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Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 October 2020) | Viewed by 39732

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Prof. Dr. Jong Hyuk Park

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Department of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
Interests: IoT; human-centric ubiquitous computing; information security; digital forensics; vehicular cloud computing; multimedia computing
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Special Issue Information

Dear Colleagues,

In recent years, human-centered computing (HC) has become most promising research domain in both industrial and academic areas worldwide. As a convergence of multiple disciplines, HC supports the effective bridging of various human-related computational elements, the physical world, and cyberspace. HC enables the design and development of an effective computer system that considers cultural, social, and personal aspects and mitigates issues such as human–computer interaction, human information interaction, information design, human–human interaction, and the relationship among art, social, cultural issues, and computing technologies. Using intelligent HC techniques, an organization or enterprise can design and develop several human–computer applications conveniently and economically to fulfill the critical functional or nonfunctional computational requisites from a set of users. However, it is critical for manufacturers of human–computer applications to implement common functions for security, data management, and communication. For instance, HC platforms (e.g., human-centered activities in multimedia, human-centered activities in IoT, human-centered activities in Blockchain) are implemented with individual security architectures, policies, goals, and have their own vulnerabilities and attack surfaces. Moreover, vulnerable computing resources in HC can be infected with malware and subsequently turned into a large botnet that further results in devastating DDoS attacks. Though information security is a serious and demanding factor of HC deployment, it is too often ignored in the design and development of HC oriented systems. Consequently, the provision of information security has been gathering much attention in all HC-related areas.

The aim of this Special Issue is to identify the emerging information security challenges in all HC-related areas. It will consist of up-to-date, state-of-the-art research contributions with novel design and developments of intelligent application, perception, and security methods in the HCC, to enhance the reliability and feasibility of HC in real-world applications.

Topics of interest may include but are not limited to the following:

Big-data analysis and datamining for HC;
Internet of Things and Blockchain for HC;
AI and soft computing for HC;
Social computing and social intelligence for HC;
HC-based cloud, fog, and edge computing;
HC-based smart home and smart city;
Human–computer interaction and user-centered design;
Design and development of a secure human–computer applications;
Design and development of deep learning framework for HC services;
Study of human aspects of information security in HC;
Blockchain in HC;
Security and privacy for secure HC;
Trust management in an HC environment;
Efficient and secure HC applications for IoT;
Security and credibility verification in HC;
Intelligent and secure data processing in HC.

Prof. Dr. Jong Hyuk Park
Guest Editor

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Keywords

Human-centered computing
Deep learning
Artificial intelligence
Internet of Things
Smart city
Information security
Blockchain
Privacy

Published Papers (11 papers)

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Research

16 pages, 562 KiB

Open AccessArticle

On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services

by Jie Gao, Fei Hao and Doo-Soon Park

Appl. Sci. 2020, 10(23), 8618; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238618 - 02 Dec 2020

Cited by 3 | Viewed by 1326

Abstract

The concept stability measure under the Formal Concept Analysis (FCA) theory is useful for improving the accuracy of structure identification of social networks. Nevertheless, the stability calculation is an NP-complete task which is the primary challenges in practical. Most existing studies have focused on the approximate estimate to calculate the stability. Therefore, we focus on introducing the Maximal Non-Generator-based Stability Calculation (MNG-SC) algorithm that directly deals with accurate stability calculation to pave the way for FCA’s application in structures identification of social networks. Specifically, a novel perspective of stability calculation by linking it to Maximal Non-Generator (MNG) is first provided. Then, the equivalence between maximal non-generator and lower neighbor concept is first proved, which greatly improves scalability and reduces computational complexity. The performed experiments show that the MNG-SC outperforms the pioneering approaches of the literature. Furthermore, a case study of identifying abnormal users in social networks is presented, which demonstrates the effectiveness and potential application of our algorithm. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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21 pages, 1451 KiB

Open AccessArticle

Method for Attack Tree Data Transformation and Import Into IT Risk Analysis Expert Systems

by Donatas Vitkus, Jonathan Salter, Nikolaj Goranin and Dainius Čeponis

Appl. Sci. 2020, 10(23), 8423; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238423 - 26 Nov 2020

