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Security Issues and Solutions in Sensing Systems and Networks

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 15649

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Special Issue Editors

Dr. Tiago Cruz

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Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: critical infrastructure protection; cyber-physical systems security; desktop management; O&M organization; low-level management support
Special Issues, Collections and Topics in MDPI journals

Dr. Paulo Alexandre Ferreira Simões

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Guest Editor

Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, P-3030-290 Coimbra, Portugal
Interests: network and infrastructure management; security; critical infrastructure protection; virtualization of networking and computing resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor systems and networks have become pervasive, often found at the core of many consumer and industrial IoT applications, with scales ranging from domestic, in-premises systems to geographically dispersed infrastructures. Regardless of their size or scope, such systems have one thing in common: a set of requirements in terms of security and safety, which ultimately define their criticality. Accidental or malicious disturbances might disrupt the nature of the involved control processes and applications, whose impact may range from a minor inconvenience to major, life-threatening incidents, especially in the case of critical infrastructures or applications.

When it comes to sensor network security, each context has its own specific issues and challenges: while vehicular networks constitute a good example of a self-contained scenario with a wide range of security and safety implications, massively distributed systems are nonetheless important. With many of the latter quickly unfolding on a massive scale (as is the case for smart grids, which have pushed infrastructure components such as smart meters and inverters up to the consumer’s doorstep), it is becoming increasingly difficult to ensure reliable, secure, and continuous operation in face of an increasingly diversified threat landscape.

This Special Issue, organised under the auspices of the POWER and Smart5Grid P2020 projects, is aimed at representing the latest advances in security for sensing systems and networks, encompassing different application scenarios such as (but not limited to):

Data-driven cyber-security for sensor applications;
Consumer sensor applications;
Industrial IoT safety and security applications;
Algorithms and techniques for anomaly detection, for safety and security.

Dr. Tiago Cruz
Dr. Paulo Alexandre Ferreira Simões
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.

Keywords

wireless and wired sensor networks
industrial IoT safety and security
hardware security for sensor networks
communications security in sensor network environments
cybersecurity for sensor and field networks
security frameworks and testbeds for WSN, both for research and educational purposes
intrusion detection systems for field and sensor networks
threat modelling in sensor networks
security and trust management in sensor networks
privacy-preserving technologies for sensor networks
vulnerability analysis of domain-specific protocols, standards and technologies

Published Papers (5 papers)

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Research

27 pages, 2922 KiB

Open AccessArticle

Enhancing Network Visibility and Security with Advanced Port Scanning Techniques

by Rana Abu Bakar and Boonserm Kijsirikul

Sensors 2023, 23(17), 7541; https://doi.org/10.3390/s23177541 - 30 Aug 2023

Cited by 1 | Viewed by 2481

Abstract

Network security is paramount in today’s digital landscape, where cyberthreats continue to evolve and pose significant risks. We propose a DPDK-based scanner based on a study on advanced port scanning techniques to improve network visibility and security. The traditional port scanning methods suffer from speed, accuracy, and efficiency limitations, hindering effective threat detection and mitigation. In this paper, we develop and implement advanced techniques such as protocol-specific probes and evasive scan techniques to enhance the visibility and security of networks. We also evaluate network scanning performance and scalability using programmable hardware, including smart NICs and DPDK-based frameworks, along with in-network processing, data parallelization, and hardware acceleration. Additionally, we leverage application-level protocol parsing to accelerate network discovery and mapping, analyzing protocol-specific information. In our experimental evaluation, our proposed DPDK-based scanner demonstrated a significant improvement in target scanning speed, achieving a 2× speedup compared to other scanners in a target scanning environment. Furthermore, our scanner achieved a high accuracy rate of 99.5% in identifying open ports. Notably, our solution also exhibited a lower CPU and memory utilization, with an approximately 40% reduction compared to alternative scanners. These results highlight the effectiveness and efficiency of our proposed scanning techniques in enhancing network visibility and security. The outcomes of this research contribute to the field by providing insights and innovations to improve network security, identify vulnerabilities, and optimize network performance. Full article

