Special Issue "Analytics, Privacy and Security for IoT and Big Data"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2022 | Viewed by 4666

Special Issue Editors

Prof. Dr. Tai-hoon Kim
E-Mail Website
Guest Editor
GVSA and University of Tasmania, Hobart TAS 7001, Australia
Interests: control and automation; information technology security; information security; mobile computing; multimedia communication; ubiquitous computing
Special Issues, Collections and Topics in MDPI journals
Dr. Robson de Oliveira Albuquerque
E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Brasília, Brasília 70910-900, Brazil
Interests: distributed systems; information security; network management; network security; network systems; open source software; wireless networks
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Lucila Sandoval Orozco
E-Mail Website
Guest Editor
Group of Analysis, Security and Systems (GASS), Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
Interests: computer and network security; multimedia forensics; error-correcting codes; information theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a network of smart devices with pervasive and autonomous communication through internet connectivity. It has emerged as a powerful and promising technology with significant technical, social, and economic impacts. However, just as IoT brings many opportunities, it also brings new threats that require holistic security thinking that spans new business models, technology and standards, and regulation. With the increase in deployment of IoT devices, the amount of data generated by these devices also increases, hence resulting in a need for large-scale data processing systems to process and extract information for efficient and impactful decision making that will improve quality of living. However, most of these smart devices fail to address even simple challenges for security and privacy: They do not protect the data they gather and transmit, they do not use encrypted connections, they use simplistic credentials, etc. Therefore, IoT raises significant challenges regarding security and privacy. Having large numbers of connected devices deployed everywhere around us, e.g., in homes, offices, buses, on the street, monitoring the everyday activities of citizens raises issues regarding user and data privacy.

On the other hand, even though it is desirable to use the large amount of data generated from IoT devices in designing intelligent systems for making informed decisions in applications such as image recognition, activity detection, etc., concerns regarding data security and privacy arise when sensitive information is involved. Mainly, the distribution of sensitive data in a multiparty computation environment may carry a high risk of secrecy leakage or data misuse when its usage is not satisfactorily vetted. Moreover, the sensitivity of intelligent systems to adversarial attacks may prevent large-scale deployment of these systems, thereby losing the advantages of a benign system.

The main objective of this Special Issue is to collect contributions by leading-edge researchers from academia and industry and show the latest research results in the rapidly developed field of analytics, privacy, and security for IoT and Big Data, therefore providing a valuable information venue to researchers as well as practitioners. Original and unpublished high-quality research results are solicited to explore various challenging topics which include but are not limited to:

Potential topics include but are not limited to:

  • Authentication and access control for IOT;
  • Big data analytics for threat and attack prediction;
  • Big data forensics;
  • Big data outsourcing;
  • Big data security for IoT;
  • Biometric technologies and systems for IoT;
  • Blockchain in the IoT;
  • Cross-domain trust management in smart networks;
  • Data security and cryptosystems for IoT;
  • Decentralized and distributed security architectures;
  • Detection and prevention of IoT-based security attacks;
  • Embedded security and privacy in IoT devices;
  • Ethics and legal considerations in IoT;
  • Forensics for IoT;
  • GDPR in the IoT;
  • Intrusion detection and prevention for mobile and IOT platforms;
  • IoT security mechanisms targeting application layer protocols;
  • IoT-based malware mitigation;
  • Lightweight security solutions;
  • MAC layer security protocols for IoT applications;
  • Malware detection, analysis, and mitigation for IOT networks
  • Mobile system security;
  • On-device authentication, authorization, and access control in IoT;
  • Physical layer security in the IoT;
  • Privacy and anonymization techniques in IoT;
  • Privacy and security for smart homes and smart cities;
  • Resource-savvy intrusion detection for Networks of Things;
  • Secure and privacy preserving data mining and aggregation in IoT applications;
  • Secure authentication of IoT devices;
  • Secure spectrum management solutions for wireless IoT communications;
  • Secure wireless channel and traffic models;
  • Security and privacy by design architectures for IoT;
  • Security and privacy in big databases;
  • Security and privacy in heterogeneous IoT;
  • Security and privacy in IoT applications;
  • Security of sensors and actuators;
  • Trust management IoT architectures.

