The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

Cryptanalysis the SHA-256 Hash Function using Rainbow Tables

The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping.

Designing and implementing a fail-safe, real-time telecommunications network is complex. In modern networks, traditional quality of service (QoS) methods for monitoring and analyzing data have some problems, such as accuracy and efficient processing of big data in real time. To solve this problem, should use an appropriate intelligent crash classification system to detect and diagnose run-time errors. The article proposes to use a comprehensive fault detection system that includes QoS and machine learning technologies using information about the state of a point-to-point protocol over ethernet (PPPoE) session on PPPoE active discovery termination (PADT) virtual local area network (VLAN) routes. This intelligent system is built using the machine learning method and is independent of the main real-time system. Demonstrated the operation of seven machine learning algorithms and presented the results of training and fault detection. Based on the received information about the state of the PPPoE session, the PADT type allows you to control the behavior of the real-time system.

The application of SON algorithms for automating the processes of operating fourth-generation mobile networks based on the networks of operation, administration and management of OAM (Operation and Maintenance) is considered. The features of the tasks at the stages of self-optimization and self-configuration of the network for the various stages of 4G mobile network life cycle are shown. Criteria and approaches to assessing the effectiveness of solving problems by the SON network are proposed. The technical requirements are also formulated for SON algorithms. The experimentally achieved values of the selected performance exponents depending on the duration of the test cluster self-optimization time of the 4G network are shown.

The purpose of this study is to develop a secure biometric data protection system that integrates artificial intelligence and cryptographic techniques to enhance data privacy and integrity. The proposed system employs a convolutional neural network (CNN) to extract features from multimodal biometric inputs, including facial images, retinal scans, and fingerprints. A generative adversarial network (GAN) is then trained on these features to produce synthetic biometric representations that closely mimic real data. To ensure confidentiality and authenticity, the El-Gamal cryptosystem is used to encrypt both the features and the biometric images, while a digital signature mechanism based on the SHA-256 hash function secures the data against tampering. The CNN achieved a classification accuracy above 99.8%, with GAN training stabilizing at a discriminator loss of approximately 0.3 and a generator loss of around 4.0. The encryption and signature verification processes demonstrated consistent success, confirming the robustness of the pipeline. This research concludes that the integrated approach is effective in safeguarding biometric data against forgery and unauthorized access. Practically, it provides a viable solution for secure transmission and storage in biometric authentication systems, with strong potential for deployment in high-security environments.