(This article belongs to the Special Issue Security Issues and Solutions in Sensing Systems and Networks)

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29 pages, 5558 KiB

Open AccessArticle

Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks

by Ulaa AlHaddad, Abdullah Basuhail, Maher Khemakhem, Fathy Elbouraey Eassa and Kamal Jambi

Sensors 2023, 23(17), 7464; https://doi.org/10.3390/s23177464 - 28 Aug 2023

Cited by 8 | Viewed by 1875

Abstract

The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid’s communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%. Full article

(This article belongs to the Special Issue Security Issues and Solutions in Sensing Systems and Networks)

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33 pages, 12420 KiB

Open AccessArticle

Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response

by Lucian Florin Ilca, Ogruţan Petre Lucian and Titus Constantin Balan

Sensors 2023, 23(15), 6757; https://doi.org/10.3390/s23156757 - 28 Jul 2023

Cited by 4 | Viewed by 2619

Abstract

In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging open-source solutions and software, a system specifically designed for SMEs with up to 250 employees is developed, focusing on the detection of new threats. Through extensive testing and validation, as well as efficient algorithms and techniques for anomaly detection, safety, and security, the effectiveness of the approach in enhancing SMEs’ cyber-defense capabilities and bolstering their overall cyber-resilience is demonstrated. The findings highlight the practicality and scalability of utilizing open-source resources to address the unique cybersecurity challenges faced by SMEs. The proposed system combines advanced malware analysis techniques with real-time threat intelligence feeds to identify and analyze malicious activities within SME networks. By employing machine-learning algorithms and behavior-based analysis, the system can effectively detect and classify sophisticated malware strains, including those previously unseen. To evaluate the system’s effectiveness, extensive testing and validation were conducted using real-world datasets and scenarios. The results demonstrate significant improvements in malware detection rates, with the system successfully identifying emerging threats that traditional security measures often miss. The proposed system represents a practical and scalable solution using containerized applications that can be readily deployed by SMEs seeking to enhance their cyber-defense capabilities. Full article

(This article belongs to the Special Issue Security Issues and Solutions in Sensing Systems and Networks)

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22 pages, 4432 KiB

Open AccessArticle

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

by Rana Abu Bakar, Xin Huang, Muhammad Saqib Javed, Shafiq Hussain and Muhammad Faran Majeed

Sensors 2023, 23(6), 3333; https://doi.org/10.3390/s23063333 - 22 Mar 2023

Cited by 16 | Viewed by 3937

Abstract

Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard. Full article

(This article belongs to the Special Issue Security Issues and Solutions in Sensing Systems and Networks)

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18 pages, 7646 KiB

Open AccessArticle

Research on Security Weakness Using Penetration Testing in a Distributed Firewall

by Andrei-Daniel Tudosi, Adrian Graur, Doru Gabriel Balan and Alin Dan Potorac

Sensors 2023, 23(5), 2683; https://doi.org/10.3390/s23052683 - 01 Mar 2023

Cited by 2 | Viewed by 4055

Abstract

The growing number of cyber-crimes is affecting all industries worldwide, as there is no business or industry that has maximum protection in this domain. This problem can produce minimal damage if an organization has information security audits periodically. The process of an audit includes several steps, such as penetration testing, vulnerability scans, and network assessments. After the audit is conducted, a report that contains the vulnerabilities is generated to help the organization to understand the current situation from this perspective. Risk exposure should be as low as possible because in cases of an attack, the entire business is damaged. In this article, we present the process of an in-depth security audit on a distributed firewall, with different approaches for the best results. The research of our distributed firewall involves the detection and remediation of system vulnerabilities by various means. In our research, we aim to solve the weaknesses that have not been solved to date. The feedback of our study is revealed with the help of a risk report in the scope of providing a top-level view of the security of a distributed firewall. To provide a high security level for the distributed firewall, we will address the security flaws uncovered in firewalls as part of our research. Full article

(This article belongs to the Special Issue Security Issues and Solutions in Sensing Systems and Networks)

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