In the current context in which user authentication is the first line of defense against emerging attacks and can be considered a defining element of any security infrastructure, the need to adopt alternative, non-invasive, contactless, and scalable authentication mechanisms is mandatory. This paper presents initial research on the design, implementation, and evaluation of a multi-factor authentication mechanism that combines facial recognition with a fully homomorphic encryption algorithm. The goal is to minimize the risk of unauthorized access and uphold user confidentiality and integrity. The proposed device is implemented on the latest version of the Raspberry Pi and Arduino ESP 32 modules, which are wirelessly connected to the computer system. Additionally, a comprehensive evaluation, utilizing various statistical parameters, demonstrates the performance, the limitations of the encryption algorithms proposed to secure the biometric database, and also the security implications over the system resources. The research results illustrate that the Brakerski–Gentry–Vaikuntanathan algorithm can achieve higher performance and efficiency when compared to the Brakerski–Fan–Vercauteren algorithm, and proved to be the best alternative for the designed mechanism because it effectively enhances the level of security in computer systems, showing promise for deployment and seamless integration into real-world scenarios of network architectures.

This paper provides preliminary research regarding the implementation and evaluation of a hybrid mechanism of authentication based on fingerprint recognition interconnected with RFID technology, using Arduino modules, that can be deployed in different scenarios, including secret classified networks. To improve security, increase efficiency, and enhance convenience in the process of authentication, we perform a comparative assessment between two homomorphic encryption algorithms, the Paillier partial homomorphic algorithm and the Brakerski–Gentry–Vaikuntanathan fully homomorphic encryption scheme, applied to biometric templates extracted from the device mentioned above, by analyzing factors such as a histogram analysis, mean squared error (MSE), peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the number of pixel change rate (NPCR), the unified average changing intensity (UACI), the correlation coefficient, and average encryption time and dimension. From security and privacy perspectives, the present findings suggest that the designed mechanism represents a reliable and low-cost authentication alternative that can facilitate secure access to computer systems and networks and minimize the risk of unauthorized access.

In this paper, we present a comprehensive taxonomy of the most popular authentication mechanisms, the main threats, vulnerabilities, the possible types of attacks and the security issues that are associated with computer systems. Our main contribution is to analyze the architecture of the current security environment from all aspects and to make a comparative assessment of different authentication methods in order to identify and develop a scalable and reliable mechanism that must deal with multi-level security and strong authentication requirements. We propose hybrid methods of authentication, which combine and integrate biometric technologies with cryptographic asymmetric algorithms, elements that play a vital role in the field of information security and aim to resolve the shortcomings of traditional methods of authentication and enhance the level of security.

In the contemporary digital landscape, AI (Artificial Intelligence) emerged as a pivotal tool in enhancing the defense technologies developed across the entire network infrastructure. As reliance on AI-based decision-making grew, so did the imperative need for interpretability, transparency, and trustworthiness, leading to the development and integration of XAI (eXplainable Artificial Intelligence). This research paper provides a comprehensive overview of the current state of the art in XAI approaches that can be effectively implemented for network traffic monitoring, especially in critical digital infrastructures. The main contribution of this research article consists of the comparative analysis of the XAI SHAP (Shapley Additive Explanation) method applied to different datasets obtained from real-time network traffic monitoring, utilizing several representative parameters, which demonstrates the performance, vulnerabilities, and limitations of the proposed method, and also the security implications of the system resources from a cybersecurity perspective. Experimental results show that Ethernet networks offer higher predictability and clearer decision boundaries. Consequently, they are a safer solution for deployment in sensitive network architectures. In contrast, BYOD (Bring Your Own Device) Wi-Fi environments exhibit greater randomness.

A modular AI-driven intrusion prevention framework integrating a hybrid machine-learning decision engine, explainable artificial intelligence, and kernel-level enforcement is presented and experimentally validated. The solution decouples attack-specific event detection from decision-making and mitigation, allowing heterogeneous detectors to operate within a shared prevention workflow. Mature detection mechanisms are preserved where appropriate, while a hybrid Random Forest (RF) and Long Short-Term Memory (LSTM) engine analyzes standardized flow-level representations to support behavior-aware blocking decisions. SHAP-based explainability provides feature-level interpretation of AI decisions, whereas extended Berkeley Packet Filter/eXpress Data Path (eBPF/XDP) enables packet filtering at the kernel level. Experimental validation was performed through repeated executions of benign traffic, SSH brute-force attacks, slow SSH brute-force attacks, and port-scanning activity. The results show successful mitigation of the evaluated attack scenarios without false positives during benign operation. Compared with a conventional Fail2Ban deployment, the proposed framework introduces additional computational overhead due to feature extraction, machine-learning inference, explainability, and orchestration services. This overhead represents the cost of a reusable prevention architecture that combines conventional detection, hybrid AI inference, interpretable decision support, and kernel-level enforcement. Overall, the study demonstrates the feasibility of integrating heterogeneous detection mechanisms into a unified AI-driven intrusion prevention pipeline and offers a basis for future extension toward broader classes of cyberattacks.