Wireless Sensor Networks (WSNs) technology is extensively utilized in various applications necessitating monitoring and control functionalities. Nonetheless, some unresolved difficulties impede its effective deployment, primarily related to security concerns. This study investigates and analyzes the parameter sensitivity of the Susceptible-Exposed1-Exposed2-Infected-Recovered (SE _1 E _2 IR) model, derived from the classical Susceptible-Exposed-Infected-Recovered (SEIR) epidemic theory and used for the early detection of multi-malware activities in WSNs. In this model, two exposed states have been considered based on the assumption of two different types of malware attacks in the network. The primary SE _1 E _2 IR model develops a mechanism for the earlier detection of malware attack incidents in WSNs. The model is structured upon a system of ordinary differential equations. The basic reproduction number is also determined as a crucial factor for characterizing malware propagation within the network. It can be helpful to identify the circumstances in which the network remains almost safe or the risk of a malware outbreak. This work offers a fundamental comprehension of sensitivity analysis for the epidemic SE _1 E _2 IR model. Furthermore, it examines the efficacy of the parameters and analyzes the impact of different model parameters on malware spread in WSNs. Then, an analytical comparison of the suggested model with existing models is presented. Numerical simulations are run in MATLAB to augment the theoretical analyses.

Nowadays, Wireless Sensor Network (WSN) technology is widely used in many applications requiring distributed monitoring and control capabilities. When large WSN infrastructures are involved, the diffusion of malware among nodes becomes a critical issue, requiring a clear understanding of propagation dynamics to guarantee timely countermeasures. By considering that fractional-order models demonstrated to be particularly effective in forecasting the dynamics of complex real-life processes, this study improves the Susceptible-Exposed1-Exposed2-Infected-Recovered (SE _1 E _2 IR) model, derived from the traditional SEIR epidemic theory and used for the early detection of multi-malware activities in WSNs. In particular, a fractional SE _1 E _2 IR epidemic model has been formulated to explain the propagation of multiple malware infections according to a more versatile and realistic view. The model is structured upon a system of fractional differential equations. The basic reproduction number is also determined as a crucial factor for characterizing malware propagation within the network. The model’s free and endemic equilibrium points are calculated, and the stability of the equilibrium points is investigated to find requirements for preventing continuous malware propagation in WSNs. To complement the theoretical analyses, MATLAB is used to perform numerical simulations.

Internet of Things (IoT) networks are susceptible to intrusions, and intrusion detection is a challenging task due to the lack of labeled attack data, the non-stationary property of benign traffic, and the emergence of new attack variants. In order to address these challenges, this paper introduces ConFID (Conformal One-Class Framework for Unsupervised Zero-Day Intrusion Detection in IoT Networks), a one-class detection framework trained only on benign traffic and incorporating three synergistic mechanisms: a hierarchical autoencoder with a temporally aware regularization objective, a multi-layer Mahalanobis scoring mechanism estimated via the Ledoit-Wolf shrinkage procedure, and a conformal calibration stage that provides a finite-sample, distribution-free guarantee that the False Positive Rate (FPR) will not exceed any user-defined level α. On the large-scale benchmark CIC-IoT-2023, with more than 46 million flows in 33 attack categories, collected from 105 real IoT devices, the framework obtains an AUROC of 0.9921 and an F1 score of 0.9406, matching or outperforming Isolation Forest, One-Class SVM, Deep SVDD, Simple Autoencoder, and a flat Mahalanobis baseline in all evaluation metrics. ConFID has a comparable detection rate to standard baselines, but the difference is that it formally guarantees the FPR, which is not provided by any existing unsupervised Intrusion Detection System baselines. The conformal guarantee is validated empirically, as the realized FPRs match the target level within 0.008 over a wide range of operating points. The leave-one-attack-out zero-day protocol achieves near-perfect detection on Mirai-based attacks, while spoofing-type threats with flow-level signatures resembling legitimate traffic remain a challenge for all evaluated methods.

The Vehicular Ad-hoc Network (VANET) is emerging as a new networking/communication model that offers remarkable approaches for controlling and managing vehicular traffic. In this scenario, Virtual traffic light (VTL) techniques, advanced navigation support, and fleet management facilities attempt to handle traffic management and control concerns by leveraging vehicular network communication paradigms. The most important communication models in vehicular networks are characterized as vehicle-to-everything (V2X) communications technology. In V2X communications, security issues, and related countermeasures are crucial, as they are in other wireless technologies. Our survey provides a systematic overview of various research directions by highlighting and investigating the most critical V2X security issues and related countermeasures. First, we discuss the points mentioned in the previous papers, including the architecture, main characteristics, and communication models of the V2X networks. Furthermore, some of the most significant applications of these mission-critical networks reported in the published papers and the main security challenges and requirements will be highlighted. We also arrange corresponding (existing) resources, to investigate different solutions for securing V2X communications. Finally, a few suggestions for future research related to V2X security challenges are presented. In general, this article reviews and summarizes the studies of the last ten years. This survey will help make it easier for scholars studying V2X communication security and related fields to keep track of the academic frontier, by offering a sufficiently clear landscape and a roadmap.