Prof. Dr. Luis Javier García Villalba
Prof. Dr. Tai-hoon Kim
Dr. Robson de Oliveira Albuquerque
Dr. Ana Lucila Sandoval Orozco
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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 2300 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.

Keywords

  • Artificial Intelligence
  • Big data
  • Big data applications
  • Cloud computing
  • Computer security
  • Cybersecurity
  • Data analysis
  • Data privacy
  • Internet of Things
  • Interoperability
  • Standardization

Published Papers (6 papers)

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Research

Article
Weaknesses in ENT Battery Design
Appl. Sci. 2022, 12(9), 4230; https://doi.org/10.3390/app12094230 - 22 Apr 2022
Viewed by 258
Abstract
Randomness testing is a key tool to analyse the quality of true (physical) random and pseudo-random number generators. There is a wide variety of tests that are designed for this purpose, i.e., to analyse the goodness of the sequences used. These tests are [...] Read more.
Randomness testing is a key tool to analyse the quality of true (physical) random and pseudo-random number generators. There is a wide variety of tests that are designed for this purpose, i.e., to analyse the goodness of the sequences used. These tests are grouped in different sets called suites or batteries. The batteries must be designed in such a way that the tests that form them are independent, that they have a wide coverage, and that they are computationally efficient. One such battery is the well-known ENT battery, which provides four measures and the value of a statistic (corresponding to the chi-square goodness-of-fit test). In this paper, we will show that this battery presents some vulnerabilities and, therefore, must be redefined to solve the detected problems. Full article
(This article belongs to the Special Issue Analytics, Privacy and Security for IoT and Big Data)
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Article
Development and Evaluation of an Intelligence and Learning System in Jurisprudence Text Mining in the Field of Competition Defense
Appl. Sci. 2021, 11(23), 11365; https://doi.org/10.3390/app112311365 - 01 Dec 2021
Viewed by 637
Abstract
A jurisprudence search system is a solution that makes available to its users a set of decisions made by public bodies on the recurring understanding as a way of understanding the law. In the similarity of legal decisions, jurisprudence seeks subsidies that provide [...] Read more.
A jurisprudence search system is a solution that makes available to its users a set of decisions made by public bodies on the recurring understanding as a way of understanding the law. In the similarity of legal decisions, jurisprudence seeks subsidies that provide stability, uniformity, and some predictability in the analysis of a case decided. This paper presents a proposed solution architecture for the jurisprudence search system of the Brazilian Administrative Council for Economic Defense (CADE), with a view to building and expanding the knowledge generated regarding the economic defense of competition to support the agency’s final procedural business activities. We conducted a literature review and a survey to investigate the characteristics and functionalities of the jurisprudence search systems used by Brazilian public administration agencies. Our findings revealed that the prevailing technologies of Brazilian agencies in developing jurisdictional search systems are Java programming language and Apache Solr as the main indexing engine. Around 87% of the jurisprudence search systems use machine learning classification. On the other hand, the systems do not use too many artificial intelligence and morphological construction techniques. No agency participating in the survey claimed to use ontology to treat structured and unstructured data from different sources and formats. Full article
(This article belongs to the Special Issue Analytics, Privacy and Security for IoT and Big Data)
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Article
Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
Appl. Sci. 2021, 11(21), 10244; https://doi.org/10.3390/app112110244 - 01 Nov 2021
Viewed by 564
Abstract
Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this [...] Read more.
Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, “Which features, classifiers, and evaluation metrics are better for malware familial classification”? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered. Full article
(This article belongs to the Special Issue Analytics, Privacy and Security for IoT and Big Data)
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Article
A Secure Random Number Generator with Immunity and Propagation Characteristics for Cryptography Functions
Appl. Sci. 2021, 11(17), 8073; https://doi.org/10.3390/app11178073 - 31 Aug 2021
Viewed by 473
Abstract
Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in [...] Read more.
Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in present cryptographic algorithms and therefore, Symmetric Random Function Generator (SRFG) has been developed. In this paper, we have analysed SRFG based on propagation feature and immunity. Moreover, NIST recommended statistical suite has been tested on SRFG outputs. The test values show that SRFG possess some of the useful randomness properties for cryptographic applications such as individual frequency in a sequence and block-based frequency, long run of sequences, oscillations from 0 to 1 or vice-versa, patterns of bits, gap bits between two patterns, and overlapping block bits. We also analyze the comparison of SRFG and some existing random number generators. We observe that SRFG is efficient for cryptographic operations in terms of propagation and immunity features. Full article
(This article belongs to the Special Issue Analytics, Privacy and Security for IoT and Big Data)
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Article
SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
Appl. Sci. 2021, 11(17), 8057; https://doi.org/10.3390/app11178057 - 30 Aug 2021
Viewed by 644
Abstract
Adoption of distributed stream processing (DSP) systems such as Apache Flink in real-time big data processing is increasing. However, DSP programs are prone to be buggy, especially when one programmer neglects some DSP features (e.g., source data reordering), which motivates development of approaches [...] Read more.
Adoption of distributed stream processing (DSP) systems such as Apache Flink in real-time big data processing is increasing. However, DSP programs are prone to be buggy, especially when one programmer neglects some DSP features (e.g., source data reordering), which motivates development of approaches for testing and verification. In this paper, we focus on the test data generation problem for DSP programs. Currently, there is a lack of an approach that generates test data for DSP programs with both high path coverage and covering different stream reordering situations. We present a novel solution, SPOT (i.e., Stream Processing Program Test), to achieve these two goals simultaneously. At first, SPOT generates a set of individual test data representing each path of one DSP program through symbolic execution. Then, SPOT composes these independent data into various time series data (a.k.a, stream) in diverse reordering. Finally, we can perform a test by feeding the DSP program with these streams continuously. To automatically support symbolic analysis, we also developed JPF-Flink, a JPF (i.e., Java Pathfinder) extension to coordinate the execution of Flink programs. We present four case studies to illustrate that: (1) SPOT can support symbolic analysis for the commonly used DSP operators; (2) test data generated by SPOT can more efficiently achieve high JDU (i.e., Joint Dataflow and UDF) path coverage than two recent DSP testing approaches; (3) test data generated by SPOT can more easily trigger software failure when comparing with those two DSP testing approaches; and (4) the data randomly generated by those two test techniques are highly skewed in terms of stream reordering, which is measured by the entropy metric. In comparison, it is even for test data from SPOT. Full article
(This article belongs to the Special Issue Analytics, Privacy and Security for IoT and Big Data)
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Article
Blockchain Based Trust Model Using Tendermint in Vehicular Adhoc Networks
Appl. Sci. 2021, 11(5), 1998; https://doi.org/10.3390/app11051998 - 24 Feb 2021
Cited by 3 | Viewed by 942
Abstract
Blockchain is the consensus-based technology used to resolve conflicts in Byzantine environments. Vehicles validate the messages received from neighboring vehicles using the gradient boosting technique (GBT). Based on the validation results, the message source vehicle generates the ratings that are to be uploaded [...] Read more.
Blockchain is the consensus-based technology used to resolve conflicts in Byzantine environments. Vehicles validate the messages received from neighboring vehicles using the gradient boosting technique (GBT). Based on the validation results, the message source vehicle generates the ratings that are to be uploaded to roadside units (RSUs), and through that, the trust offset value can be calculated. All RSUs maintain the trust blockchain, and each RSU tries to add their blocks to the trust blockchain. We proposed a blockchain-based trust management model for the vehicular adhoc network (VANET) based on Tendermint. It eliminates the problem of malicious nodes entering the network, and will also overcome the problem of power consumption. Simulation results also show that the proposed system is 7.8% and 15.6% effective and efficient in terms of packet delivery ratio (PDR) and end-to-end delay (EED), respectively, to collect the trusted data between the vehicles. Full article
(This article belongs to the Special Issue Analytics, Privacy and Security for IoT and Big Data)
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