Future components to enhance the basic, native security of 5G networks are either complex mechanisms whose impact in the requiring 5G communications are not considered, or lightweight solutions adapted to ultra-reliable low-latency communications (URLLC) but whose security properties remain under discussion. Although different 5G network slices may have different requirements, in general, both visions seem to fall short at provisioning secure URLLC in the future. In this work we address this challenge, by introducing cost-security functions as a method to evaluate the performance and adequacy of most developed and employed non-native enhanced security mechanisms in 5G networks. We categorize those new security components into different groups according to their purpose and deployment scope. We propose to analyze them in the context of existing 5G architectures using two different approaches. First, using model checking techniques, we will evaluate the probability of an attacker to be successful against each security solution. Second, using analytical models, we will analyze the impact of these security mechanisms in terms of delay, throughput consumption, and reliability. Finally, we will combine both approaches using stochastic cost-security functions and the PRISM model checker to create a global picture. Our results are first evidence of how a 5G network that covers and strengthened all security areas through enhanced, dedicated non-native mechanisms could only guarantee secure URLLC with a probability of ∼55%.
5G communication networks [ Enhanced Mobile Broadband Communications (eMBBC) [ Ultra-reliable Low-Latency Communications (URLLC) [ Massive machine-type communications (mMTC) [
These requirements are mandatory for all network configurations and implementations (standalone or non-standalone). Briefly, in the transition from 4G to 5G networks, all network segments and components are aimed to move towards service-based open architectures, including security services, being able to cooperate and interoperate together. This change affects core networks in a more relevant way, as more changes are needed from monolithic closed 4G cores. Regarding Radio Access Networks (RAN), some 4G implementations have already achieved certain decentralization and distribution level, but 5G solutions must still improve the current openness of 4G RAN.
Although in previous mobile networks (mainly 3G and 4G) security solutions cover a wide catalogue of aspects and potential attacks, their main focus are authentication and integrity solutions. This analysis is still critical and valid in future 5G networks [
Given the currently proposed 5G open architectures and physical technologies (very close-fitting, even for 4G+ services and QoS requirements) and the current state of non-native components to provide enhanced protection to 5G services and users (either very heavy and complex, or not fully secure),
An initial step to address this problem is to define a joint evaluation framework, in which the advantages (protections level) and disadvantages (impact on QoS) of security solutions for 5G networks are analyzed in the proper context, i.e., the future network architectures. Using this combined analysis, we can define boundaries to the opportunity-cost balance and clearly show which proposed security solutions are valid, which design principles are adequate, and which solutions must be discarded either because of their poor security performance or because of their unaffordable computational cost. Therefore, in this paper we analyze the performance of proposed security mechanisms for 5G networks. We propose a stochastic cost-security function, which combines both the reached protection level and the impact in the provided QoS.
We analyzed the impact security mechanisms have on service quality through relevant key performance indicators (KPI), defined in a probabilistic manner (with a special focus on those parameters that affect the key features of 5G services such as URLLC). We deducted this impact numerically, using analytical models that describe the behavior of relevant variables such as the end-to-end delay and jitter. These models are built from basic general communication schemes, that are increasingly enriched when we analyze the internal structure of future 5G networks and the characteristics of future application scenarios. We define and study different models, considering the most common and promising 5G implementations, and the most relevant scientific proposals [
The contributions of this paper are the following:
Analytical models for most developed open, service-based 5G architectures (in terms of reported experiments and experiences with those architectures), including core and RAN solutions. Analytical models for most developed non-native protection components for 5G networks and enhanced secure communications. A cost-security function that combines both 5G architecture and security solution models to determine the feasibility of URLLC service provisioning in the presence of security mechanism. A numerical analysis using the PRISM model checker that shows in general, a 5G network deploying all security mechanisms can only guarantee URLLC with a probability of ∼55%.
The proposed models are based on open service-architectures available by the writing of this paper. Future versions and 5G specification may require additional adjustments to the models. The global and final goal of this analytical model is to provide a systematic and quantitative approach to evaluate the feasibility of fully secure 5G URLLC, considering different network architectures and the most relevant reported non-native security components nowadays. Through this model, any potential stakeholder may evaluate and compare their own security architecture with the state of the art using quantitative metrics.
