Chaotic Network Research Articles

Intrusion Detection System Using Chaotic Walrus Optimization-based Convolutional Echo State Networks for IoT-assisted Wireless Sensor Networks

Wireless Sensor Networks (WSN) based Internet of Things (IoT) networks have achieved greater research interest due to their multi-purpose data collection and transmission over different geographical locations. However, the fabrication of different cyber-attacks in these networks has been a severe concern for applications such as remote healthcare, military communications, etc. These attacks question the integrity and security of WSN-IOT networks for such applications, and the traditional Intrusion Detection Systems (IDS) have shown increased false alarm rates because of their substantial processing overhead, resource constraints, and low detection rates. This paper presents an intelligent IDS model using Chaotic Walrus Optimization-based Convolutional Echo State Networks (CWO-CESN) to solve existing problems. It increases the detection accuracy of different attacks. In this CWO-CESN-based IDS model, the data are gathered from the sensor/IoT nodes and are pre-processed. Then, the proposed CWO-CESN learns the features from these data and classifies them into attacks and standard data classes. This proposed CWO-CESN is a hybrid classifier model that integrates Convolutional Neural Networks (CNN) and Echo State Networks (ESN) as a single model and employs Chaotic map-based population initialized Walrus Optimizer for optimizing the hyperparameters. Validated on benchmark datasets, the proposed CWO-CESN-based IDS model attained accuracies of 99%, 99.5%, and 99.8% for the detection of different attacks for NSL-KDD, WSN-DS and IoT-23 datasets, respectively, and ensured secured and reliable application in significant fields.

Chaotic Encryption-Based Network Robot for Indoor Security and Remote Video Monitoring

Chaotic Encryption-Based Network Robot for Indoor Security and Remote Video Monitoring

Master–slave synchronization of electrocardiogram chaotic networks dealing with stochastic perturbance

Master–slave synchronization of electrocardiogram chaotic networks dealing with stochastic perturbance

Lightweight High‐Throughput TRNG Based on Single‐Node Boolean Chaotic Structure

ABSTRACTWith the rise of the Internet and electronic devices, the security of network information is gaining attention, and the true random number generator (TRNG) is playing an increasingly crucial role in information security. TRNG, based on Boolean chaotic entropy source, has drawn significant interest due to its uncomplicated circuit design and minimal hardware resource usage. However, most existing structures consist of two‐input or three‐input logic devices, forming a complex multinode, geometrically symmetric Boolean chaotic network using multiple logic devices. This network configuration results in increased complexity and reduced throughput. This study introduces an entropy source based on Boolean chaos utilizing single‐node and four‐input XOR gates, which can be easily placed and routed on Xilinx Artix‐7 FPGA. It requires only 29 LUTs and 5 DFFs without any postprocessing, achieving a throughput of up to 700 Mb/s. The output of TRNG has successfully passed various tests including the autocorrelation test, NIST SP800‐22, NIST SP800‐90B, AIS‐31, and TESTU01 tests with favorable results. Furthermore, by applying a three‐stage XOR chain postprocessing on Xilinx Spartan‐6 FPGA and Xilinx Virtex‐6 FPGA, it has passed the NIST SP800‐22 and NIST SP800‐90B tests at 300 Mb/s. The structure was also tested using Xilinx Virtex‐6 FPGA under different temperature and voltage conditions, passing the NIST SP800‐90B IID test.

