EP-4740567-A1 - METHOD AND SYSTEM FOR AUTOMATICALLY MONITORING A NETWORK

EP4740567A1EP 4740567 A1EP4740567 A1EP 4740567A1EP-4740567-A1

Abstract

The present disclosure relates to a method [300] and a system [200] for automatically monitoring a network The method comprises: receiving, by a transceiver unit [202], a Streaming Data Record (SDR) data, fetching, by a validation unit [204], a set of pre-configured validation policy based on the network procedure failure, performing, by the validation unit [204], a validation associated with the SDR data, detecting, by the validation unit [204], a validation status associated with the validation, generating, by an analytics engine [206], an enriched SDR data based on the validation pass status, generating, by the analytics engine [206], a network analysis report associated with the network function based on the enriched SDR data and monitoring, by a monitoring unit [208], the network based on at least the network analysis report.

Inventors

MURARKA, ANKIT
BHATNAGAR, AAYUSH
SAXENA, GAURAV
Sarohi, Meenakshi
DE, Supriya Kaushik
Kishore, Jugal
KUMAR, RAHUL

Assignees

Jio Platforms Limited

Dates

Publication Date: 20260513
Application Date: 20240612

Claims (12)

1. A method [300] for automatically monitoring a network, the method [300] comprising: receiving [304], by a transceiver unit [202], a Streaming Data Record (SDR) data associated with a network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure; fetching [306], by a validation unit [204], a set of pre-configured validation policy based on the network procedure failure; - performing [308], by the validation unit [204], a validation associated with the SDR data based on the set of pre-configured validation policy; detecting [310], by the validation unit [204], a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status; - generating [312], by an analytics engine [206], an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; - generating [314], by the analytics engine [206], a network analysis report associated with the network function based on the enriched SDR data; and - monitoring [316], by a monitoring unit [208], the network based on at least the network analysis report.
2. The method [300] as claimed in claim 1, further comprising: normalising, by the analytics engine [206], the enriched SDR data, and storing, by the analytics engine [206], the enriched SDR data in a database in a predefined record format, wherein predefined record format is at least one of a raw record format and a computed record format.
3. The method [300] as claimed in claim 2, wherein the network analysis report associated with the network function is generated based on at least one of the raw record format and the computed record format.
4. The method [300] as claimed in claim 1, wherein the validation pass status is detected based on comparing the SDR data and at least one pre-configured validation policy from the set of preconfigured validation policy.
5. The method [300] as claimed in claim 1, further comprising displaying, via an interface, the network analysis report.
6. A system [200] for automatically monitoring a network, the system [200] comprises: a transceiver unit [202] configured to receive, a Streaming Data Record (SDR) data associated with a network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure; a validation unit [204] connected to at least the transceiver unit [202], the validation unit [204] configured to: • fetch, a set of pre-configured validation policy based on the network procedure failure, • perform, a validation associated with the SDR data based on the set of preconfigured validation policy, and • detect a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status; an analytics engine [206] connected to at least the validation unit [204], the analytics engine [206] configured to: • generate, an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format, and • generate, a network analysis report associated with the network function based on the enriched SDR data; and a monitoring unit [208] connected to at least the analytics engine [206], the monitoring unit [208] configured to monitor the network based on at least the network analysis report.
7. The system [200] as claimed in claim 6, wherein the analytics engine [206] is further configured to: - normalise, the enriched SDR data; and store the enriched SDR data in a database in a predefined record format, wherein predefined record format is at least one of a raw record format and a computed record format.
8. The system [200] as claimed in claim 7, wherein the network analysis report associated with the network function is generated based on at least one of the raw record format and the computed record format.
9. The system [200] as claimed in claim 6, wherein the validation pass status is detected based on comparing the SDR data and at least one pre-configured validation policy from the set of preconfigured validation policy.
10. The system [200] as claimed in claim 6, the system [200] further comprises displaying via an interface the network analysis report.
11. A User Equipment (UE) comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to: transmit, to a system, a set of pre-configured validation policy based on a network procedure failure, wherein the set of pre-configured policy, when received by the system, is used for generating a network analysis report; and receive, from the system, the network analysis report, wherein the network analysis report is used for monitoring the network, and wherein the network analysis report is generated, by the system, based on: receiving a Streaming Data Record (SDR) data associated with the network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure; on fetching the set of pre-configured validation policy based on the network procedure failure, performing a validation associated with the SDR data based on the set of pre-configured validation policy; detecting a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status; - generating an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; and - generating a network analysis report associated with the network function based on the enriched SDR data.
2. A non-transitory computer-readable storage medium storing instructions for service fallback in 5G core (5GC) network, the storage medium comprising executable code which, when executed by a processor [704], cause the processor [704] to: receive a Streaming Data Record (SDR) data associated with a network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure; fetch a set of pre-configured validation policy based on the network procedure failure; - perform a validation associated with the SDR data based on the set of pre-configured validation policy; detect a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status; - generate an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; - generate a network analysis report associated with the network function based on the enriched SDR data; and - monitor the network based on at least the network analysis report.

