DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
METHOD AND SYSTEM FOR ASSET DISCOVERY IN A NETWORK
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
[0001] The present invention generally relates to communication networks, and in particular to a system and method for asset discovery in the communication networks.
BACKGROUND OF THE INVENTION
[0002] A server is a computer program or device that provides a service to another computer program and its user, also known as the client. In a data centre, the physical computer that a server program runs on is also frequently referred to as a server. A server is a powerful machine designed to compute, store, and manage data, devices, and systems over a network. In computing, a server is a piece of computer hardware or software (computer program) that provides functionality for other programs or devices.
[0003] A server farm or server cluster is a collection of computer servers maintained by an organization to supply server functionality far beyond the capability of a single device. The number of servers keeps on increasing as new servers are added and for a large organisation the number of servers may be 5000 odd. Server discovery allows client applications to find servers on the network.
[0004] A user or network administrator may be unaware of a new server being present or added and being available in the network. To identify a server and for the server discovery process, the IP (Internet Protocol) address of the new server or at least the subnet is required. In the prior art, the IPs are received manually via such as a data sheet (Excel File) or through the user interface and the like. The server discovery process is then initiated by a user interface or bulk / batch scripting. This involves manual effort and time to initiate manually the server discovery process subsequently.
[0005] Besides, the user might not be aware of a server being present or any new server being added unless informed so. This is not desirable as it leads to sub – optimal use of network resources.
[0006] It is desired that presence of a new server or addition of a new server is substantially instantly known to the user and the server discovery process is automatically initiated upon identification of any new server.
[0007] Thus, there exists a need for a solution that overcomes the above challenges and provides for a system and method for detecting and identifying any new server in the network automatically and initiates the server discovery process without any manual intervention.
SUMMARY OF THE INVENTION
[0008] One or more embodiments of the present invention provide a system and method of asset discovery in a network.
[0009] In accordance with one embodiment, a method of asset discovery in a network is disclosed. The method includes the step of monitoring, by one or more processors, data packets being transmitted across the network in real time. Further, the method includes the step of extracting, by the one or more processors, relevant data from the monitored data packets. The method includes the step of parsing, by the one or more processors, the relevant data to check for at least one of a new source Internet Protocol (IP) address and a new destination IP address. Further, the method includes the step of identifying, by the one or more processors, an asset in the network based on the parsing of the relevant data, the asset is identified based on presence of at least one of the new source IP address and the new destination IP address of the asset in the network.
[0010] In one embodiment, the relevant data is at least one of a source Internet Protocol (IP) address and a destination IP address.
[0011] In another embodiment, on identification, the method further includes the step of collecting, by the one or more processors, additional information pertaining to the identified asset, the collected information is stored in a database.
[0012] In yet another embodiment, subsequent to storing the collected information in the database, the method further comprises the step of incorporating, by the one or more processors, the collected information pertaining to the identified asset to a training data to identify additional assets in the network.
[0013] In another embodiment, a system for asset discovery in a network is disclosed. The system includes a monitoring unit configured to monitor data packets being transmitted across the network in real time. The system further includes an extraction unit configured to extract relevant data from the monitored data packets. Further, the system includes a parsing unit configured to parse the relevant data to check for at least one of a new source Internet Protocol (IP) address and a new destination IP address. The system includes an identification unit configured to identify an asset in the network based on the parsing of the relevant data, the asset is identified based on presence of at least one of the new source IP address and the new destination address of the asset in the network.
[0014] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes invention of electrical components, electronic components or circuitry commonly used to implement such components.
[0016] FIG. 1 is an exemplary block diagram of an environment of asset discovery in a network, according to various embodiments of the present invention.
[0017] FIG. 2 illustrates an architecture for a system for detecting and identifying new servers/assets in the network, according to various embodiments of the present invention;
[0018] FIG. 3 is an exemplary block diagram of a system of asset discovery in the network, according to various embodiments of the present invention; and
[0019] FIG. 4 is a flow chart illustrating a method of asset discovery in a network, according to various embodiments of the present invention.