The provided analysis aims to answer three basic research questions:
REQ#1: Can secure URLLC be guaranteed by the best existing technologies for 5G networks? REQ#2: What is the success probability of secure URLLC in 5G core networks in the current technological state? REQ#3: What is the success probability of secure URLLC if 5G endpoints implement current security mechanisms?
The rest of the paper is organized as follows: Section 2 describes the state of the art on 5G security analysis and benchmarking. Section 3 describes the main study, from the 5G architectural description and model creation to the analysis and modeling of different security mechanisms for 5G networks and their evaluation through model checking techniques. Section 4 shows numerical results for the selected scenarios. Finally, Section 5 presents our conclusions.
Although different studies have already addressed the key security-relevant aspects of future and current 5G deployments [
In general, works on 5G networks and secure URLLC are exploratory and qualitative. Ahmad et al. [
A different approach is to consider lightweight solutions that, potentially, may be compatible with URLLC [
On the other hand, although this paper is focused on 5G scenarios, security models are also applicable to previous mobile generations such as 4G LTE (Long-Term Evolution). Specifically, different models for native security protocols in 4G mobile networks have been proposed [
In this paper we address this gap in the existing literature by proposing a new methodology based on cost-security functions to study the feasibility of URLLC in fully secure 5G networks (including, specifically, non-native security components).
In this section, we propose and describe the analytical models, functions, and methodology of this study. Section 3.1 describes the most relevant 5G core and RAN implementations and, for each one, it proposes an analytic model describing its operation. Section 3.2 presents the current security solutions reported for future 5G networks and the most relevant related attacks. Section 3.3 describes the formal security analysis methodology, based on model checking. Finally, Section 3.4 proposes the global cost-security functions to evaluate the viability of secure URLLC.
In order to evaluate the feasibility of URLLC in 5G networks in the presence of non-native security components (what we have called, fully secure URLLC), we must consider components in the context of a particular network architecture. In fact, the internal structure of networks and the required interactions among network modules highly condition the global performance (delay, jitter, etc.) and then, the feasibility of URLLC.
Modern mobile networks, including future 5G solutions, are separated into two different network domains: the radio access network (RAN), focused on providing local wireless connectivity to mobile users and the core network, managing global connectivity. The 3GPP Release 15 [
In this work we consider the three basic indicators that characterize URLLC: end-to-end delay (latency) D, jitter J, and reliability R (understood as the probability of a data packet to be successfully transmitted and delivered). Global indicators, then, may be easily obtained from measurements associated to core network and RAN through the probability theory
Parameters
The 5G Core network is expected to be an IP backbone over optical transport. Two types of delays manifest in core network links: the transmission delay
Given a packet
Furthermore, the transmitter and the receiver in a link, may manage packets from different services and users, so the packet
To calculate the delays associated to queues we use a Poisson model M/M/1/N (in Kendall notation), assuming that packet generation
The propagation delay in an optical fiber link depends on the length of the link
Previous delays are totally dependent on the module
Specific values for every variable (
The delay associated to RAN may be deducted in a very similar way. However, in this case we must consider a remote wireless user equipment and a base station with a C-RAN (Cloud-RAN) structure instead of the IP backbone and optical transport. It is important to note that, during handovers, although the core network does not participate in the process, the delay is modeled as a core network delay because remote base stations communicate with each other through an IP backbone.
The software modules participating in the communication process are divided into two groups: modules in the base station
As said before, in this model, all random variables without a specific distribution, are considered Gaussian, because of the central limit theorem.
Many different parameters in the proposed delay model are still unknown and will be evaluated later through model checking techniques to calculate a final probability distribution function for the end-to-end delay. In this process, the same model will be employed to evaluate the probability distribution of jitter, which is understood as the maximum expected delay fluctuation in normal conditions. The statistical analysis of the previously proposed model for the latency may also generate that result, if the standard deviation of partial probability distributions is calculated and combined (see Section 3.3 and Section 3.4). No additional model for jitter is then required.
