Recent advances in Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques have improved data processing in network management. However, existing RAG methods like VectorRAG and GraphRAG struggle with the complexity and implicit nature of semi-structured technical data, leading to inefficiencies in time, cost, and retrieval. This paper introduces FastRAG, a novel RAG approach for semi-structured data. FastRAG proposes chunk sampling, schema learning, and script learning to extract and structure data without submitting entire data sources to the LLM. It integrates text search with knowledge graph (KG) querying to improve accuracy. The evaluation results demonstrate that FastRAG provides accurate question answering while improving up to $90 \%$ in time and $85 \%$ in cost compared to GraphRAG.

Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). To address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval-Augmented Generation (RAG). By enriching design decisions with domain-specific optical standards and systematically incorporating symbolic reasoning and verification techniques, our pipeline generates explainable, verifiable, and trustworthy optical network designs. This approach significantly advances IBN by ensuring reliability and correctness, essential for mission-critical networking tasks.

As modern networks grow in complexity, ensuring their reliability and security becomes increasingly vital. Data plane analysis is a key process for verifying network behavior, but traditional data plane analysis tools face challenges in extensibility, customization, and interoperability. This paper explores the potential of implementing a data plane analysis system on a general-purpose native graph database, to create a more uniform and insightful approach to network verification and analysis. Using the graph database query language and built-in graph algorithms, we simplify the network forwarding analysis while enhancing its features. Our study offers practical insights into managing complex networks, promising more efficient and intuitive tools on the horizon.

This paper presents a network management AI assistant built with Large Language Models. It adapts at runtime to the network state and specific platform, leveraging techniques like prompt engineering, document retrieval, and Knowledge Graph integration. The AI assistant aims to simplify management tasks and is easily reproducible with available source code.

Network management relies on extensive monitoring of network state to analyse network behavior, design optimizations, plan upgrades, and conduct troubleshooting. Network monitoring collects various data from network devices through different protocols and interfaces such as NETCONF and Syslog, and from monitoring tools such as Zeek and Osquery. To unify and automate the monitoring workflow across the network, this paper identifies and discusses the data collection requirements for network management, reviews different monitoring approaches, and proposes an efficient data collection platform that addresses the requirements through an extensible and lightweight protocol. The platform design is demonstrated through an adaptive collection of data for network management based on digital twin technology.

This paper introduces a microservice-based architecture designed to enable automation of network programmability and management. Amid the complexity of today’s networks and the diversity of equipment, achieving efficient and reliable network programmability poses a significant challenge. Our architecture leverages the OpenConfig YANG models and the NETCONF protocol to simplify network configuration, automate tasks, and avoid errors. We demonstrate how our solution streamlines the collection and configuration workflows, enabling network operators to efficiently manage complex networks. The paper further presents a performance evaluation of the proposed design, which confirms its efficacy in handling complex configurations and its fault tolerance capabilities.

This paper builds upon our previous work on Network Security Traffic Analysis Platforms (NSTAP) [1], presenting an advanced framework for the real-time monitoring of network traffic and endpoint security in large-scale enterprises. We employ a fully integrated technology stack that includes Elastic Stack, ZEEK, Osquery, Kafka, and GeoLocation data to create a comprehensive security analytics solution. A significant contribution of this research is the integration of supervised machine learning models into our platform, trained specifically on the UNSW-NB15 dataset. We explored three supervised machine learning algorithms - Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM). For SVM, we also tested a dimensionality reduction algorithm to maximize model accuracy and optimized both computation time and performance. The evaluation is based on Accuracy, False Positive Rate (FPR) and revealed that the Random Forest Classifier, in conjunction with Pearson correlation-based feature selection methods, achieved the highest accuracy of 99.32% and an error rate of 0.67%.These findings not only substantiate the robustness of our unified platform but also set the stage for future research in developing scalable, efficient, and automated security solutions tailored for large enterprises.

The widespread decentralized applications and Blockchain components significantly boost the security frameworks in many vertical applications and use-cases including different secured payment methods and smart contracts. The integral part of any smart contract is the validation of the stake-holder identity, in general, while ideally being achieved without the third-party involvement. Recent industrial research works introduce the sovereign-identity system, where Blockchain becomes a decentralized component to establish a self-certified identity and to avoid a centralized trust third party. Hence, the classification of distributed transactions with respect to identity validation across several users becomes more challenging, especially because of the massive and sensitive identities that are issued through many users and IoT devices and that are used to validate transactions. In this context, it is important to identify and classify the malicious and non-malicious types of transactions. Our proposed method achieves the target of identity classifications from variety of transaction data. Since different users may have different device usage patterns, the data samples and labels located on any individual device may follow a different distribution, which cannot represent the global data distribution. Therefore, the solution could be bi-focal to compensate the gap. This paper coins the approach of hybridizing the consensus where as to initiate a machine learning mechanism to collect the local data globally through a permission driven and a federated approach. We introduce here a Federated Reinforcement learning to be improvised for distributed independent data as a policy of consortium while binding the proof of consensus more centrally authenticated.

Enabling Named Data Networking (NDN) in realworld Internet of Things (IoT) deployments becomes essential to benefit from Information Centric Networking (ICN) features in current IoT systems. To design realistic NDN-based communication solutions for IoT, revisiting mainstream technologies such as low-power wireless standards may be the key. In this paper, we explore the NDN forwarding over IEEE 802.15.4 by modeling a broadcast-based forwarding strategy. Based on the observations, we adapt the Carrier-Sense Multiple Access (CSMA) algorithm of 802.15.4 to improve NDN wireless forwarding while reducing broadcast effects in terms of packet redundancy, round-trip time and energy consumption.