Personal Mode Home Wi-Fi networks are an integral part of our daily lives, providing convenience and ease of access to the Internet. However, many people believe that modern encryption protocols such as Wi-Fi Protected Access3 (WPA3) provide sufficiently strong protection. This research aims to evaluate the effectiveness of encryption protocols used in home Wi-Fi networks, focusing on the currently most widely used Wi-Fi Protected Access2 (WPA2) protocol and the newer and more secure WPA3 protocol, and the effectiveness of the Protected Management Frames (PMF) against deauthentication attacks. A penetration test was conducted in a controlled, secure environment using a set of specialized tools such as Aircrack-ng, Fluxion, Bettercap, and Wireshark to assess the vulnerability of these networks to various attacks. The research results showed that home Wi-Fi networks using WPA2 protocol and WPA3 protocol (who support transitional mode) are vulnerable to hacking. deauthentication attacks and dictionary attacks were successful in hacking networks, especially when the passwords were weak or could be guessed. In addition, evil twin attacks using the captive portal approach have been proven effective in penetrating networks that use WPA2 and WPA3 (even when they do not support transitional mode) by exploiting weaknesses in user behavior. The results also show that deauthentication attacks are still effective before establishing a 4-way handshake. This paper proposes some countermeasures to reduce the risk of home network penetration.

The article discusses the solution to the issue of information systems architecture, which makes it possible to separate identification flows associated with the processes of managing the identification of digital entities and data flows associated with the processes of managing data describing the properties of digital entities. At the same time, a new architecture of connections between digital entities and services is proposed, which makes it possible to create a flexible system for processing the properties of identifiers that can be described in an irregular and unstructured form. In this case, the nomenclature of the parameters of the properties of a digital entity can be customized and expanded as a separate entity, which always maintaining a connection with the identifier. This allows, within the framework of the information system, on the one hand, to adapt any identification systems and ensure the solution of convergence requirements, to ensure compliance and solution of the requirements of recognition, immutability, stability in conditions of processing large volumes of data without implementing generally accepted principles of a global unique permanent identifier, and on the other hand, build flexible connections between digital entities and services.

Peer-to-peer (P2P) networks rely heavily on resources shared by peers in the network as a result of this mitigating free riding in the network is very crucial in a P2P system. In this work, we introduced a dynamic grace period allocation and a content scanning mechanism to a hybrid P2P architecture to mitigate free riding and prevent peers from uploading repeated and fake files within the network. The method introduced was simulated using Python programming language with peers selected at random to upload and download files representing a typical scenario of a P2P network. From the range of 0-500 and 600-2000, twenty different peers were selected at random the first scenario represents few peers and the second scenario represents many peers for analysis and experimentation purposes and also for the different percentages of free riders used for the experiment, this was chosen at random. Finally, we compared our method with a credit-based approach (CBA) that uses a common grace period assigned to peers in the network. Then, for the performance evaluation metric, we used the total uploads and downloads, contributing peers uploads and downloads, free rider peers uploads and downloads, and repeated and fake files detected gotten from the simulation result to evaluate the model and analyze the outcome of the experiments. Results from the simulation revealed that the Dynamic grace period approach (DGA) is 25-70% more effective than CBA in maintaining contributing peer activity and preventing the spread of repeated and fake files,while also achieving lower latency, higher throughput, and better quality of service (QoS) across diverse network conditions, particularly in high free-rider environments. 

To ensure robust signal recovery and efficient data transmission in wireless communication systems, accurate channel estimation plays a vital role, especially under dynamic and complex conditions. Machine learning-based channel estimation is explored for binary phase shift keying (BPSK) and quadrature phase shift keying  (QPSK) modulation schemes over Rayleigh, Rician, and Gaussian fading models. In this work, a framework using Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP) is developed to predict channel coefficients and analyze the impact on bit error rate (BER), throughput, and spectral efficiency for binary modulations. A comprehensive performance comparison of BPSK and QPSK under ML-based estimation across various fading conditions is provided. The results show that CNNs are effective in tracking time-varying coefficients, while MLPs often yield lower mean squared error (MSE). The study emphasizes practical applications in low-SNR environments and supports energy-efficient designs aligned with SDG goals. Key simulation results include BER vs SNR, throughput, and spectral efficiency comparisons between BPSK and QPSK under ML estimation.

The escalating complexity of cybersecurity threats necessitates advanced technological solutions to protect digital infrastructures. This study explores the application of Autoencoder neural networks, a deep learning model, for anomaly detection in network traffic, aiming to enhance real-time identification of cyberattacks. Using the CICIDS2017 dataset, which encompasses diverse attack types such as Distributed Denial of Service (DDoS) and infiltration, the Autoencoder was trained to detect deviations from normal traffic patterns based on reconstruction errors. The model was optimized through preprocessing, feature selection, and hyperparameter tuning, achieving strong performance metrics including precision, recall, F1-score, accuracy, and ROC-AUC. Despite its effectiveness in distinguishing normal and malicious traffic, challenges arose in detecting stealthy attacks like slow brute-force attempts. These results underscore the Autoencoder's potential in cybersecurity frameworks and highlight opportunities for improvement through adaptive thresholds and hybrid models. This study contributes to advancing AI-driven anomaly detection, promoting proactive defense against evolving cyber threats.