Regarding network reliability, in this paper we focus on permanent effects (mainly the physical noise), instead of transient phenomena (such as electrical malfunctions). Basically, two main causes affect the network reliability in our model: congestion and physical noise. Congestion fills the queues of network elements rapidly, causing packets to be discarded as the system is blocked. In a M/M/1/N system, this blocking probability is calculated through the flow equilibrium
Physical noise is considered to follow a uniform distribution in the frequency domain, whose power
In the core network, all communications among routers in the IP backbone and/or among software modules belonging to the mobile network are independent events, and then, a global error probability
In RAN, congestion situations are usually caused by congestion in the base station not in the user devices (that only manage one communication link and serve only one user). Thus, modules where congestion may appear are those embedded into the 5G base stations. However, physical noise affects all devices equally. Then, the global error probability
Some of the previously presented parameters, such as physical noise, packet length, number of routers that are crossed by a packet, and optical fiber characteristics are contextual. As we perform evaluations using model checking techniques, we will consider existing information about the technological state of the art for the values of these parameters. On the other hand, parameters such as number of involved network modules, processing delays, and capacity of queues depend on the selected 5G network implementation. Many different proposals for core network and RAN scopes have been reported in the last years [
ONAP [
Regarding RAN implementations, ORAN (Open RAN) [
In our work, we assume all network implements follow the recommended default configuration. Moreover, while Aether is a monolithic architecture (so transmissions delays tend to be lower, although reliability tend to decrease); the other 5G implementations are distributed and many redundancies are considered (delays and jitter are higher, but reliability greatly improves). Finally, regarding hardware requirements, all the considered implementations have similar needs. Besides, delays introduced by hardware devices are exogenous variables and are independent from the structural feasibility analysis we are developing in this work. Future work will address this variable, but this initial study does not.
All described 5G implementations are open and then, they can be modified and enriched according to the users’ or service providers’ needs. Specifically, all of them allow the implementation of non-native security components to improve the global security level in URLLC. So, we can study network architectures and non-native security component independently.
Considering these different implementations and the way in which they operate (which network components they deploy, how they interact when stablishing a connection, etc.) we can define specific values, or a probability distribution, for variables in the previous models for delay, jitter, and reliability. During the experimental setup, the behavior of every network implementation (RAN or core) will be represented using a Markov model. Configurable network settings will be the variables of these models. The experiments will be based on this simulation framework (all possible scenarios will be considered thanks to a Monte Carlo approach) and no real network deployment will be required (see Section 3.3 and Section 4).
Model | Parameter | Description | Values | Type |
---|---|---|---|---|
Delay and jitter (core and RAN) |
Standard deviation (packet length) |
30–100 bytes |
Contextual | |
Delay and jitter (core and RAN) |
Medium value (packet length) |
50–1000 bytes (typically 550 bytes) |
Contextual | |
Delay and jitter (core) | Expected average rate (packet generation) | 10–120 packet per second and device | Contextual | |
Delay and jitter (core) | Reference time period | 1 s–1 day (typically 1 h) | Contextual | |
Delay and jitter (core) |
Queue size | 32–640 packets (typically 60) | Implementation | |
Delay and jitter (core) | Medium link physical length | 0.5–5 km | Contextual | |
Delay and jitter (core) | Delay per kilometer ratio | Contextual | ||
Delay and jitter (core) |
Number of physical links | 5–40 (typically 15) | Contextual | |
Delay and jitter (core) |
Network modules | 10–25 | Implementation | |
Delay and jitter (RAN) |
Modules in the base station | 3–10 | Implementation | |
Delay and jitter (RAN) |
Modules in the user device | 3–10 | Implementation | |
Reliability (core and RAN) | Packet generation rate | 150–250 packet per second and device | Contextual | |
Reliability (core and RAN) | Detection and correction capabilities of cyclic codes | 8–64 bits | Implementation | |
Reliability (core and RAN) | Energy per bit | −1 to −2 dBm | Contextual | |
Reliability (core and RAN) | Noise power | −30 to −10 dBm | Contextual |
Cyber protection mechanisms can be divided into three different categories depending on the final objective: privacy modules, trust provision modules, and security modules [
In general, 5G specifications include protocols and solutions to address most relevant challenges in all these areas, but for many application scenarios these native components are not enough [
Privacy technologies typically include anonymization and encryption solutions. Anonymization (and Pseudonymization) technologies process personal data so private information can no longer be attributed to a specific subject without a secret key. Most future commercial anonymization solutions for 5G are device-based (or SIM-based) and deployed in the endpoint. In this paper we consider the two most developed security proposals: Thales’ 5G SIM (Subscriber Identity Module) [