New approaches to the control of chronic inflammatory diseases with a focus on the endolysosomal system of immune cells

Abstract Chronic inflammation is implicated in many types of diseases, including cardiovascular, neurodegenerative, metabolic, and immune disorders. The search for therapeutic targets to control chronic inflammation often involves narrowing down the various molecules associated with pathology that have been discovered by various omics analyses. Herein, a different approach to identify therapeutic targets against chronic inflammation is proposed and one such target is discussed as an example. In chronically inflamed tissues, a large number of cells receive diverse proinflammatory signals, the intracellular signals are intricately integrated, and complicated intercellular interactions are orchestrated. This review focuses on effectively blocking this chaotic inflammatory signaling network via the endolysosomal system, which acts as a cellular signaling hub. In endolysosomes, the inflammatory signals mediated by pathogen sensors, such as Toll-like receptors, and the signals from nutrient and metabolic pathways are integrally regulated. Disruption of endolysosome signaling results in a strong anti-inflammatory effect by disrupting various signaling pathways, including pathogen sensor-mediated signals, in multiple immune cells. The endolysosome-resident amino acid transporter, solute carrier family 15 member 4 (SLC15A4), which plays an important role in the regulation of endolysosome-mediated signals, is a promising therapeutic target for several inflammatory diseases, including autoimmune diseases. The mechanisms by which SLC15A4 regulates inflammatory responses may provide a proof of concept for the efficacy of therapeutic strategies targeting immune cell endolysosomes.

Towards antifragility of cloud systems: An adaptive chaos driven framework

ContextUnlike resilience, antifragility describes systems that get stronger rather than weaker under stress and chaos. Antifragile systems have the capacity to overcome stressors and come out stronger, whereas resilient systems are focused on their capacity to return to their previous state following a failure. As technology environments become increasingly complex, there is a great need for developing software systems that can benefit from failures while continuously improving. Most applications nowadays operate in cloud environments. Thus, with this increasing adoption of Cloud-Native Systems they require antifragility due to their distributed nature. ObjectiveThe paper proposes UNFRAGILE framework, which facilitates the transformation of existing systems into antifragile systems. The framework employs chaos engineering to introduce failures incrementally and assess the system's response under such perturbation and improves the quality of system response by removing fragilities and introducing adaptive fault tolerance strategies. MethodThe UNFRAGILE framework's feasibility has been validated by applying it to a cloud-native using a real-world architecture to enhance its antifragility towards long outbound service latencies. The empirical investigation of fragility is undertaken, and the results show how chaos affects application performance metrics and causes disturbances in them. To deal with chaotic network latency, an adaptation phase is put into effect. ResultsThe findings indicate that the steady stage's behaviour is like the antifragile stage's behaviour. This suggests that the system could self-stabilise during the chaos without the need to define a static configuration after determining from the context of the environment that the dependent system was experiencing difficulties. ConclusionOverall, this paper contributes to ongoing efforts to develop antifragile software capable of adapting to the rapidly changing complex environment. Overall, the research provides an operational framework for engineering software systems that learn and improve through exposure to failures rather than just surviving them.

Novel competitive fractional chaotic circuits interaction networks with hierarchical structure and encryption application

In the realm of complex networks, the challenge of ensuring secure communication amidst the vulnerabilities of conventional encryption methods has become increasingly critical. This study delves into the complex realm of synchronized behaviors in networks, employing fractional-order chaotic circuits within hierarchically structured competitive interaction networks to enhance encryption security, particularly for medical image transmission. We propose a novel paradigm that transcends traditional synchronization methods used across various disciplines, from engineering to social sciences, by unveiling the intricate dynamics of how units within networks share interactions. Our approach leverages the unique properties of fractional chaos and network hierarchy, demonstrating that the proposed model, characterized by multi-directed links and competitive strategies, significantly improves synchronization. Through detailed analysis, including bifurcation diagrams and Lyapunov exponent plots, we uncover the optimal configurations of coupling strength and fractional order that lead to enhanced network synchronization. This synchronization is pivotal for our encryption application, showcasing a high level of security and privacy in the transmission of medical images. The encryption technique benefits from the network’s complex and synchronized dynamics, rendering it a formidable challenge for potential attackers to decipher the encrypted data. While our findings offer a promising mechanism for creating robust communication networks capable of securing sensitive medical data, the implications of our work extend beyond this application. The successful application of fractional-order chaotic circuits sets a groundwork for securing diverse types of data transmissions against the evolving landscape of cyber threats. This research not only marks a significant advancement in network security but also opens new avenues for applying these principles across a spectrum of fields where data security and privacy are paramount.