Wireless sensor networks (WSNs) are attracting significant interest due to their substantial potential in various applications. The present paper examines the Susceptible-Exposed1-Exposed2-Infectious1-Infectious2-Recovered-Vaccinated (S E _1 E _2 I _1 I _2 RV) model constructed according to the classical SEIRV epidemic model. The original SEIRV model, which includes a vaccination compartment, offers a structure for precisely capturing the spatial and temporal dynamics of the process of malware propagation. In the SE _1 E _2 I _1 I _2 RV model, two distinct exposed and infected states are considered due to the assumption of two types of malware attacks, specifically worms and viruses, within the network. The considered model is based on a system of differential equations. The free equilibrium points and model stability are investigated. The basic reproduction number, a critical parameter for characterizing malware propagation in WSNs, is calculated. Numerical simulations were conducted utilizing Matlab to validate the theoretical analyses.

The propagation of malware in wireless sensor networks has recently garnered significant attention from researchers as a pressing issue. Wireless sensor networks consist of sensor nodes interconnected in a decentralized and distributed manner via wireless connectivity. Malware attacks can raise the energy consumption of wireless sensor network sensor nodes. The propagation of the infection initiates from a single infected node and utilizes neighboring nodes to increase throughout the entirety of the wireless sensor network. Hence, a comprehensive knowledge of spreading malware patterns in wireless sensor networks is advantageous. This paper examines the current models to analyze malware propagation in wireless sensor networks. This study presents a comprehensive overview of the strategies developed over the past decade. Equation-based epidemic models are observed to accurately represent the critical characteristics of wireless sensor networks and the spread of malware within them. The vulnerability of wireless sensor networks to malware attacks poses a significant challenge due to the limited defense capabilities of sensors. Consequently, the propagation of malware within these networks concerns the security community. This study examines the most crucial and up-to-date global epidemic models to explain network malware propagation.

The rapid progress of Internet technology has led to a strong increase in the use of online social networks for disseminating information on the Internet. In this scenario, it is crucial to establish approaches that can effectively reduce the diffusion of false information (fake news) that can potentially cause harm to society. A defensive approach, based on integer-order differential equations, has been recently developed to analyze the effects of verification and blocking of users for containing the spread of fake news. Starting from it, we introduce a novel fractional model providing a more accurate, powerful, and realistic representation of the transmission of fake news messages. The model aims to predict the spread of such messages, by better considering the effect of the system’s status evolution over time. The use of fractional differential equations to schematize the propagation of fake news results in incorporating a greater amount of memory information and better considering hereditary properties of the system of interest, also capturing its hidden nonlinear dynamics, mainly related to fractality and multiscale nature.

This work presents a new numerical method for solving Volterra-type integro functional equations with variable bounds and mixed delay. This paper applies discrete orthogonal Hahn polynomials and their properties numerically. A discrete scalar product is associated with discrete orthogonal polynomials. Several numerical experiments (including linear and nonlinear) for multiple test problems are provided to validate the accuracy of this method.

The advent of the Internet of Things, with the consequent changes in network architectures and communication dynamics, has affected the security market by introducing further complexity in traffic flow analysis, classification, and detection activities. Consequently, to face these emerging challenges, new empowered strategies are needed to effectively spot anomalous events within legitimate traffic and guarantee the success of early alerting facilities. However, such detection and classification strategies strongly depend on the right choice of employed features, which can be mined from individual or aggregated observations. Therefore, this work explores the theory of dynamic non-linear systems for effectively capturing and understanding the more expressive Internet traffic dynamics arranged as Recurrence Plots. To accomplish this, it leverages the abilities of Convolutional Autoencoders to derive meaningful features from the constructed plots. The achieved results, derived from a real dataset, demonstrate the effectiveness of the presented approach by also outperforming state-of-the-art classifiers.

