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Recent Advances in Computational Intelligence Paradigms and Machine Learning for Security, Privacy and Forensics in Cloud Computing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 32832

Special Issue Editors

Dr. Brij B Gupta

E-Mail Website
Guest Editor
National institute of Technology Kurukshetra, India
Interests: artificial intelligence; information security; cyber security; intrusion detection; cloud security, mobile security, web security, big data analytics; botnet detection; phishing; ddos attacks; network performance evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Shingo Yamaguchi

E-Mail Website
Guest Editor
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi, Japan
Interests: IoT; cyber security; AI; data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christian Esposito

E-Mail Website
Guest Editor
University of Salerno, Italy
Interests: Distributed Systems; Middleware; Dependability; Ubiquitous Computing and Artificial Intelligence; approaches and techniques to guarantee event deliveries in Internet-scale publish/subscribe services
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michael Sheng

E-Mail Website
Guest Editor
School of Computing, Faculty of Science & Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: web of things; internet of things; big data analytics; web science; service-oriented computing; pervasive computing; sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, cloud computing services are becoming pivotal parts of modern information and communication systems, and in our daily lives. Cloud computing has proven to be an incredible technology for provisioning quickly-deployed and scalable information technology (IT) solutions at reduced infrastructure costs. Unfortunately, the use of the cloud raises serious issues related to security, privacy, latency, inadequate service levels, governance, forensics, data protection, maturity, and reliability. These are the issues that prevent Cloud Computing solutions from becoming the prevalent alternative for mission-critical systems. Moving information assets to the cloud computing platform offers the potentials of reduced costs, on-demand self-service, ubiquitous network access, location independent resource pooling, rapid elasticity and measured service for the cloud user. As such, these cloud computing services open a number of security, privacy and forensics issues and challenges.

The concept of applying a computational intelligence (CI) and machine learning to ensure the security, privacy and forensics of users’ data in the cloud is feasible and sound. Moreover, CI and its associated learning paradigms show promise in a large number of application areas related to cloud security and privacy, cloud management, cloud forensics, optimization analysis, and so on. Consequently, the CI paradigm consists of various branches that are not limited to expert systems, artificial immune system, swarm intelligence, fuzzy system, neural network, evolutionary computing, and various hybrid systems, which are combinations of two or more branches. However, the recent advances in the CI paradigm and its solutions are promising; more investigations are still required to convert theoretical approaches into practical solutions that can be efficiently adopted for security, privacy and forensics of clouds users’ data. This Special Issue intends to bring together state-of-art research and developments on CI approaches for security and privacy of cloud services, novel attacks on cloud services, cloud forensics and novel defenses for cloud service attacks and cloud security analysis. We invite researchers to contribute original research articles, as well as comprehensive review articles, which will seek to understand CI techniques, leading to real-world cloud challenges and future improvements in security, privacy and forensics for cloud data and services.

The topics relevant to this Special Issue include, but are not limited to:

  • Cyber-security issues in clouds
  • Information revelation and privacy in cloud computing
  • Multi-party online gaming on clouds—risk, threat and security solutions
  • Cloud computing security data analysis tools and services
  • Secured handling of extra-scale computational loads on clouds
  • Anonymous authentication for privacy preserving in the cloud
  • Privacy concepts and applications in cloud platforms
  • User behaviour and modelling on cyberspace
  • Cloud forensics
  • Security and privacy of cloud user’s data
  • Evolutionary algorithms for privacy analysis in cloud computing
  • Evolutionary algorithms for mining cloud computing for decision support
  • Optimization of dynamic processes in cloud computing
  • Computational intelligence solutions to security and privacy issues in mobile cloud computing
  • Chaos theory and chaotic systems for cloud content security
  • Soft computing technologies for both quantitative and qualitative security assessment and privacy management in cloud computing
  • Artificial neural network and neural system applied to cloud computing and mitigating the privacy risks of cloud networking
  • Cloud databases built to be highly scalable and robust against hardware failures
  • Cloud storage resilience designed to run over distributed file systems providing data replication and automatic failover capabilities
  • Cyber-attacks and solutions for high fidelity cloud storage

Coherent list of topics:

Papers must be tailored to the emerging fields of CI paradigms regarding security, privacy and forensics of Cloud data and services through deployments models, challenges and novel solutions. The editors maintain the right to reject papers they deem to be out of scope of this Special Issue. Only originally unpublished contributions and invited articles will be considered for the issue. The papers should be formatted according to the journal guidelines.