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks

The echo state network (ESN) is an excellent machine learning model for processing time-series data. This model, utilising the response of a recurrent neural network, called a reservoir, to input signals, achieves high training efficiency. Introducing time-history terms into the neuron model of the reservoir is known to improve the time-series prediction performance of ESN, yet the reasons for this improvement have not been quantitatively explained in terms of reservoir dynamics characteristics. Therefore, we hypothesised that the performance enhancement brought about by time-history terms could be explained by delay capacity, a recently proposed metric for assessing the memory performance of reservoirs. To test this hypothesis, we conducted comparative experiments using ESN models with time-history terms, namely leaky integrator ESNs (LI-ESN) and chaotic echo state networks (ChESN). The results suggest that compared with ESNs without time-history terms, the reservoir dynamics of LI-ESN and ChESN can maintain diversity and stability while possessing higher delay capacity, leading to their superior performance. Explaining ESN performance through dynamical metrics are crucial for evaluating the numerous ESN architectures recently proposed from a general perspective and for the development of more sophisticated architectures, and this study contributes to such efforts.

Theory of Coupled Neuronal-Synaptic Dynamics

In neural circuits, synaptic strengths influence neuronal activity by shaping network dynamics, and neuronal activity influences synaptic strengths through activity-dependent plasticity. Motivated by this fact, we study a recurrent-network model in which neuronal units and synaptic couplings are interacting dynamic variables, with couplings subject to Hebbian modification with decay around quenched random strengths. Rather than assigning a specific role to the plasticity, we use dynamical mean-field theory and other techniques to systematically characterize the neuronal-synaptic dynamics, revealing a rich phase diagram. Adding Hebbian plasticity slows activity in already chaotic networks and can induce chaos in otherwise quiescent networks. Anti-Hebbian plasticity quickens activity and produces an oscillatory component. Analysis of the Jacobian shows that Hebbian and anti-Hebbian plasticity push locally unstable modes toward the real and imaginary axes, respectively, explaining these behaviors. Both random-matrix and Lyapunov analysis show that strong Hebbian plasticity segregates network timescales into two bands, with a slow, synapse-dominated band driving the dynamics, suggesting a flipped view of the network as synapses connected by neurons. For increasing strength, Hebbian plasticity initially raises the complexity of the dynamics, measured by the maximum Lyapunov exponent and attractor dimension, but then decreases these metrics, likely due to the proliferation of stable fixed points. We compute the marginally stable spectra of such fixed points as well as their number, showing exponential growth with network size. Finally, in chaotic states with strong Hebbian plasticity, a stable fixed point of neuronal dynamics is destabilized by synaptic dynamics, allowing any neuronal state to be stored as a stable fixed point by halting the plasticity. This phase of freezable chaos offers a new mechanism for working memory. Published by the American Physical Society 2024

Complete synchronization of three-layer Rulkov neuron network coupled by electrical and chemical synapses.

This paper analyzes the complete synchronization of a three-layer Rulkov neuron network model connected by electrical synapses in the same layers and chemical synapses between adjacent layers. The outer coupling matrix of the network is not Laplacian as in linear coupling networks. We develop the master stability function method, in which the invariant manifold of the master stability equations (MSEs) does not correspond to the zero eigenvalues of the connection matrix. After giving the existence conditions of the synchronization manifold about the nonlinear chemical coupling, we investigate the dynamics of the synchronization manifold, which will be identical to that of a synchronous network by fixing the same parameters and initial values. The waveforms show that the transient chaotic windows and the transient approximate periodic windows with increased or decreased periods occur alternatively before asymptotic behaviors. Furthermore, the Lyapunov exponents of the MSEs indicate that the network with a periodic synchronization manifold can achieve complete synchronization, while the network with a chaotic synchronization manifold can not. Finally, we simulate the effects of small perturbations on the asymptotic regimes and the evolution routes for the synchronous periodic and the non-synchronous chaotic network.

