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.

Dense Wavelength Division Multiplexing (DWDM) networks are pivotal in modern optical communications, facilitating high data capacity by transmitting multiple wavelengths along a single optical fiber. Ensuring optimal network performance and reliability requires close monitoring of crucial signal characteristics such as signal-to-noise ratio (SNR), chromatic dispersion, nonlinear effects, and modulation types. However, traditional monitoring approaches often demand sophisticated and costly equipment, limiting their scalability. This paper proposes an innovative machine learning (ML)-based solution for enhanced signal monitoring and anomaly detection within DWDM networks. Utilizing K-means clustering for data categorization and feature extraction, alongside Support Vector Machine (SVM) for effective signal classification and anomaly detection, this approach provides scalable real-time monitoring while minimizing dependency on complex hardware. Additionally, this ML-driven framework allows for proactive network management, offering dynamic adaptability to network fluctuations. Our study emphasizes the promise of machine learning in optical network monitoring, paving the way for intelligent, self-optimizing systems that can support evolving communication demands and maintain superior service quality.