Dr. B. B. Gupta
Dr. Shingo Yamaguchi
Dr. Christian Esposito
Dr. Michael Sheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

21 pages, 25546 KiB  
Article
Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion
by Tao Wang, Wen Wang, Hui Liu and Tianping Li
Sensors 2019, 19(5), 1245; https://doi.org/10.3390/s19051245 - 12 Mar 2019
Cited by 9 | Viewed by 3838
Abstract
With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video [...] Read more.
With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Paradigms and Machine Learning for Security, Privacy and Forensics in Cloud Computing)
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18 pages, 3683 KiB  
Article
Hausdorff Distance Model-Based Identity Authentication for IP Circuits in Service-Centric Internet-of-Things Environment
by Wei Liang, Weihong Huang, Wuhui Chen, Kuan-Ching Li and Keqin Li
Sensors 2019, 19(3), 487; https://doi.org/10.3390/s19030487 - 24 Jan 2019
Cited by 13 | Viewed by 4278
Abstract
Rapid advances in the Internet-of-Things (IoT) have exposed the underlying hardware devices to security threats. As the major component of hardware devices, the integrated circuit (IC) chip also suffers the threat of illegal, malicious attacks. To protect against attacks and vulnerabilities of a [...] Read more.
Rapid advances in the Internet-of-Things (IoT) have exposed the underlying hardware devices to security threats. As the major component of hardware devices, the integrated circuit (IC) chip also suffers the threat of illegal, malicious attacks. To protect against attacks and vulnerabilities of a chip, a credible authentication is of fundamental importance. In this paper, we propose a Hausdorff distance-based method to authenticate the identity of IC chips in IoT environments, where the structure is analyzed, and the lookup table (LUT) resources are treated as a set of reconfigurable nodes in field programmable gate array (FPGA)-based IC design. Unused LUT resources are selected for insertion of the copyright information by using the depth-first search algorithm, and the random positions are reordered with the Hausdorff distance matching function next, so these positions are mapped to satisfy the specific constraints of the optimal watermark positions. If the authentication process is activated, virtual positions are mapped to the initial key file, yet the identity of the IC designed can be authenticated using the mapping relationship of the Hausdorff distance function. Experimental results show that the proposed method achieves good randomness and secrecy in watermark embedding, as well the extra resource overhead caused by watermarks are promising. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Paradigms and Machine Learning for Security, Privacy and Forensics in Cloud Computing)
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13 pages, 2456 KiB  
Article
A Blockchain-Based Location Privacy Protection Incentive Mechanism in Crowd Sensing Networks
by Bing Jia, Tao Zhou, Wuyungerile Li, Zhenchang Liu and Jiantao Zhang
Sensors 2018, 18(11), 3894; https://doi.org/10.3390/s18113894 - 12 Nov 2018
Cited by 110 | Viewed by 7270
Abstract
Crowd sensing is a perception mode that recruits mobile device users to complete tasks such as data collection and cloud computing. For the cloud computing platform, crowd sensing can not only enable users to collaborate to complete large-scale awareness tasks but also provide [...] Read more.
Crowd sensing is a perception mode that recruits mobile device users to complete tasks such as data collection and cloud computing. For the cloud computing platform, crowd sensing can not only enable users to collaborate to complete large-scale awareness tasks but also provide users for types, social attributes, and other information for the cloud platform. In order to improve the effectiveness of crowd sensing, many incentive mechanisms have been proposed. Common incentives are monetary reward, entertainment & gamification, social relation, and virtual credit. However, there are rare incentives based on privacy protection basically. In this paper, we proposed a mixed incentive mechanism which combined privacy protection and virtual credit called a blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Its network structure can be divided into three parts which are intelligence crowd sensing networks, confusion mechanism, and blockchain. We conducted the experiments in the campus environment and the results shows that the incentive mechanism proposed in this paper has the efficacious effect in stimulating user participation. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Paradigms and Machine Learning for Security, Privacy and Forensics in Cloud Computing)