Description

METHOD AND SYSTEM FOR AUTOMATICALLY MONITORING A NETWORK FIELD OF THE DISCLOSURE [0001] The present disclosure relates generally to the field of wireless communication systems. More particularly, the present disclosure relates to methods and systems for automatic network monitoring and network structure probing i.e., automatically monitoring a network. BACKGROUND [0002] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art. [0003] Wireless communication technology has rapidly evolved over the past few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. 3G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users. [0004] In the context of 5G networks, probing plays a crucial role in ensuring the robustness and efficiency of the network infrastructure. With the advent of 5G technology, which introduces new capabilities such as ultra-low latency, high bandwidth, and massive connectivity, the need for sophisticated probing techniques becomes paramount. Network operators and administrators rely on probing mechanisms to continuously monitor and analyse various aspects of 5G networks, including signal strength, latency, packet loss, and Quality of Service (QoS) metrics. Probing enables them to identify potential bottlenecks, optimize network performance, and troubleshoot issues promptly, thereby ensuring seamless delivery of high-speed, low-latency services to endusers. [0005] Moreover, in the dynamic and complex environment of 5G networks, probing also serves as a vital tool for security and risk management. With the proliferation of loT devices, edge computing, and mission-critical applications in 5G ecosystems, the attack surface and potential vulnerabilities increase significantly. Probing techniques such as vulnerability scanning, intrusion detection, and traffic analysis help detect and mitigate security threats, unauthorized access attempts, and malicious activities in real-time. By proactively probing the network for security risks and anomalies, organizations can enhance their cyber defence posture and safeguard sensitive data. [0006] Further, traditional network monitoring and network structure probing methods have long faced challenges due to the limitations imposed by physical taps, aggregators, and packet capturing tools. Physical taps, which involve physically accessing and tapping into network cables, often require significant effort and resources. They can disrupt network connectivity and pose risks of damage or interference to the network infrastructure. Similarly, aggregators, which are used to collect network traffic from multiple sources, face limitations in terms of scalability and flexibility. They can struggle to handle large volumes of network data, leading to potential data loss or delays in capturing critical information. Additionally, aggregators may not provide granular visibility into specific network segments or devices, limiting their effectiveness in complex network environments. Further, the packet capturing tools, although commonly used for network monitoring, also present challenges. They typically require deep packet inspection and analysis, which can be time-consuming and resource intensive. Furthermore, encrypted traffic poses a significant hurdle for traditional packet capturing tools, as they are unable to decipher encrypted content, limiting their ability to provide comprehensive insights. These challenges have led to the development of alternative approaches and technologies in network monitoring and structure probing. For instance, software-defined networking (SDN) and network functions virtualization (NFV) have emerged as solutions that offer greater flexibility, scalability, and visibility. They enable centralized management and control of network resources, allowing fo