[0020] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0021] Some embodiments of the present invention, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise.
[0022] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present invention including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0023] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0024] The present invention relates to detection and identification of new assets or servers in a network whenever one or more new servers/assets are added in the network. In addition, the invention is directed towards running discovery to collect details regarding a newly discovered asset/server thereby obviating the need for manual intervention. In accordance with an embodiment of the invention, the system comprises an AI (Artificial Intelligence)/ML (Machine Learning) model to analyze the data packets travelling over the network such as source and destination IP addresses and the like.
[0025] The invention enables automatic detection of newly introduced assets and servers within the networks thereby reducing the need for user intervention, as it identifies any newly added servers and performs discovery processes on those specific servers to obtain additional details. This is achieved through the AI/ML model feedback into the system, which continuously monitors the network data packets transmitted over the network, and specifically the source IP address and the destination IP address.
[0026] FIG. 1 is an exemplary block diagram of an environment 100 of asset discovery in a network 105, according to various embodiments of the present invention. The environment 100 includes the network 105, a User Equipment (UE) 110, a server 115, and a system 125. The UE 110 aids a user to interact with the system 125 for transmitting data packets to one or more processors 302 (as shown in FIG. 3) via the network 105.
[0027] For the purpose of description and explanation, the description will be explained with respect to one or more User Equipments (UEs) 110, or to be more specific will be explained with respect to a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present invention.
[0028] The terms “user equipment,” and “first UE”, “the second UE”, and “the third UE,” and variations thereof, as used herein, are used interchangeably, without limiting the scope of the present disclosure.
[0029] In an embodiment, each of the first UE 110a, the second UE 110b, and the third UE 110c is one of, but are not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0030] In accordance with one aspect of the present invention, each of the first UE 110a, the second UE 110b, and the third UE 110c is configured to facilitate the transmission of data packets via the network 105 for the purpose of availing a variety of services. The said services are inclusive of, but not limited to, engaging with the server 115 for the receiving the data packets thereto. This configuration enables a streamlined and efficient interaction between each of the first UE 110a, the second UE 110b, and the third UE 110c and the network resources, thereby enhancing the utility and performance of the network 105 in providing the said services.
[0031] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0032] The environment 100 includes the server 115 accessible via the network 105. The server 115 may include by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise, a defence facility, or any other facility that provides content.
[0033] The environment 100 further includes the system 125 communicably coupled to the server 115 and each of the first UE 110a, the second UE 110b, and the third UE 110c via the network 105. The system 125 is adapted to be embedded within the server 115 or is embedded as an individual entity. However, for the purpose of description, the system 125 is described as an integral part of the server 115, without deviating from the scope of the present invention. The system 125 is configured to asset discovery in the network 105.
[0034] Operational and construction features of the system 125 will be explained in detail with respect to the following figures.
[0035] FIG. 2 illustrates an architecture 200 for the system 125 for detecting and identifying new servers/assets in the network 105, according to various embodiments of the present invention. The architecture 200 includes a distributed cache 202, a plurality of network nodes 204, an Artificial Intelligence/Machine Learning (AI/ML) model 206, an Input/Output (I/O) interface unit 208, and the database 210.
[0036] The distributed cache 202 is a pool of random-access memory (RAM) of multiple networked computers into a single in-memory data store for use as a data cache to provide fast access to data. The distributed cache 202 is essential for applications that need to scale across multiple servers or distributed geographically. The distributed cache 202 ensures that the data is available close to where it’s needed, even if the original data source is remote or under heavy load.