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23 pages, 3901 KiB  
Article
eTPM: A Trusted Cloud Platform Enclave TPM Scheme Based on Intel SGX Technology
by Haonan Sun, Rongyu He, Yong Zhang, Ruiyun Wang, Wai Hung Ip and Kai Leung Yung
Sensors 2018, 18(11), 3807; https://doi.org/10.3390/s18113807 - 6 Nov 2018
Cited by 19 | Viewed by 6239
Abstract
Today cloud computing is widely used in various industries. While benefiting from the services provided by the cloud, users are also faced with some security issues, such as information leakage and data tampering. Utilizing trusted computing technology to enhance the security mechanism, defined [...] Read more.
Today cloud computing is widely used in various industries. While benefiting from the services provided by the cloud, users are also faced with some security issues, such as information leakage and data tampering. Utilizing trusted computing technology to enhance the security mechanism, defined as trusted cloud, has become a hot research topic in cloud security. Currently, virtual TPM (vTPM) is commonly used in a trusted cloud to protect the integrity of the cloud environment. However, the existing vTPM scheme lacks protections of vTPM itself at a runtime environment. This paper proposed a novel scheme, which designed a new trusted cloud platform security component, ‘enclave TPM (eTPM)’ to protect cloud and employed Intel SGX to enhance the security of eTPM. The eTPM is a software component that emulates TPM functions which build trust and security in cloud and runs in ‘enclave’, an isolation memory zone introduced by SGX. eTPM can ensure its security at runtime, and protect the integrity of Virtual Machines (VM) according to user-specific policies. Finally, a prototype for the eTPM scheme was implemented, and experiment manifested its effectiveness, security, and availability. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Paradigms and Machine Learning for Security, Privacy and Forensics in Cloud Computing)
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29 pages, 2355 KiB  
Article
Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System
by José María Jorquera Valero, Pedro Miguel Sánchez Sánchez, Lorenzo Fernández Maimó, Alberto Huertas Celdrán, Marcos Arjona Fernández, Sergio De Los Santos Vílchez and Gregorio Martínez Pérez
Sensors 2018, 18(11), 3769; https://doi.org/10.3390/s18113769 - 4 Nov 2018
Cited by 19 | Viewed by 6242
Abstract
Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the [...] Read more.
Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users’ quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users’ behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users’ authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Paradigms and Machine Learning for Security, Privacy and Forensics in Cloud Computing)
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17 pages, 561 KiB  
Article
Hidden Policy Attribute-Based Data Sharing with Direct Revocation and Keyword Search in Cloud Computing
by Axin Wu, Dong Zheng, Yinghui Zhang and Menglei Yang
Sensors 2018, 18(7), 2158; https://doi.org/10.3390/s18072158 - 4 Jul 2018
Cited by 38 | Viewed by 3766
Abstract
Attribute-based encryption can be used to realize fine-grained data sharing in open networks. However, in practical applications, we have to address further challenging issues, such as attribute revocation and data search. How do data users search for the data they need in massive [...] Read more.
Attribute-based encryption can be used to realize fine-grained data sharing in open networks. However, in practical applications, we have to address further challenging issues, such as attribute revocation and data search. How do data users search for the data they need in massive amounts of data? When users leave the system, they lose the right to decrypt the shared data. In this case, how do we ensure that revoked users cannot decrypt shared data? In this paper, we successfully address these issues by proposing a hidden policy attribute-based data sharing scheme with direct revocation and keyword search. In the proposed scheme, the direct revocation of attributes does not need to update the private key of non-revoked users during revocation. In addition, a keyword search is realized in our scheme, and the search time is constant with the increase in attributes. In particular, the policy is hidden in our scheme, and hence, users’ privacy is protected. Our security and performance analyses show that the proposed scheme can tackle the security and efficiency concerns in cloud computing. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Paradigms and Machine Learning for Security, Privacy and Forensics in Cloud Computing)
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