The second group of privacy preserving technologies are cryptographic solutions. Almost every general-purpose cryptographic technology may be eventually applied to future 5G networks. However, in this paper, we focus on algorithms designed specifically for 5G networks or explicitly accepted by 3GPP to be used in that context. All these accepted algorithms are described in 3GPP technical reports TR33.841 [
Security components are, probably, the most developed non-native protection services in 5G networks nowadays. They include authentication services and integrity assurance solutions. Technical report T33.899 [
Regarding integrity assurance mechanisms, technical report T33.501 also describes potential solutions for different contexts. In this paper, two basic technologies are analyzed: Non-access stratum (NAS) integrity algorithm, and Radio Resource Control (RRC) integrity mechanism and User Plane (UP) Packet Data Convergence Protocol (PDCP) [
Trust provision technologies are also envisioned to be integrated into 5G networks. Trust provision mechanisms include intrusion detection technologies and reputation management solutions. Two basic solutions for intrusion detection are considered in this paper: Sec5G IDPS (Intrusion detection and prevention system) [
Apart from this objective-based classification, cyber protection solutions are usually classified according to the deployment scope. Three different deployment scopes can be defined in the context of 5G networks: core network, end-point devices, and management plane. The core network includes all modules and agents in charge of routing user traffic and providing services. End-point devices refer to all final and user equipment or infrastructure communicating through the core network. Finally, some components are not integrated within the user plane, but they are deployed in the management plane. These components are especially important in future 5G networks, where cloud solutions, remote dashboard, and Software Defined Networks (SDN) are envisioned to be exhaustively employed.
5G protection solutions | Adversary or attacker model | Deployment scope | ||||
---|---|---|---|---|---|---|
Core | Endpoint | Management plane | ||||
Category | Privacy | Anonymization | Analyst attacker [ |
5G Ensure Enhanced Identity protection |
Thales’ 5G SIM |
-- |
Encryption | Single-key attack [ |
Key creation algorithms | Radio interface encryption |
Key Agreement protocols |
||
Security | Authentication | Spoof attacks [ |
Technical reports T33.501 and TR33.835 | Technical reports T33.501 and TR33.835 |
Technical reports T33.501 and TR33.835 | |
Integrity | Standard data integrity threat [ |
Non-access stratum integrity algorithm |
Non-access stratum integrity algorithm |
-- | ||
Trust | Intrusion detection | Sinkhole attack [ |
Sec5G IDPS |
-- | -- | |
Reputation | Self-promoting and slandering attackers [ |
SenderBase Reputation Service | -- | -- |
Model checking techniques focus on evaluating the probability of a system (described through a specification) to fulfill a certain property (described as a logic rule). Rules and specification may represent any possible scenario, situation, or stochastic process that must be evaluated. Specifically, if models represent a scenario where an adversary performs a known attack, and the property under study describes the success of that attack, the model checker generates a numerical function describing the probability of the attack to be successful. On the other hand, if the specification describes analytical models of a network, and the property under study represents the expected performance of the network, the model checker calculates the probability of a successful service provision with the expected Quality of Service. In this paper we use model checking techniques to evaluate the viability of secure URLLC for a given network configuration (see Section 3.1) and cyber protection solutions (see Section 3.2). To do that, models, tools, and expected QoS from 5G are set as described in the previous sections. For this analysis we used PRISM software [
PRISM is a probabilistic model checker that enables the analysis of systems with a random behavior. It accepts several probabilistic models such as probabilistic automata and Markov decision processes. To match with the proposed probabilistic queue model based on Markov models and Poisson distributions, we model 5G networks as continuous-time Markov chains in this study. Models (specifications) must be described in PRISM language (a state-based language), while rules (properties) can be written using several languages (such as Linear Temporal Logic Rules or Continuous-time Stochastic Logic rules). In this study we use probabilistic CTL (continuous time logic) rules.
We created the specifications by combining the previously described analytical models with the attacker models and the models describing the behavior of each one of the described cyber protection solutions. On the other hand, we prepared properties to analyze the success of cyber-attacks and the probability of delay, jitter, and reliability to show the expected values of 5G URLLC.
We performed our analysis in a Linux-based machine (Ubuntu 18.04 LTS) with the following hardware characteristics: Dell R540 server with 96 GB of RAM, two processors Intel Xeon Silver 4114 2.2G, and a HD 2TB SATA 7, 2K rpm hard drive. As results may be affected by hardware phenomena and numerical errors, each analysis was repeated 12 times. The final results employed for further analyses were calculated as the mean value of all partial calculations.
