Large Language Models for Networking: Applications, Enabling Techniques, and Challenges

The rapid evolution of network technologies and the growing complexity of network tasks necessitate a paradigm shift in how networks are designed, configured, and managed. With a wealth of knowledge and expertise, large language models (LLMs) are one of the most promising candidates. This paper aims to pave the way for constructing domain-adapted LLMs for networking. Firstly, we present potential LLM applications for vertical network fields and showcase the mapping from natural language to network language. Then, several enabling technologies are investigated, including parameter-efficient finetuning and prompt engineering. The insight is that language understanding and tool usage are both required for network LLMs. Driven by the idea of embodied intelligence, we propose the ChatNet, a domain-adapted network LLM framework with access to various external network tools. ChatNet can reduce the time required for burdensome network planning tasks significantly, leading to a substantial improvement in efficiency. Finally, key challenges and future research directions are highlighted.Comment: 7 pages, 3 figures, 2 table

SecBot: a Business-Driven Conversational Agent for Cybersecurity Planning and Management

Businesses were moving during the past decades to-ward full digital models, which made companies face new threatsand cyberattacks affecting their services and, consequently, theirprofits. To avoid negative impacts, companies’ investments incybersecurity are increasing considerably. However, Small andMedium-sized Enterprises (SMEs) operate on small budgets,minimal technical expertise, and few personnel to address cy-bersecurity threats. In order to address such challenges, it isessential to promote novel approaches that can intuitively presentcybersecurity-related technical information.This paper introduces SecBot, a cybersecurity-driven conver-sational agent (i.e., chatbot) for the support of cybersecurityplanning and management. SecBot applies concepts of neuralnetworks and Natural Language Processing (NLP), to interactand extract information from a conversation. SecBot can(a)identify cyberattacks based on related symptoms,(b)indicatesolutions and configurations according to business demands,and(c)provide insightful information for the decision on cy-bersecurity investments and risks. A formal description hadbeen developed to describe states, transitions, a language, anda Proof-of-Concept (PoC) implementation. A case study and aperformance evaluation were conducted to provide evidence ofthe proposed solution’s feasibility and accurac

Intent-based network slicing for SDN vertical services with assurance: Context, design and preliminary experiments

Network slicing is announced to be one of the key features for 5G infrastructures enabling network operators to provide network services with the flexibility and dynamicity necessary for the vertical services, while relying on Network Function Virtualization (NFV) and Software-defined Networking (SDN). On the other hand, vertical industries are attracted by flexibility and customization offered by operators through network slicing, especially if slices come with in-built SDN capabilities to programmatically connect their application components and if they are relieved of dealing with detailed technicalities of the underlying (virtual) infrastructure. In this paper, we present an Intent-based deployment of a NFV orchestration stack that allows for the setup of Qos-aware and SDN-enabled network slices toward effective service chaining in the vertical domain. The main aim of the work is to simplify and automate the deployment of tenant-managed SDN-enabled network slices through a declarative approach while abstracting the underlying implementation details and unburdening verticals to deal with technology-specific low-level networking directives. In our approach, the intent-based framework we propose is based on an ETSI NFV MANO platform and is assessed through a set of experimental results demonstrating its feasibility and effectiveness

A Comparative Analysis of NLP Algorithms for Implementing AI Conversational Assistants

The rapid adoption of low-code/no-code software systems has reshaped the landscape of software development, but it also brings challenges in usability and accessibility, particularly for those unfamiliar with the specific components and templates of these platforms. This thesis targets improving the developer experience in Nokia Corporation's low-code/no-code software system for network management through the incorporation of Natural Language Interfaces (NLIs) using Natural Language Processing (NLP) algorithms. Focused on key NLP tasks like entity extraction and intent classification, we analyzed a variety of algorithms, including MaxEnt Classifier with NLTK, Spacy, Conditional Random Fields with Stanford NER for entity recognition, and SVM Classifier, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and RASA DIET for intent classification. Each algorithm's performance was rigorously evaluated using a dataset generated from network-related utterances. The evaluation metrics included not only performance metrics but also system metrics. Our research uncovers significant trade-offs in algorithmic selection, elucidating the balance between computational cost and predictive accuracy. It reveals that while some models, like RASA DIET, excel in accuracy, they require extensive computational resources, making them less suitable for lightweight systems. In contrast, simpler models like Spacy and StanfordNER provide a balanced performance but require careful consideration for specific entity types. While the study is limited by dataset size and focuses on simpler algorithms, it offers an empirically grounded framework for practitioners and decision-makers at Nokia and similar corporations. The findings point towards future research directions, including the exploration of ensemble methods, the fine-tuning of existing models, and the real-world implementation and scalability of these algorithms in low-code/no-code platforms

