A systematic literature review on wireless security testbeds in the cyber-physical realm

Kampourakis, Vyron; Gkioulos, Vasileios; Katsikas, Sokratis K.

doi:10.1016/j.cose.2023.103383

Cited by 14 publications

(4 citation statements)

References 75 publications

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“…The diversity of IoT devices, the massive data generated, the unstructured data processing, and data security are key challenges in the industrial cyber-physical cybersecurity realm [135,136]. Latif et al [137] developed a new architecture for IoT ecosystems, enhancing security and energy efficiency by integrating Software-Defined Networking and blockchain.…”

Section: Iot Applicationsmentioning

confidence: 99%

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

Bhumichai,

Smiliotopoulos,

Benton

et al. 2024

Information

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Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry.

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Section: Iot Applicationsmentioning

confidence: 99%

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

Bhumichai,

Smiliotopoulos,

Benton

et al. 2024

Information

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“…PLCs are designed to perform control functions within harsh industrial environments while withstanding extreme temperatures, vibrations, and noise conditions, among others. In other words, PLCs are tasked with the real-time control and monitoring of machinery and processes, making them indispensable components in various sectors, including automotive manufacturing, water treatment facilities, oil refineries, and power plants [3,5,18,19]. Given the crucial role of PLCs in critical infrastructures, their security and reliability are decisive.…”

Section: Programmable Logic Controllersmentioning

confidence: 99%

StructuredFuzzer: Fuzzing Structured Text-Based Control Logic Applications

Koffi,

Kampourakis,

Song

et al. 2024

Electronics

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Rigorous testing methods are essential for ensuring the security and reliability of industrial controller software. Fuzzing, a technique that automatically discovers software bugs, has also proven effective in finding software vulnerabilities. Unsurprisingly, fuzzing has been applied to a wide range of platforms, including programmable logic controllers (PLCs). However, current approaches, such as coverage-guided evolutionary fuzzing implemented in the popular fuzzer American Fuzzy Lop Plus Plus (AFL++), are often inadequate for finding logical errors and bugs in PLC control logic applications. They primarily target generic programming languages like C/C++, Java, and Python, and do not consider the unique characteristics and behaviors of PLCs, which are often programmed using specialized programming languages like Structured Text (ST). Furthermore, these fuzzers are ill suited to deal with complex input structures encapsulated in ST, as they are not specifically designed to generate appropriate input sequences. This renders the application of traditional fuzzing techniques less efficient on these platforms. To address this issue, this paper presents a fuzzing framework designed explicitly for PLC software to discover logic bugs in applications written in ST specified by the IEC 61131-3 standard. The proposed framework incorporates a custom-tailored PLC runtime and a fuzzer designed for the purpose. We demonstrate its effectiveness by fuzzing a collection of ST programs that were crafted for evaluation purposes. We compare the performance against a popular fuzzer, namely, AFL++. The proposed fuzzing framework demonstrated its capabilities in our experiments, successfully detecting logic bugs in the tested PLC control logic applications written in ST. On average, it was at least 83 times faster than AFL++, and in certain cases, for example, it was more than 23,000 times faster.

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“…Compared to, say, max pool, both backward and forwards, convolution is a significant leisure operation. Each training step is meant to become significantly longer if indeed the network is large [30][31][32][33][34][35].…”

Section: Cnn-based Feature Extractionmentioning

confidence: 99%

Smart Agricultural–Industrial Crop-Monitoring System Using Unmanned Aerial Vehicle–Internet of Things Classification Techniques

et al. 2023

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Unmanned aerial vehicles (UAVs) coupled with machine learning approaches have attracted considerable interest from academicians and industrialists. UAVs provide the advantage of operating and monitoring actions performed in a remote area, making them useful in various applications, particularly the area of smart farming. Even though the expense of controlling UAVs is a key factor in smart farming, this motivates farmers to employ UAVs while farming. This paper proposes a novel crop-monitoring system using a machine learning-based classification with UAVs. This research aims to monitor a crop in a remote area with below-average cultivation and the climatic conditions of the region. First, data are pre-processed via resizing, noise removal, and data cleaning and are then segmented for image enhancement, edge normalization, and smoothing. The segmented image was pre-trained using convolutional neural networks (CNN) to extract features. Through this process, crop abnormalities were detected. When an abnormality in the input data is detected, then these data are classified to predict the crop abnormality stage. Herein, the fast recurrent neural network-based classification technique was used to classify abnormalities in crops. The experiment was conducted by providing the present weather conditions as the input values; namely, the sensor values of temperature, humidity, rain, and moisture. To obtain results, around 32 truth frames were taken into account. Various parameters—namely, accuracy, precision, and specificity—were employed to determine the accuracy of the proposed approach. Aerial images for monitoring climatic conditions were considered for the input data. The data were collected and classified to detect crop abnormalities based on climatic conditions and pre-historic data based on the cultivation of the field. This monitoring system will differentiate between weeds and crops.

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A systematic literature review on wireless security testbeds in the cyber-physical realm

Cited by 14 publications

References 75 publications

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

StructuredFuzzer: Fuzzing Structured Text-Based Control Logic Applications

Smart Agricultural–Industrial Crop-Monitoring System Using Unmanned Aerial Vehicle–Internet of Things Classification Techniques

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