Chaos generator device based on a 32 bit microcontroller embedded system

Giakoumis, Angelos; Volos, Christos; Stouboulos, I. N.; Polatoglou, Hariton Μ.; Kyprianidis, I. Μ.

doi:10.1109/mocast.2018.8376598

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Hence, memory blocks are described as arrays and divided into cells that can store data and be accessed using a unique identifier, representing its address or position relative to the memory array. Consequently, memory blocks can be either volatile or non-volatile, and the control processing unit "CPU" accesses them concerning the computer architecture; This can be either Von Neumann or Harvard architecture [49]. In this scenario, the proposed hybrid model is tested in AVR, Tensilica, and ARM microcontrollers.…”

Section: E Memory Consumptionmentioning

confidence: 99%

Hybrid Anomaly Detection Model on Trusted IoT Devices

Rosero-Montalvo

István

Tözün

et al. 2023

IEEE Internet Things J.

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Most machine learning proposals in the Internet of Things (IoT) are designed and evaluated on pre-processed datasets, where data acquisition and cleaning steps are often considered a black box. In addition, IoT environments have numerous challenges related to acquiring data from sensors, where sensitive data can be threatened by malicious users who seek to interfere with the communication channel or storage. Additionally, sensor data can also be affected by noise. Therefore, differentiating the type of threat/anomaly requires additional energy and computational resources. We propose to carry out data cleaning/anomaly techniques on the IoT device itself, not in the cloud servers but closer to the data source. Therefore, the IoT device sends trusted data to the Cloud. Among the benefits of this is the considerable reduction in the cost of implementation due to less movement of data between IoT devices and the Cloud. Consequently, we define three anomaly detection steps using smoothing filters, unsupervised learning, and deep learning techniques (i.e., hybrid model) to detect different variations of anomalies and threats while focusing on a small computational/memory footprint. The deployment of the hybrid model on AVR, Tensilica, and ARM microcontrollers showed that the last ones are an adequate target to implement the model because they best satisfy the necessary hardware requirements. The proposed model consumes 50KB of Flash and 12KB of RAM and processes data locally, achieving a bandwidth reduction of 60%. Finally, the hybrid model was tested in external datasets.

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Section: E Memory Consumptionmentioning

confidence: 99%

Hybrid Anomaly Detection Model on Trusted IoT Devices

Rosero-Montalvo

István

Tözün

et al. 2023

IEEE Internet Things J.

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“…In addition, today's embedded system designs consume much less energy. For these reasons, nowadays, embedded system devices have started to be used in the design of chaotic systems [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A class of unexcited hyperjerk systems with megastability and its analog and microcontroller-based embedded system design

et al. 2020

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Discovering chaotic systems with special features is of interest in the recent years. In this paper we introduce a new class of simple hyperjerk systems with infinitely coexisting chaotic attractors usually termed as megastability. The novelty of the proposed systems is that the systems shows megastability without external excitation which was not the case in most of the existing megastable attractors discussed in the literatures. Various dynamical properties of one of the proposed systems like the stability of equilibrium points, bifurcation and Lyapunov spectrum are discussed. Also, a circuit realization using off-the-shelf components is shown to prove the implementation feasibility of the systems. In addition, microcontroller based embedded design with graphic LCD of obtained new simple hyperjerk megastable oscillators was realized. Thus, the obtained new oscillators can be used both in a variety of real digital applications such as random number generators, encryption, communication and for educational purposes.

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