With this simulation strategy, we guarantee the experimental error is below 10% (error is one magnitude order lower than the results). According to the error theory, when final results are calculated as the average value of a set of samples, the relative error is inversely proportional to the number of samples
PRISM model checker generates a probability density function for each rule introduced in the system. Besides, the probability of all (or a subset of) rules being met can also be calculated. However, deeper analyses are required for our study. The basic challenge we face is to analyze the risk or security reduction we are willing to accept in order to ensure the performance of URLLC or, equally, what kind of balances between both important issues are feasible and may be supported at long-term. To do that, results related to network performance and URLLC viability must be combined using the adequate cost functions.
From PRISM model checker we obtain a set of
For each of these probabilities, we define a threshold
Considering the analytical models (see Section 3.1) and the requirements of URLLC in 5G networks (see Section 1), we define three additional relevant probabilities describing the viability of URLLC [ The probability The probability The probability
We propose a cost function to calculate a global evaluation of all these factors together. In its general form, the cost function
Many different functions may be defined to meet the previously described conditions. As the complexity of attacks and cyber risks to be represented increases, the mathematical complexity of the cost function also increases. In this first work, in order to build the stochastic function, different qualitative facts are taken into account. Namely:
Networks’ performance and user traffic follow exponential laws. In general, above a certain threshold, the expected behavior from networks gets worse very fast, typically exponentially. In most standard services, QoS is more affected by high jitter values than by high, and stable, delay values. In general, users and applications can tolerate a delay, but relevant fluctuations in time are difficult to manage. Equally, reliability is a more relevant component of 5G QoS than latency. Packet losses may prevent the communication services, while high delay may be managed although the QoS decreases. All attacks are, in general, independent, and therefore their success probabilities are also totally independent. Although, in some cases, attacks are interconnected and cross-layer, and may be directed against several network modules at the same time; in this first work we are addressing the basic attacker models (see The value of a vulnerable network configuration is negligible. Thus, the global value must be null if any success probability
With all previous consideration we propose a cost function
The proposed cost function may be understood as a measure of the success rate of secure URLLC, for a given network configuration. While calculating the different probabilities that compose this model, as they are random phenomena, we have some uncertainties to quantify. This is called the significance level. Specifically, the significance level is understood as the probability of the proposed cost function to consider URLLC feasible when they are not. This value may be also understood as a measure of how precise our calculations are.
Considering this cost function, the methodology for security analyses based on PRISM model checker (Section 3.3), and the analytical models (Section 3.1), we perform a global evaluation of security mechanisms in the context of different network configuration. To perform this final and global analysis, we introduced partial data in MATLAB 2018b software for processing and visualization, running on an Ubuntu 18.04 LTS system.
As many variables may affect the results of this study, we consider the following simplifications: first, we fix all significance levels
Parameter | Value |
---|---|
0.15 | |
0.1 | |
0.05 |
At this point, we must remember the proposed cost function may be understood as a measure of the success rate of secure URLLC, for a given network configuration. And the significance level may be understood as a measure of how precise our calculations are (as lower it is, the global error is lower). Specifically, the global significance level
Computing the cost of all considered network architectures (see Section 3.1), when no protection mechanisms are implemented or analyzed, the architecture based on ONAP and ORAN technologies is the one showing the best performance, supporting URLLC in a successful manner in 98% of cases. On the contrary, SD-RAN technology is always associated to a lower performance, and only 89% of cases URLLC is successfully supported. In further analyses, these percentages are the reference to conclude whether cyber protection has a relevant impact or not in URLLC.
For the rest of this study, we focus on the architecture that showed the best performance (i.e., ONAP as core implementation and ORAN as RAN technology).
The general behavior, in this case, is similar to the one observed in core security mechanisms. In this case, as the number of elements and modules communicating is lower (only the user device and the base station is considered), the impact of security solution is less relevant. Privacy mechanisms for end devices are embedded into the SIM card or the mobile operating system, so they do not affect the network performance. Only small variations caused by the different significance level are observed in privacy instruments. On the contrary, security (and particularly authentication) solutions introduce a relevant jitter and delay that highly affect the URLLC feasibility and provision as introduce traffic in the network. In particular, a scheme in which endpoint cyber protection solutions are deployed only guarantees a success probability of 65% (approximately) for URLLC (KEY FINDING#2). Similar to core technology cyber protections, in this case it is clear that URLLC is also highly affected. With this finding, we can also answer the third research question (REQ#3). And we can conclude URLLC are not feasible if 5G endpoints implement currently existing security mechanisms.