Cited by 6 | Viewed by 3951

Abstract

Information technology (IT) security risk analysis preventatively helps organizations in identifying their vulnerable systems or internal controls. Some researchers propose expert systems (ES) as the solution for risk analysis automation since risk analysis by human experts is expensive and timely. By design, ES need a knowledge base, which must be up to date and of high quality. Manual creation of databases is also expensive and cannot ensure stable information renewal. These facts make the knowledge base automation process very important. This paper proposes a novel method of converting attack trees to a format usable by expert systems for utilizing the existing attack tree repositories in facilitating information and IT security risk analysis. The method performs attack tree translation into the Java Expert System Shell (JESS) format, by consistently applying ATTop, a software bridging tool that enables automated analysis of attack trees using a model-driven engineering approach, translating attack trees into the eXtensible Markup Language (XML) format, and using the newly developed ATES (attack trees to expert system) program, performing further XML conversion into JESS compatible format. The detailed method description, along with samples of attack tree conversion and results of conversion experiments on a significant number of attack trees, are presented and discussed. The results demonstrate the high method reliability rate and viability of attack trees as a source for the knowledge bases of expert systems used in the IT security risk analysis process. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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17 pages, 3316 KiB

Open AccessArticle

Cooperation Based Proactive Caching in Multi-Tier Cellular Networks

by Fawad Ahmad, Ayaz Ahmad, Irshad Hussain, Peerapong Uthansakul and Suleman Khan

Appl. Sci. 2020, 10(18), 6145; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186145 - 04 Sep 2020

Cited by 11 | Viewed by 1718

Abstract

The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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27 pages, 11761 KiB

Open AccessArticle

A Kohonen SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks

by Vita Santa Barletta, Danilo Caivano, Antonella Nannavecchia and Michele Scalera

Appl. Sci. 2020, 10(15), 5062; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155062 - 23 Jul 2020

Cited by 29 | Viewed by 3136

Abstract

The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X–Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF–K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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32 pages, 2821 KiB

Open AccessArticle

Forensic Exchange Analysis of Contact Artifacts on Data Hiding Timestamps

by Da-Yu Kao

Appl. Sci. 2020, 10(13), 4686; https://0-doi-org.brum.beds.ac.uk/10.3390/app10134686 - 07 Jul 2020

Cited by 1 | Viewed by 3806

Abstract

When computer systems are increasingly important for our daily activities, cybercrime has created challenges for the criminal justice system. Data can be hidden in ADS (Alternate Data Stream) without hindering performance. This feature has been exploited by malware authors, criminals, terrorists, and intelligence agents to erase, tamper, or conceal secrets. However, ADS problems are much ignored in digital forensics. Rare researches illustrated the contact artifacts of ADS timestamps. This paper performs a sequence of experiments from an inherited variety and provides an in-depth overview of timestamp transfer on data hiding operations. It utilizes files or folders as original media and uses the timestamp rules as an investigative approach for the forensic exchange analysis of file sets. This paper also explores timestamp rules using case examples, which allow practical applications of crime scene reconstruction to real-world contexts. The experiment results demonstrate the effectiveness of temporal attributes, help digital forensic practitioners to uncover hidden relations, and trace the contact artifacts among crime scenes, victims, and suspects/criminals. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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17 pages, 2685 KiB

Open AccessArticle

Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs

by Gege Luo, Mingxian Shi, Caidan Zhao and Zhiyuan Shi

Appl. Sci. 2020, 10(12), 4206; https://0-doi-org.brum.beds.ac.uk/10.3390/app10124206 - 19 Jun 2020

Cited by 9 | Viewed by 2762

Abstract

With the increasing demand for intelligent traffic management and road network intelligent information services, the vehicular ad hoc networks (VANETs) combined with information of air, space and ground have outstanding advantages in coverage, reliable transmission, and resource richness. Due to the characteristics of heterogeneous, numerous nodes, and frequent cross-network flow, the space–air–ground integrated network (SAGIN) puts forward higher requirements for security. This paper proposes a cross-regional node identity management architecture based on the hash chain, combined with radio frequency (RF) fingerprint theory, to guarantee node identity security with a non-duplicated physical information identity authentication mechanism. At the same time, the blockchain consensus mechanism is simplified to achieve block recording and verification. OMNet ++, SUMO, and Veins co-simulation platforms are used to generate transactions for cross-regional traffic flow. Based on the Hyperledger–Fabric architecture, Kafka and PBFT consensus algorithms are simulated. The simulation results show that the average delay of a single transaction generated block is about 0.9 ms, which achieves efficient and low-latency authentication. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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16 pages, 7542 KiB

Open AccessArticle

A Real-Time Chain and Variable Bulk Arrival and Variable Bulk Service (VBAVBS) Model with λ_F

by Nohpill Park, Abhilash Kancharla and Hye-Young Kim

Appl. Sci. 2020, 10(10), 3651; https://0-doi-org.brum.beds.ac.uk/10.3390/app10103651 - 25 May 2020

