toward Privacy-Preserving Personalized Recommendation Services
Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren
Engineering    2018, 4 (1): 21-28.
Abstract   PDF (471KB)

Recommendation systems are crucially important for the delivery of personalized services to users. With personalized recommendation services, users can enjoy a variety of targeted recommendations such as movies, books, ads, restaurants, and more. In addition, personalized recommendation services have become extremely effective revenue drivers for online business. Despite the great benefits, deploying personalized recommendation services typically requires the collection of users’ personal data for processing and analytics, which undesirably makes users susceptible to serious privacy violation issues. Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintain the intelligence of personalized recommendation services while respecting user privacy. In this paper, we provide a comprehensive survey of the literature related to personalized recommendation services with privacy protection. We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services. We classify the existing works according to their underlying techniques for personalized recommendation and privacy protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy and recommendation accuracy. We also identity some future research directions.

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Theories of Social Media: Philosophical Foundations
Jiayin Qi, Emmanuel Monod, Binxing Fang, Shichang Deng
Engineering    2018, 4 (1): 94-102.
Abstract   PDF (642KB)

Although many different views of social media coexist in the field of information systems (IS), such theories are usually not introduced in a consistent framework based on philosophical foundations. This paper introduces the dimensions of lifeworld and consideration of others. The concept of lifeworld includes Descartes’ rationality and Heidegger’s historicity, and consideration of others is based on instrumentalism and Heidegger’s ‘‘being-with.” These philosophical foundations elaborate a framework where different archetypal theories applied to social media may be compared: Goffman’s presentation of self, Bourdieu’s social capital, Sartre’s existential project, and Heidegger’s ‘‘shared-world.” While Goffman has become a frequent reference in social media, the three other references are innovative in IS research. The concepts of these four theories of social media are compared with empirical findings in IS literature. While some of these concepts match the empirical findings, some other concepts have not yet been investigated in the use of social media, suggesting future research directions.

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A DNA Computing Model for the Graph Vertex Coloring Problem Based on a Probe Graph
Jin Xu, Xiaoli Qiang, Kai Zhang, Cheng Zhang, Jing Yang
Engineering    2018, 4 (1): 61-77.
Abstract   PDF (3109KB)

The biggest bottleneck in DNA computing is exponential explosion, in which the DNA molecules used as data in information processing grow exponentially with an increase of problem size. To overcome this bottleneck and improve the processing speed, we propose a DNA computing model to solve the graph vertex coloring problem. The main points of the model are as follows:①The exponential explosion problem is solved by dividing subgraphs, reducing the vertex colors without losing the solutions, and ordering the vertices in subgraphs; and②the bio-operation times are reduced considerably by a designed parallel polymerase chain reaction (PCR) technology that dramatically improves the processing speed. In this article, a 3-colorable graph with 61 vertices is used to illustrate the capability of the DNA computing model. The experiment showed that not only are all the solutions of the graph found, but also more than 99% of false solutions are deleted when the initial solution space is constructed. The powerful computational capability of the model was based on specific reactions among the large number of nanoscale oligonucleotide strands. All these tiny strands are operated by DNA self-assembly and parallel PCR. After thousands of accurate PCR operations, the solutions were found by recognizing, splicing, and assembling. We also prove that the searching capability of this model is up to O(359). By means of an exhaustive search, it would take more than 896 000 years for an electronic computer (5 1014 s1) to achieve this enormous task. This searching capability is the largest among both the electronic and non-electronic computers that have been developed since the DNA computing model was proposed by Adleman’s research group in 2002 (with a searching capability of O(220)).