A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

With the enhancement of information volume, people are not satisfied with transmitting only a single secret image at a time but chase to hide multiple secret images in a single picture; however, the large-capacity steganographic scale can easily lead to the degradation of the quality of the image, which attracts the attention of eavesdroppers. In this paper, we propose a Chaotic mapping-enHanced imAge Steganography nEtwork (CHASE), which pioneers to hide colour images in grey images and reduces the difference between the container image and the cover image through the image permutation method, so as to enhance the security of the steganography. The method demonstrates excellent steganalysis resistance in experiments and introduces Generative Adversarial Networks (GANs) to improve the image fidelity in large-capacity steganographic scales. The fusion of chaotic mapping and GAN optimisation enables the steganographic network to simultaneously balance security and image quality. The experimental results show that CHASE can keep the secret image with good invisibility under large-capacity steganographic scales, and at the same time, it can reveal the secret image with high fidelity, and its steganalysis-resistant capability is much better than other state-of-the-art methods.

A High-Precision Chaotic LSTM Network Model Optimized by FrVMD for the Earth Natural Pulse Electromagnetic Signal Intensity Trend Forecast

A High-Precision Chaotic LSTM Network Model Optimized by FrVMD for the Earth Natural Pulse Electromagnetic Signal Intensity Trend Forecast

Integrating Quadratic Polynomial and Symbolic Chaotic Map-Based Feistel Network to Improve Image Encryption Performance

Integrating Quadratic Polynomial and Symbolic Chaotic Map-Based Feistel Network to Improve Image Encryption Performance

Мониторинг состояния растительности в районе разработки Тасеевского месторождения (Забайкальский край)

The article presents the results of an environmental survey conducted in the development area of the Taseyev gold deposit of the Baleysky district of the Trans-Baikal Territory. Environmental monitoring was carried out on the most vulnerable component of the natural environment in order to ensure the environmental safety of the facility during the implementation of the construction of a heap leaching complex on the basis of the technogenic waste deposit “Baleyskoy ZIF‑1” of the Trans-Baikal Territory. The current state of the vegetation cover was studied. An analysis of the flora of the technogenically disturbed territory of the Taseevskoye field was carried out. As part of the local monitoring of the gold deposit and its environs, 4 types of plant complexes were identified – forest, meadow-steppe, near-river and synanthropic. According to the results of the study, a satisfactory state of plant communities was revealed. A quantitative indicator of diversity has been established, which is more than 80 species within the boundaries of the allotment. Species listed in the Red Book of the Trans-Baikal Territory, growing in the background areas of this field, are noted. A change in the natural vegetation cover on the area of the technogenically disturbed landscape was recorded as a result of industrial work – the movement of soil masses, dumping and excavation. The territory is covered by a chaotic network of country roads that disturb the soil and soil cover. Along the roads gully erosion is developed. There is a negative impact and a decrease in the recreational ecosystem services of the Baleisky district due to the production activities of gold mining enterprises (insufficient formation of a comfortable environment for the population). The presence of synanthropic communities in the anthropogenically disturbed area with traces of household waste dumps, sewage disposal, changes in the microrelief during industrial development on the river is traced Unda. In general, despite the adverse factors affecting the vegetation, plants adapt to stressful situations, all disturbed areas are capable of self-healing.

