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Systems
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Intelligent Systems and Cybersecurity
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Intelligent Systems and Cybersecurity

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 9984

Special Issue Editors

Dr. Nuno Lopes

E-Mail Website
Guest Editor
2AI—School of Technology, Instituto Politécnico do Cávado e do Ave, 4750-810 Barcelos, Portugal
Interests: parallel and distributed systems; cyberphysical production systems; production scheduling algorithms; artificial intelligence in cybersecurity
Dr. Joaquim Gonçalves

E-Mail Website
Guest Editor
2AI—School of Technology, Instituto Politécnico do Cávado e do Ave, 4750-810 Barcelos, Portugal
Interests: intelligent systems; health systems; machine learning; data science

Special Issue Information

Dear Colleagues,

Intelligent systems are improving our ordinary life through the means of cyberphysical systems, a classical system where physical resources influence computer resources and vice versa, or pure cyber systems, such as cloud computing systems, which consist in computers that process data in remote data centers reachable by mobile devices on the Internet. Both types of systems, cyberphysical and cyber, can make use of artificial intelligence to improve the system outcome. Achieving physical feats such as self-driving cars or cyber-based social impacts such as real-time text translation of pictures in multiple languages are outcomes that would otherwise not be possible if using traditional algorithms.

Although new intelligent algorithms are being developed, there are still some challenges that must be tackled to have a successful working system. Cybersecurity relates to the properties (consider, for example confidentiality, integrity and availability, among others) of the system to maintain its correct working behavior, despite the presence of unexpected events that would otherwise trigger the system into executing unwanted operations. Within this, availability relates to the capability of the system to maintaining the same level of service, without losing the ability to provide proper feedback in useful time, in the face of unexpected events.

This Special Issue welcomes contributions on the challenges to provide an intelligent, secure, and reliable systems, including the following list of topics:

  • Cybersecurity challenges in cyberphysical systems;
  • Artificial intelligence algorithms for improving CPS cybersecurity;
  • Industrial CPS applications for cybersecurity;
  • Challenges of AI and security in cyberphysical production systems;
  • Availability of critical cyberphysical systems;
  • AI for new applications in health systems;
  • Intelligent Systems for medical diagnostics;
  • Privacy and security on AI algorithms;
  • Intelligent systems for data privacy.

Dr. Nuno Lopes
Dr. Joaquim Gonçalves
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. Systems is an international peer-reviewed open access monthly 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 2400 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.

Published Papers (4 papers)

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Research

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22 pages, 923 KiB  
Article
Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform
by Rafael Oliveira, Tiago Pedrosa, José Rufino and Rui Pedro Lopes
Systems 2024, 12(4), 126; https://doi.org/10.3390/systems12040126 - 07 Apr 2024
Viewed by 527
Abstract
The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of [...] Read more.
The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios. Full article
(This article belongs to the Special Issue Intelligent Systems and Cybersecurity)
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21 pages, 6906 KiB  
Article
A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain
by Osama Younis, Kamal Jambi, Fathy Eassa and Lamiaa Elrefaei
Systems 2024, 12(3), 84; https://doi.org/10.3390/systems12030084 - 06 Mar 2024
Viewed by 976
Abstract
Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor [...] Read more.
Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases. Full article
(This article belongs to the Special Issue Intelligent Systems and Cybersecurity)
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18 pages, 1207 KiB  
Article
Enhancing Intrusion Detection Systems Using a Deep Learning and Data Augmentation Approach
by Rasheed Mohammad, Faisal Saeed, Abdulwahab Ali Almazroi, Faisal S. Alsubaei and Abdulaleem Ali Almazroi
Systems 2024, 12(3), 79; https://doi.org/10.3390/systems12030079 - 01 Mar 2024
Viewed by 1817
Abstract
Cybersecurity relies heavily on the effectiveness of intrusion detection systems (IDSs) in securing business communication because they play a pivotal role as the first line of defense against malicious activities. Despite the wide application of machine learning methods for intrusion detection, they have [...] Read more.
Cybersecurity relies heavily on the effectiveness of intrusion detection systems (IDSs) in securing business communication because they play a pivotal role as the first line of defense against malicious activities. Despite the wide application of machine learning methods for intrusion detection, they have certain limitations that might be effectively addressed by leveraging different deep learning architectures. Furthermore, the evaluation of the proposed models is often hindered by imbalanced datasets, limiting a comprehensive assessment of model efficacy. Hence, this study aims to address these challenges by employing data augmentation methods on four prominent datasets, the UNSW-NB15, 5G-NIDD, FLNET2023, and CIC-IDS-2017, to enhance the performance of several deep learning architectures for intrusion detection systems. The experimental results underscored the capability of a simple CNN-based architecture to achieve highly accurate network attack detection, while more complex architectures showed only marginal improvements in performance. The findings highlight how the proposed methods of deep learning-based intrusion detection can be seamlessly integrated into cybersecurity frameworks, enhancing the ability to detect and mitigate sophisticated network attacks. The outcomes of this study have shown that the intrusion detection models have achieved high accuracy (up to 91% for the augmented CIC-IDS-2017 dataset) and are strongly influenced by the quality and quantity of the dataset used. Full article
(This article belongs to the Special Issue Intelligent Systems and Cybersecurity)
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Review

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31 pages, 554 KiB  
Review
Distributed Control of Cyber Physical System on Various Domains: A Critical Review
by Muzaffar Hamzah, Md. Monirul Islam, Shahriar Hassan, Md. Nasim Akhtar, Most. Jannatul Ferdous, Muhammed Basheer Jasser and Ali Wagdy Mohamed
Systems 2023, 11(4), 208; https://doi.org/10.3390/systems11040208 - 20 Apr 2023
Cited by 9 | Viewed by 5548
Abstract
Cyber-Physical System (CPS) is a symbol of the fourth industrial revolution (4IR) by integrating physical and computational processes which can associate with humans in various ways. In short, the relationship between Cyber networks and the physical component is known as CPS, which is [...] Read more.
Cyber-Physical System (CPS) is a symbol of the fourth industrial revolution (4IR) by integrating physical and computational processes which can associate with humans in various ways. In short, the relationship between Cyber networks and the physical component is known as CPS, which is assisting to incorporate the world and influencing our ordinary life significantly. In terms of practical utilization of CPS interacting abundant difficulties. Currently, CPS is involved in modern society very vastly with many uptrend perspectives. All the new technologies by using CPS are accelerating our journey of innovation. In this paper, we have explained the research areas of 14 important domains of Cyber-Physical Systems (CPS) including aircraft transportation systems, battlefield surveillance, chemical production, energy, agriculture (food supply), healthcare, education, industrial automation, manufacturing, mobile devices, robotics, transportation, and vehicular. We also demonstrated the challenges and future direction of each paper of all domains. Almost all articles have limitations on security, data privacy, and safety. Several projects and new dimensions are mentioned where CPS is the key integration. Consequently, the researchers and academicians will be benefited to update the CPS workspace and it will help them with more research on a specific topic of CPS. 158 papers are studied in this survey as well as among these, 98 papers are directly studied with the 14 domains with challenges and future instruction which is the first survey paper as per the knowledge of authors. Full article
(This article belongs to the Special Issue Intelligent Systems and Cybersecurity)
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