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Calculation of the Behavior Utility of a Network System: Conception and Principle
Changzhen Hu
Engineering    2018, 4 (1): 78-84.
Abstract   PDF (847KB)

The service and application of a network is a behavioral process that is oriented toward its operations and tasks, whose metrics and evaluation are still somewhat of a rough comparison. This paper describes scenes of network behavior as differential manifolds. Using the homeomorphic transformation of smooth differential manifolds, we provide a mathematical definition of network behavior and propose a mathematical description of the network behavior path and behavior utility. Based on the principle of differential geometry, this paper puts forward the function of network behavior and a calculation method to determine behavior utility, and establishes the calculation principle of network behavior utility. We also provide a calculation framework for assessment of the network’s attack-defense confrontation on the strength of behavior utility. Therefore, this paper establishes a mathematical foundation for the objective measurement and precise evaluation of network behavior.

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Social Influence Analysis: Models, Methods, and Evaluation
Kan Li, Lin Zhang, Heyan Huang
Engineering    2018, 4 (1): 40-46.
Abstract   PDF (415KB)

Social influence analysis (SIA) is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and evaluation aspects related to SIA. We divide SIA models into two types: microscopic and macroscopic models. Microscopic models consider human interactions and the structure of the influence process, whereas macroscopic models consider the same transmission probability and identical influential power for all users. We analyze social influence methods including influence maximization, influence minimization, flow of influence, and individual influence. In social influence evaluation, influence evaluation metrics are introduced and social influence evaluation models are then analyzed. The objectives of this paper are to provide a comprehensive analysis, aid in understanding social behaviors, provide a theoretical basis for influencing public opinion, and unveil future research directions and potential applications.

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SRIM Scheme: An Impression-Management Scheme for Privacy-Aware Photo-Sharing Users
Fenghua Li, Zhe Sun, Ben Niu, Yunchuan Guo, Ziwen Liu
Engineering    2018, 4 (1): 85-93.
Abstract   PDF (1445KB)

With the development of online social networks (OSNs) and modern smartphones, sharing photos with friends has become one of the most popular social activities. Since people usually prefer to give others a positive impression, impression management during photo sharing is becoming increasingly important. However, most of the existing privacy-aware solutions have two main drawbacks: ① Users must decide manually whether to share each photo with others or not, in order to build the desired impression; and ② users run a high risk of leaking sensitive relational information in group photos during photo sharing, such as their position as part of a couple, or their sexual identity. In this paper, we propose a social relation impression-management (SRIM) scheme to protect relational privacy and to automatically recommend an appropriate photo-sharing policy to users. To be more specific, we have designed a lightweight face-distance measurement that calculates the distances between users’ faces within group photos by relying on photo metadata and face-detection results. These distances are then transformed into relations using proxemics. Furthermore, we propose a relation impression evaluation algorithm to evaluate and manage relational impressions. We developed a prototype and employed 21 volunteers to verify the functionalities of the SRIM scheme. The evaluation results show the effectiveness and efficiency of our proposed scheme.

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A Practical Approach to Constructing a Knowledge Graph for Cybersecurity
Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang, Aiping Li
Engineering    2018, 4 (1): 53-60.
Abstract   PDF (925KB)

Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work.

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Research on the Construction of a Novel Cyberspace Security Ecosystem
Xiao-Niu Yang, Wei Wang, Xiao-Feng Xu, Guo-Rong Pang, Chun-Lei Zhang
Engineering    2018, 4 (1): 47-52.
Abstract   PDF (822KB)

Given the challenges facing the cyberspace of the nation, this paper presents the tripartite theory of cyberspace, based on the status quo of cyberspace. Corresponding strategies and a research architecture are proposed for common public networks (C space), secure classified networks (S space), and key infrastructure networks (K space), based on their individual characteristics. The features and security requirements of these networks are then discussed. Taking C space as an example, we introduce the SMCRC (which stands for “situation awareness, monitoring and management, cooperative defense, response and recovery, and countermeasures and traceback”) loop for constructing a cyberspace security ecosystem. Following a discussion on its characteristics and information exchange, our analysis focuses on the critical technologies of the SMCRC loop. To obtain more insight into national cyberspace security, special attention should be paid to global sensing and precise mapping, continuous detection and active management, cross-domain cooperation and systematic defense, autonomous response and rapid processing, and accurate traceback and countermeasure deterrence.

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