[0037] The I/O interface unit 208 is configured to transmit a request to the system 125. In an embodiment, the request corresponds to the monitoring of the data packets, and identification of the new server/asset in the network 105. The system 125 is configured to monitor the data packets. The data packets are continuously travelling through the plurality of network nodes 204 to the AI/ML model 206. The plurality of network nodes 204 are individual points or devices within the network 105 that are capable of sending, receiving, and forwarding data. In an embodiment, the plurality of network nodes 204 include, but not limited to, a first network node, a second network node, a third network node and the like. On monitoring the data packets from the plurality of network nodes 204 by a monitoring unit 308 (as shown in FIG. 3). The relevant information is extracted from the monitored data packets by an extraction unit 310 (as shown in FIG. 3). The relevant information includes, but not limited to, a source IP address and a destination IP address. In an embodiment, the source IP address and the destination IP address is related to, at least one of, but not limited to, the server 115, the plurality of network nodes 204, routers, switches, hubs, firewalls, printers, hosts, and wireless access points
[0038] Upon extracting the relevant information from the monitored data packets, the system 125 is configured to develop and train the AI/ML model 206 utilizing the extracted relevant information. The AI/ML model 340 utilizes a variety of ML techniques, is at least one of a supervised learning model, an unsupervised learning model, and a clustering model such as a K-Means clustering. In one embodiment, the supervised learning is a type of machine learning model, which is trained on a labeled dataset. The supervised learning refers to each training example paired with an output label. The supervised learning model learns to map inputs to a correct output. In one embodiment, the unsupervised learning is a type of machine learning model, which is trained on data without any labels. The unsupervised learning algorithm tries to learn the underlying structure or distribution in the data in order to discover patterns or groupings. The K-Means clustering partitions network traffic data into K clusters based on feature similarity. New clusters can indicate the presence of new servers/assets. The AI/ML model 206 is trained to recognize one or more new server/asset in the network 105 by using a parsing unit 312 (as shown in FIG. 3). The trained AI/ML model 206 continuously captures and analyzes the data packets in real-time and recognizes the new server/asset in the network 105.
[0039] The system 125 is provided with IP addresses of servers, which is thereafter stored in the database 210. The system 125 is configured to identify the servers as the IP address of the data packets being monitored by the AI/ML model 206 is already present in the database 210. In addition, if any new server/asset is present in the network 105, the system 125 does not recognize the existence of the new server/asset in the network 105. For this, the system 125 is configured to collect a source IP address and a destination IP address of the new server/asset. Thereafter, the collected source and destination IP addresses are transmitted to the AI/ML model 206 to train the collected data. The trained data in the AI/ML model is stored in the database 210 and thereby aids in identifying the new server/asset in the network 105. Further, the system 125 initiates an auto discovery process for identifying the new server/asset in the network 105.
[0040] The terms “new server,” and “new asset,” and variations thereof, as used herein, are used interchangeably, without limiting the scope of the present disclosure.
[0041] Upon training of the AI/ML model 206, the system 125 is configured to identify the new server/asset in the network 105 by using an identification unit 314 (as shown in FIG. 3). The system 125 indicates existence of the new server/asset in the network 105 to the I/O interface unit 208. The system 125 is configured to enable detection of newly introduced assets and servers within the network 105, which reduces the need for user intervention. Once the new server is detected, the system 125 initiates an auto discovery process and collects the information of the new server/asset by using a collection unit 316 (as shown in FIG. 3). The auto discovery process is run to collect information about the newly discovered server. The collected information pertaining to the identified new asset is incorporated for training data in the AI/ML model 206 to identify additional servers/assets in the network 105 by an incorporation unit 318 (as shown in FIG. 3).
[0042] In an embodiment, the input/output (I/O) interface unit 208 includes a variety of interfaces, for example, interfaces for data input and output devices, referred to as Input/Output (I/O) devices, storage devices, and the like. The I/O interface unit 208 is responsible for facilitating communication between the Central Processing Unit (CPU) and external devices such as storage devices, peripherals, and communication networks. In one embodiment, the I/O interface unit 208 provides a communication pathway for one or more components of the system 125. Examples of such components include, but are not limited to, the UE 110 and the database 210.
