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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3039

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Prof. Dr. Francesco Mercaldo

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Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
Interests: software security; software testing; software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is facilitated by heterogeneous technologies which contribute to providing innovative and intelligent services in a large number of application domains. The satisfaction of security and privacy requirements in this scenario, are becoming a main challenge for IoT systems and their developers.

Nevertheless, most efforts in IoT security and privacy requirements look at these requirements from a high-level view. Hence, important aspects of security and privacy functionalities will be disregarded, causing wrong design decisions. Exploiting data from infrastructure, computers, and cyber physical systems, it can be possible to discover useful information from data in order to secure the systems from both administrators and end users.

Decision makers can make more informative and conscious decisions through this kind of emerging analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the security of processes. In this context, fuzzy logic can be properly used to help deal with issues associated with computer security and computer forensics.

Submissions are expected from, but not limited to, the following topics:

Securing private data on mobile and wearable devices
Security in cyber physical system
Security in Smart Grid and in Cloud computing environments
Formal methods for security
Artificial Intelligence for cybersecurity
Cybersecurity in healthcare
Fraud detection and forensics
Big Data security for complex data analysis (video, sensors, text, etc.)
Security issues in complex systems and environments

Prof. Dr. Francesco Mercaldo
Collection Editor

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Keywords

Internet of Things
Artificial Intelligence
Cybersecurity

Published Papers (3 papers)

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Research

23 pages, 2330 KiB

Open AccessArticle

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

by Nouf Saeed Alotaibi, Hassan I. Sayed Ahmed, Samah Osama M. Kamel and Ghada Farouk ElKabbany

Sensors 2024, 24(5), 1638; https://doi.org/10.3390/s24051638 - 02 Mar 2024

Viewed by 593

Abstract

The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to transfer and manage data between devices and is extensively employed for applications ranging from smart homes and industrial automation to healthcare and transportation systems. However, it lacks built-in security features, thereby making it vulnerable to many types of attacks such as man-in-the-middle (MitM), buffer overflow, pre-shared key, brute force authentication, malformed data, distributed denial-of-service (DDoS) attacks, and MQTT publish flood attacks. Traditional methods for detecting MQTT attacks, such as deep neural networks (DNNs), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and fuzzy logic, may exist. The increasing prevalence of device connectivity, sensor usage, and environmental scalability become the most challenging aspects that novel detection approaches need to address. This paper presents a new solution that leverages an H2O-based distributed machine learning (ML) framework to improve the security of the MQTT protocol in networks, particularly in IoT environments. The proposed approach leverages the strengths of the H2O algorithm and architecture to enable real-time monitoring and distributed detection and classification of anomalous behavior (deviations from expected activity patterns). By harnessing H2O’s algorithms, the identification and timely mitigation of potential security threats are achieved. Various H2O algorithms, including random forests, generalized linear models (GLMs), gradient boosting machine (GBM), XGBoost, and the deep learning (DL) algorithm, have been assessed to determine the most reliable algorithm in terms of detection performance. This study encompasses the development of the proposed algorithm, including implementation details and evaluation results. To assess the proposed model, various evaluation metrics such as mean squared error (MSE), root-mean-square error (RMSE), mean per class error (MCE), and log loss are employed. The results obtained indicate that the H2OXGBoost algorithm outperforms other H2O models in terms of accuracy. This research contributes to the advancement of secure IoT networks and offers a practical approach to enhancing the security of MQTT communication channels through distributed detection and classification techniques. Full article

(This article belongs to the Special Issue Deep Learning Security and Privacy Defensive Techniques)

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30 pages, 15869 KiB

Open AccessArticle

Keyboard Data Protection Technique Using GAN in Password-Based User Authentication: Based on C/D Bit Vulnerability

by Jaehyuk Lee, Wonbin Jeong and Kyungroul Lee

Sensors 2024, 24(4), 1229; https://doi.org/10.3390/s24041229 - 15 Feb 2024

Viewed by 441

Abstract

In computer systems, user authentication technology is required to identify users who use computers. In modern times, various user authentication technologies, including strong security features based on ownership, such as certificates and security cards, have been introduced. Nevertheless, password-based authentication technology is currently mainly used due to its convenience of use and ease of implementation. However, according to Verizon’s “2022 Data Breach Investigations Report”, among all security incidents, security incidents caused by password exposures accounted for 82%. Hence, the security of password authentication technology is important. Consequently, this article analyzes prior research on keyboard data attacks and defense techniques to draw the fundamental reasons for keyboard data attacks and derive countermeasures. The first prior research is about stealing keyboard data, an attack that uses machine learning to steal keyboard data to overcome the limitations of a C/D bit attack. The second prior research is an attack technique that steals keyboard data more efficiently by expanding the features of machine learning used in the first prior research. In this article, based on previous research findings, we proposed a keyboard data protection technique using GAN, a Generative Adversarial Network, and verified its feasibility. To summarize the results of performance evaluation with previous research, the machine learning-based keyboard data attack based on the prior research exhibited a 96.7% attack success rate, while the study’s proposed method significantly decreased the attack success rate by approximately 13%. Notably, in all experiments, the average decrease in the keyboard data classification performance ranged from a minimum of −29% to a maximum of 52%. When evaluating performance based on maximum performance, all performance indicators were found to decrease by more than 50%. Full article

(This article belongs to the Special Issue Deep Learning Security and Privacy Defensive Techniques)

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41 pages, 5655 KiB

Open AccessArticle

Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis

by Fatma S. Alrayes, Mohammed Zakariah, Maha Driss and Wadii Boulila

Sensors 2023, 23(20), 8362; https://doi.org/10.3390/s23208362 - 10 Oct 2023

Cited by 2 | Viewed by 1259

Abstract

Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization’s network security. This is because IDSs serve as the organization’s first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs’ performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model’s performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF’s capabilities in intrusion detection and network security solutions. Full article

(This article belongs to the Special Issue Deep Learning Security and Privacy Defensive Techniques)

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