Finite-Time Nonfragile Synchronization Control of Time-Varying Delays T-S Fuzzy Discrete Chaotic Network Systems With Deception Attacks

Finite-Time Nonfragile Synchronization Control of Time-Varying Delays T-S Fuzzy Discrete Chaotic Network Systems With Deception Attacks

INVESTIGATION AND THE ANALYSIS OF MEDICAL BIG DATA AND THE EMPLOYMENT OF ARTIFICIAL INTELLIGENCE FOR DISEASE PREDICTION

The development of technologies like big data, cloud services, Internet+, and artificial intelligence has greatly improved data communication services in the traditional pharmaceutical industry, driving the progress of the healthcare sector in our country. These advancements have facilitated healthcare system reforms, enhanced the quality and efficiency of medical services, and fostered technological innovation. In this context, our research focuses on the following areas and draws the following conclusions: In recent years, the pharmaceutical industry has leveraged technologies such as big data, cloud computing, Internet+, and artificial intelligence to enhance data transmission services. These advancements are considered crucial strategic resources for the pharmaceutical industry and ongoing healthcare system reforms in our country. Their goal is to improve the efficiency and quality of medical services, establish new models of healthcare delivery, and drive economic growth. Given this, our research aims to further explore these advancements. There is an increasing demand for an in-depth exploration of the utilization of logic in the fields of artificial intelligence and computer science. To address this need, the Manual of Logic in Artificial Intelligence and Logic Programming, along with its companion, the Manual of Logic in Computer Science, were developed. These resources combine comprehensive investigations and fundamental research to delve into various underlying topics across multiple disciplines. A foundational understanding of mathematical complexity is assumed as background knowledge, making the material of interest to logicians and mathematicians [1]. However, existing works primarily focus on core concepts and technologies, leaving out extensions, applications, and languages. The handbook aims to provide a comprehensive coverage of constraint programming, offering readers an accurate overview of the entire field and its potential. While each topic is presented in a survey-like manner, some details may be omitted in favor of broader coverage [2].Among various types of uncertainties, randomness and ambiguity are considered crucial and fundamental. The relationship between randomness and ambiguity is discussed, exploring uncertain states and their changes through measures like entropy and hyperentropy. The study investigates uncertainties in chaotic, fractal, and complex networks' evolutions and differentiations. A simple and effective method for simulating uncertainty through knowledge representation is proposed, providing a foundation for automating logical and visual thinking in the presence of uncertainty [4]. The appropriate timing for monitoring implementation remains unclear. Although there is room for improvement, if a data processing algorithm functions correctly, it can be employed separately from the original algorithm. However, there are limitations to its usage [5] Keywords: Big Data, biomedicine, cluster, electronic health cards, healthcare.

Electricity consumption modeling by a chaotic convolutional radial basis function network

Electricity consumption modeling by a chaotic convolutional radial basis function network

Recognition of Noisy Digital Images Using the Asymmetric Coupling Semiconductor Chaotic Lasers Network

In this work, we construct a model of an asymmetrically coupled network of semiconductor chaotic lasers in order to recognize noisy digital images of digits 0–9, derived from different samples in the digital image sets 0–9 found within the MNIST dataset. Here, the lasers network consists of eight asymmetrically coupled semiconductor lasers. The chaotic lasers network is driven by the external inputs, which encode one noise digital image to be recognized. The outputs of the chaotic lasers network driven by a total of 40 samples from the digital image sets 0–9 are utilized as ten sets of reference signals. The output of the chaotic lasers network induced by one noisy digital image is used as a test signal. By judging the maximum of the correlations of the test signal with the ten sets of reference signals, all noisy digital images 0–9 can be recognized well under different noises. Moreover, we further explore the recognition rate for each noisy digital image under different noises and a fixed injection strength. It is found that all noisy digital images can be recognized well under a certain low injection strength. The recognition-rates of all noisy digital images can further decrease to a certain extent under higher noise and a fixed the injection strength. The injection strength has little influence on the recognition rate of one noise digital image target with lower noise. The recognition rate under higher noise maintains a higher value (more than 0.9) when the injection strength is smaller than a certain value, but for the larger injection strength, the recognition rate exhibits further decrease. The modeled chaotic lasers network can play the role of photonic accelerators for the recognition of the noisy digital images.

An efficient spam detector model for accurate categorization of spam tweets using quantum chaotic optimization-based stacked recurrent network

An efficient spam detector model for accurate categorization of spam tweets using quantum chaotic optimization-based stacked recurrent network