We conduct the same analysis for management plane solutions (see
Globally, a fully secure 5G network (based on ONAP and ORAN) can only guarantee URLLC with a probability of 55% (approximately). As a conclusion, secure URLLC cannot be guaranteed using the best current solutions together (KEY FINDING#4), but a new optimization or more efficient cyber protection schemes may reach that objective, as technically URLLC is feasible with a 55% success rate.
This last finding finally answers the first research question (REQ#1). We can conclude the current technological state does not make feasible secure 5G URLLC.
For completeness, we conducted the same analysis with architectures based on ONAP as core implementation but considering SD-RAN and OpenAirInterface for the RAN.
For the remaining network architectures, as the observed evolution for different significance levels is independent of the network configuration, we only focus on cost calculated for a significance level of 0.1 so, results can be displayed in a clearer manner.
For all cases, the cost (or success rate) for secure URLLC is around 50%, being slightly lower (45%, approximately) for SD-RAN networks and slightly higher (around 55%) for ORAN deployments. From these results it can be deducted that the underlaying network architecture only affects global cost in a small percentage. In general, the impact of jitter and delays introduced by cyber protection mechanisms are larger than the differences initially observed among the different network configurations.
Thus, as main and global conclusion, secure URLLC are not feasible with current 5G implementations and cyber protection mechanisms. New and more efficient cyber protection strategies and schemes are required and should be studied.
Threats to the internal validity are not relevant in our results. PRISM model checker considers statistical methods to reduce the impact of bias and numerical error or malfunctions. Besides, standard software, systems and libraries were employed in order to avoid calibration or instrumentation problems.
Regarding the external validity, numerical results obtained through the proposed cost function may change if different mathematical functions are employed in the future (in order to represent more complex attacks) but conclusions will remain the same. Actually, and according to the Taylor series theory
In this work we introduce a cost-security function as a method to evaluate the performance and adequacy of security mechanisms in 5G networks to support ultra-reliable low-latency communications (URLLC). We categorized existing security solutions according to their purpose and deployment scope. Each one is analyzed in the context of different 5G architecture implementations using two different approaches. First, using model checking techniques, we evaluated the probability of an attacker to be successful against each security technology. Second, using analytical models, we analyzed the impact of these security mechanisms in terms of delay, throughput consumption, and reliability. Finally, we combined both analyses using stochastic cost-security functions and the PRISM model checker to create a global picture.
Our simulation results show how cyber protection mechanisms have a relevant impact in URLLC. Precisely, a fully secure 5G network can only guarantee URLLC with a probability of ∼55% using the best configuration of available core and RAN technologies. Furthermore, it may turn even unfeasible to provide secure URLLC in some scenarios and 5G implementations.
Four key findings are presented in this work:
In general, and only considering core security solutions, a global cyber-protected 5G network can only guarantee a 60% of success in URLLC provision. A scheme in which endpoint cyber protection solutions are deployed only guarantees a success probability of 65% (approximately) for URLLC. The impact of management plane security and privacy mechanisms in URLLC is negligible. Secure URLLC cannot be guaranteed using the best current solutions together.
The proposed analysis is valid for network deployments with a homogenous composition. In our framework, the RAN, and the core network, are based on one unique technology with a standard and known behavior. The applicability of our proposal to transition scenarios is limited, as hybrid technologies are typically employed and different values for delays, jitter and packet losses are observed in different network domains. On the other hand, the proposed cost-security functions are only valid for consumer applications. Other scenarios such as critical infrastructures, emergency systems, military communications, etc. show very specific QoS requirements and, eventually, the proposed stochastic cost-security functions should be adapted to represent in a better way the real weight of every factor in those scenarios.
Our future work will consider real network deployments and real cyber protection mechanisms to carry out experiments that show the actual performance of secure URLLC in practice.
Finally, the proposed framework is also valid for networks beyond 5G, such as the novel 6G networks. In general, mobile networks beyond 5G show similar behavior and QoS requirements to current 5G schemes. Although some changes could be done at physical level (RAN and core network), most important changes are envisioned to affect the network and service management solutions. Our approach perfectly fits these future networks as well, as different management protocols or service provision architectures can be considered just by introducing the right value for the number of interconnected modules in the RAN and core network, the new physical characteristics of networks and the actual description of the network topology.