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Securing the Spectrum of Threats with IoT IDS Solutions
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Securing the Spectrum of Threats with IoT IDS Solutions

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4939

Special Issue Editors

Prof. Dr. Salvatore Domenic Morgera

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
Interests: cybersecurity; 5G; cloud–fog networking; Interrnet of Things; wireless networks; military networks; neurological networks; medical devices
Special Issues, Collections and Topics in MDPI journals
Dr. Mischa Atrov Morgera

E-Mail Website
Guest Editor
MM Consulting & Associates, Tampa, FL 33511, USA
Interests: sustainability; energy; commissioning; security; sensors and automation; smart environments

Special Issue Information

Dear Colleagues,

The goal of smart environments is to improve the quality of human life in terms of safety, privacy, security, comfort, and efficiency. The IoT paradigm is a key technology for realizing smart environments, ranging from healthcare, building automation, industrial control, and environmental monitoring, to supply-chain management and home and office. We are now aware of several vulnerabilities in IoT-based systems that create security threats and impact the realization of smart environments. It is imperative to secure IoT networks against the substantial losses that security and privacy breaches can bring to industry, government, and consumers.

To mitigate IoT-related security attacks, there is a crucial need for intrusion detection systems (IDSs) specifically designed for smart environment applications. Because of the limited computing, storage, transmission range, and energy capabilities of IoT devices and the specific protocols used, conventional IDSs are typically not an option for IoT environments. Despite previous work regarding the design and implementation of IDSs for the IoT paradigm, developing fast response, accurate, and robust IDSs for IoT-based smart environments remains a crucial task.

This Special Issue invites papers with novel architectures, methods, mechanisms, and features for IDS design based on the IoT model. Papers that describe security vulnerabilities in embedded devices; their origins in implementation, design, and/or deployment; and their elimination or mitigation fall within the scope of this Special Issue.

We also welcome papers dealing with security considerations for IoT sensors, ranging from medical sensors for pathogens, to building security and environmental sensors, to 5G enabled robots. Security is critical for all IoT devices, and sensor devices are no exception. Sensors play a critical role in IoT solutions, collecting the data that drive the entire solution. Ensuring the integrity of IoT data is crucial. Building security into sensors and detecting malicious intrusions present a unique challenge. Of interest in this area are novel approaches to the cost versus prioritization of attack vectors for lightweight security and IDS systems for both static and mobile devices.

This Special Issue is intended to be a seminal contribution to the literature. We also encourage contributions to emerging IoT standards, case studies involving actual IoT deployments, and examples demonstrating outcomes of security by design for IoT devices and sensors.

Prof. Dr. Salvatore Domenic Morgera
Dr. Mischa Atrov Morgera
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

  • Internet of Things (IoT)
  • Wireless attack
  • Encryption
  • Smart environment
  • Intrusion detection system
  • Smart sensors
  • Biosensors
  • Cybersecurity
  • Resource constrained networks
  • 5G
  • Security by design
  • IoT standards

Published Papers (1 paper)

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Research

21 pages, 3729 KiB  
Article
IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection
by Mnahi Alqahtani, Hassan Mathkour and Mohamed Maher Ben Ismail
Sensors 2020, 20(21), 6336; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216336 - 06 Nov 2020
Cited by 41 | Viewed by 3868
Abstract
Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT [...] Read more.
Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process. Full article
(This article belongs to the Special Issue Securing the Spectrum of Threats with IoT IDS Solutions)
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