Cited by 2 | Viewed by 2566

Abstract

This paper proposes a real-time chain and a novel embedded Markovian queueing model with variable bulk arrival (VBA) and variable bulk service (VBS) in order to establish and assure a theoretical foundation to design a blockchain-based real-time system with particular interest in Ethereum. Based on the proposed model, various performances are simulated in a numerical manner in order to validate the efficacy of the model by checking good agreements with the results against intuitive and typical expectations as a baseline. A demo of the proposed real-time chain is developed in this work by modifying the open source of Ethereum Geth 1.9.11. The work in this paper will provide both a theoretical foundation to design and optimize the performances of the proposed real-time chain, and ultimately address and resolve the performance bottleneck due to the conventional block-synchrony by employing an asynchrony by the real-time deadline to some extent. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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15 pages, 1080 KiB

Open AccessArticle

Analysis of Vulnerabilities That Can Occur When Generating One-Time Password

by Hyunki Kim, Juhong Han, Chanil Park and Okyeon Yi

Appl. Sci. 2020, 10(8), 2961; https://0-doi-org.brum.beds.ac.uk/10.3390/app10082961 - 24 Apr 2020

Cited by 9 | Viewed by 3878

Abstract

A one-time password (OTP) is a password that is valid for only one login session or transaction, in IT systems or digital devices. This is one of the human-centered security services and is commonly used for multi-factor authentication. This is very similar to generating pseudo-random bit streams in cryptography. However, it is only part of what is used as OTP in the bit stream. Therefore, the OTP mechanism requires an algorithm to extract portions. It is also necessary to convert hexadecimal to decimal so that the values of the bit strings are familiar to human. In this paper, we classify three algorithms for extracting the final data from the pseudo random bit sequence. We also analyze the fact that a vulnerability occurs during the extraction process, resulting in a high frequency of certain numbers; even if cryptographically secure generation algorithms are used. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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20 pages, 5982 KiB

Open AccessArticle

Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy

by Dewang Wang, Xianquan Zhang, Chunqiang Yu and Zhenjun Tang

Appl. Sci. 2020, 10(6), 2058; https://0-doi-org.brum.beds.ac.uk/10.3390/app10062058 - 18 Mar 2020

Cited by 5 | Viewed by 2096

Abstract

In this paper, a reversible data hiding method in encrypted image (RDHEI) is proposed. Prior to image encryption, the embeddable pixels are selected from an original image according to prediction errors due to adjacent pixels with strong correlation. Then the embeddable pixels and the other pixels are both rearranged and encrypted to generate an encrypted image. Secret bits are directly embedded into the multiple MSBs (most significant bit) of the embeddable pixels in the encrypted image to generate a marked encrypted image during the encoding phase. In the decoding phase, secret bits can be extracted from the multiple MSBs of the embeddable pixels in the marked encrypted image. Moreover, the original embeddable pixels are restored losslessly by using correlation of the adjacent pixels. Thus, a reconstructed image with high visual quality can be obtained only when the encryption key is available. Since exploiting multiple MSBs of the embeddable pixels, the proposed method can obtain a very large embedding capacity. Experimental results show that the proposed method is able to achieve an average embedding rate as large as 1.7215 bpp (bits per pixel) for the BOW-2 database. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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24 pages, 6408 KiB

Open AccessArticle

An Identity Model for Providing Inclusive Services and Applications

by Lourdes Marco, Álvaro Alonso and Juan Quemada

Appl. Sci. 2019, 9(18), 3813; https://0-doi-org.brum.beds.ac.uk/10.3390/app9183813 - 11 Sep 2019

Cited by 6 | Viewed by 3863

Abstract

Information and Communication Technologies (ICT) need to be accessible for every single person in the globe. Governments and companies are starting to regulate products and services to ensure digital accessibility as a mandatory requirement. A recent example is the European standard EN 301 549, where the functional accessibility requirements for ICT products and services are defined. Especially on the Web, these standards must be integrated throughout the development processes, where the selected architecture models play an essential role. Starting from a model that is based on the OAuth 2.0 protocol, and that allows the complete delegation of authorization (so that an as a service access control mechanism is provided), this paper propose an identity model for providing inclusive services and applications. The model takes advantage of the users’ profiles and their functional attributes to determine how to serve web interfaces to them in a specific service. Those attributes are entirely flexible, and can be defined linked to users’ functional capabilities, or even a particular skill. We have implemented the proposed model as an extension of an existing open source Identity Manager and tested it with a real use case deployment. We conclude that the proposed solution enables a new identity paradigm that allows service providers to design their interfaces satisfying the diversity requirements in terms of design and development. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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12 pages, 3705 KiB

Open AccessArticle

Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles

by Aiguo Zhou, Zhenyu Li and Yong Shen

Appl. Sci. 2019, 9(15), 3174; https://0-doi-org.brum.beds.ac.uk/10.3390/app9153174 - 04 Aug 2019

Cited by 39 | Viewed by 9408

Abstract

The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus. Full article

(This article belongs to the Special Issue Human-Centered Computing and Information Security: Recent Advances & Intelligent Applications)

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