The continuous emergence of new and sophisticated malware specifically targeting Android-based Internet of Things devices is causing significant security hazards and is consequently fostering the need for effective detection models and strategies able to work with these hardware-constrained devices. In addition, since such models are often trained on confidential application data, many involved subjects are reluctant to share their data for this purpose. Accordingly, several Federated Learning-based solutions are emerging, which rely on the capabilities of Machine Learning models in malware detection/classification without sharing user data. However, Federated Learning methods are often adversely affected by non-independent and identically distributed data in terms of both the required training time and classification results. Therefore, a promising solution could be to overcome the Federated Learning-related issues by preserving the privacy of end-user data. In this direction, the capabilities of Markov chains and associative rules are extended within a federated environment to face malware classification tasks in the IoT scenario. The presented approach, evaluated on several malware families, has achieved an average accuracy of 99% in the presence of centralized and decentralized unbalanced training/testing data by overcoming the most common state-of-the-art approaches. Also, its runtime performance is comparable with centralized ones by considering several non-independent and identically distributed dataset partitions, splitting criteria, and clients, respectively.

Feature selection and its subsequent dimensionality reduction are significant problems in machine learning and it is at the core of several data science techniques. The ‘shape’ of data, or in other words its related topological properties, can provide crucial insights into the corresponding data types and sources and it enables the identification of general properties that facilitate its analysis and assessment. In this article, we discuss an information theoretic approach combined with data homological properties to assess dimensionality reduction, which can be applied to semantic feature selection.

This study proposes a numerical technique based on a hybrid of block-pulse functions and Chelyshkov polynomials to solve fractional delay differential equations. The Galerkin approach transforms the solution of fractional delay differential equations into a system of algebraic equations using the fractional operational matrix of integration for these hybrid functions. The suggested method's accuracy and efficiency are demonstrated using numerical examples.

This work presents a novel formulation for the numerical solution of optimal control problems related to nonlinear Volterra fractional integral equations systems. A spectral approach is implemented based on the new polynomials known as Chelyshkov polynomials. First, the properties of these polynomials are studied to solve the aforementioned problems. The operational matrices and the Galerkin method are used to discretize the continuous optimal control problems. Thereafter, some necessary conditions are defined according to which the optimal solutions of discrete problems converge to the optimal solution of the continuous ones. The applicability of the proposed approach has been illustrated through several examples. In addition, a comparison is made with other methods for showing the accuracy of the proposed one, resulting also in an improved efficiency.

Nowadays both sciences and technology, including Intelligent Transportation Systems, are involved in improving current approaches. Overview studies give you fast, comprehensive, and easy access to all of the existing approaches in the field. With this inspiration, and the effect of traffic congestion as a challenging issue that affects the regular daily lives of millions of people around the world, in this work, we concentrate on communications paradigms which can be used to address traffic congestion problems. Vehicular Ad-hoc Networking (VANET), a modern networking technology, provides innovative techniques for vehicular traffic control and management. Virtual traffic light (VTL) methods for VANET seek to address traffic issues through using vehicular network communication models. These communication paradigms can be classified into four scenarios: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) and Vehicle-to-Pedestrian (V2P). In general, these four scenarios are included in the category of vehicle-to-everything (V2X). Therefore, in this paper, we provide an overview of the most important scenarios of V2X communications based on their characteristics, methodologies, and assessments. We also investigate the applications and challenges of V2X.

This research examines and compares the construction of protein-protein interaction (PPI) networks of CD4 + and CD8 + T cells and investigates why studying these cells is critical after HIV infection. This study also examines a mathematical model of fractional HIV infection of CD4 + T cells and proposes a new numerical procedure for this model that focuses on a recent kind of orthogonal polynomials called discrete Chebyshev polynomials. The proposed scheme consists of reducing the problem by extending the approximated solutions and by using unknown coefficients to nonlinear algebraic equations. For calculating unknown coefficients, fractional operational matrices for orthogonal polynomials are obtained. Finally, there is an example to show the effectiveness of the recommended method. All calculations were performed using the Maple 17 computer code.

We consider pseudofinite MV-algebras. As a main result, we show that an infinite MV-algebra is pseudofinite if and only if it is definably well founded, improving a result of a previous paper. Moreover, we show that the theory of pseudofinite MV-algebras has a partial form of elimination of quantifiers. Further, we show that the class of pseudofinite MV-chains and the class of pseudofinite MV-algebras are not finitely axiomatizable, we give some collapsing results for pseudofinite MV-algebras, we consider relative subalgebras of pseudofinite MV-algebras, and we study ideals of pseudofinite MV-algebras.

We survey some representative results on time-delay fractional differential optimal control problems. In this paper we provide a review of the techniques, developed in the last decade, for the numerical solution of time-delay fractional optimal control problems. In particular, Chebyshev and Chelyshkov wavelet methods, continuous and discrete Chebyshev polynomials methods are focused on this study.