IMPROVING NETWORK POLICY ENFORCEMENT USING NATURAL LANGUAGE PROCESSING AND PROGRAMMABLE NETWORKS

Computer networks are becoming more complex and challenging to operate, manage, and protect. As a result, Network policies that define how network operators should manage the network are becoming more complex and nuanced. Unfortunately, network policies are often an undervalued part of network design, leaving network operators to guess at the intent of policies that are written and fill in the gaps where policies don’t exist. Organizations typically designate Policy Committees to write down the network policies in the policy documents using high-level natural languages. The policy documents describe both the acceptable and unacceptable uses of the network. Network operators then take the responsibility of enforcing the policies and verifying whether the enforcement achieves expected requirements. Network operators often encounter gaps and ambiguous statements when translating network policies into specific network configurations. An ill-structured network policy document may prevent network operators from implementing the true intent of the policies, and thus leads to incorrect enforcement. It is thus important to know the quality of the written network policies and to remove any ambiguity that may confuse the people who are responsible for reading and implementing them. Moreover, there is a need not only to prevent policy violations from occurring but also to check for any policy violations that may have occurred (i.e., the prevention mechanisms failed in some way), since unwanted packets or network traffic, were somehow allowed to enter the network. In addition, the emergence of programmable networks provides flexible network control. Enforcing network routing policies in an environment that contains both the traditional networks and programmable networks also becomes a challenge. This dissertation presents a set of methods designed to improve network policy enforcement. We begin by describing the design and implementation of a new Network Policy Analyzer (NPA), which analyzes the written quality of network policies and outputs a quality report that can be given to Policy Committees to improve their policies. Suggestions on how to write good network policies are also provided. We also present Network Policy Conversation Engine (NPCE), a chatbot for network operators to ask questions in natural languages that check whether there is any policy violation in the network. NPCE takes advantage of recent advances in Natural Language Processing (NLP) and modern database solutions to convert natural language questions into the corresponding database queries. Next, we discuss our work towards understanding how Internet ASes connect with each other at third-party locations such as IXPs and their business relationships. Such a graph is needed to write routing policies and to calculate available routes in the future. Lastly, we present how we successfully manage network policies in a hybrid network composed of both SDN and legacy devices, making network services available over the entire network

SLA Management in Intent-Driven Service Management Systems: A Taxonomy and Future Directions

Traditionally, network and system administrators are responsible for designing, configuring, and resolving the Internet service requests. Human-driven system configuration and management are proving unsatisfactory due to the recent interest in time-sensitive applications with stringent quality of service (QoS). Aiming to transition from the traditional human-driven to zero-touch service management in the field of networks and computing, intent-driven service management (IDSM) has been proposed as a response to stringent quality of service requirements. In IDSM, users express their service requirements in a declarative manner as intents. IDSM, with the help of closed control-loop operations, perform configurations and deployments, autonomously to meet service request requirements. The result is a faster deployment of Internet services and reduction in configuration errors caused by manual operations, which in turn reduces the service-level agreement (SLA) violations. In the early stages of development, IDSM systems require attention from industry as well as academia. In an attempt to fill the gaps in current research, we conducted a systematic literature review of SLA management in IDSM systems. As an outcome, we have identified four IDSM intent management activities and proposed a taxonomy for each activity. Analysis of all studies and future research directions, are presented in the conclusions.Comment: Extended version of the preprint submitted at ACM Computing Surveys (CSUR

A Survey on Popularity Bias in Recommender Systems

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.Comment: Under review, submitted to UMUA

Network Security Automation

L'abstract è presente nell'allegato / the abstract is in the attachmen

Decoding Digital Culture with Science Fiction: Hyper-Modernism, Hyperreality, and Posthumanism

How do digital media technologies affect society and our lives? Through the cultural theory hypotheses of hyper-modernism, hyperreality, and posthumanism, Alan N. Shapiro investigates the social impact of Virtual/Augmented Reality, AI, social media platforms, robots, and the Brain-Computer Interface. His examination of concepts of Jean Baudrillard and Katherine Hayles, as well as films such as Blade Runner 2049, Ghost in the Shell, Ex Machina, and the TV series Black Mirror, suggests that the boundary between science fiction narratives and the »real world« has become indistinct. Science-fictional thinking should be advanced as a principal mode of knowledge for grasping the world and digitalization