[0043] The I/O interface unit 208 receives responses from the system 125 regarding the discovery of the existence of the new server/asset during the auto discovery process. Upon discovery of the existence of the new server/asset during the auto discovery process, the information of the new server/asset is updated into the database 210. The information of the new server/asset includes, but not limited to the source IP address and the destination IP address.
[0044] Upon updating information of the new server/asset into the database 210, the newly discovered server details are incorporated into the AI/ML model 206 for training data to identify and detect the new server/asset. In an example, the network 105 includes 4000 servers. The database 210 includes IP addresses of all the 4000 servers of the network 105. If the IP address on the data packets being monitored by the AI/ML model 206 is already present in the database 210, the system 125 recognizes the old server and no new server is said to have been identified. On the contrary, when a new IP address is identified, the auto discovery process of the server is initiated by the system 125.
[0045] As such, the above technique of the present invention reduces user interference, provides real time monitoring and analysis of the data packets to recognize the new server details. This ensures the system 125 can effortlessly adapt to changes in the network 105 in real time, thereby providing enhanced network security, improved network management, improved processing speed, and reduced requirement of memory space.
[0046] FIG. 3 is an exemplary block diagram of the system 125 of asset discovery in the network 105, according to various embodiments of the present invention.
[0047] As per the illustrated embodiment, the system 125 includes one or more processors 302, a memory 304, and the I/O Interface unit 208. The one or more processors 302, hereinafter referred to as the processor 302 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. As per the illustrated embodiment, the system 125 includes one or more processors 302. However, it is to be noted that the system 125 may include multiple processors as per the requirement and without deviating from the scope of the present invention.
[0048] The information related to data packets and asset discovery in the network 105 may be provided or stored in the memory 304. Among other capabilities, the processor 302 is configured to fetch and execute computer-readable instructions stored in the memory 304. The memory 304 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 304 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0049] The information related to data packets and asset discovery in the network 105 may be rendered on the I/O interface unit 208. In an embodiment, the input/output (I/O) interface unit 208 includes a variety of interfaces, for example, interfaces for data input and output devices, referred to as Input/Output (I/O) devices, storage devices, and the like. The I/O interface unit 208 is responsible for facilitating communication between the Central Processing Unit (CPU) and external devices such as storage devices, peripherals, and communication networks. In one embodiment, the I/O interface unit 208 provides the communication pathway for one or more components of the system 125. Examples of such components include, but are not limited to, the UE 110 and the database 210.
[0050] The database 210 is configured to store the data packets transmitted by the UE 110. The database 210 is one of, but is not limited to, one of a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of the database 210 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0051] Further, the processor 302, in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 302. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 302 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for processor 302 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 304 may store instructions that, when executed by the processing resource, implement the processor 302. In such examples, the system 125 may comprise the memory 304 storing the instructions and the processing resource to execute the instructions, or the memory 304 may be separate but accessible to the system 125 and the processing resource. In other examples, the processor 302 may be implemented by electronic circuitry.
[0052] In order for the system 125 to asset discovery in the network 105, the processor 302 includes the monitoring unit 308, the extraction unit 310, the parsing unit 312, the identification unit 314, the collection unit 316, and the incorporation unit 318 communicably coupled to each other for monitoring data packets, extracting relevant data, parsing the relevant data, identifying asset, collecting additional information, and incorporating the collected information pertaining to the identified asset.
[0053] The monitoring unit 308, the extraction unit 310, the parsing unit 312, the identification unit 314, the collection unit 316, and the incorporation unit 318 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 302. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 302 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 304 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 125 may comprise the memory 304 storing the instructions and the processing resource to execute the instructions, or the memory 304 may be separate but accessible to the system 125 and the processing resource. In other examples, the processor 302 may be implemented by electronic circuitry.
[0054] The monitoring unit 308 is communicably connected to each of the first UE 110a, the second UE 110b, and the third UE 110c via the network 105. Accordingly, the monitoring unit 308 is configured to monitor data packets being transmitted across the network 105 in real time. The data packets are continuously travelling through the plurality of network nodes 204. The plurality of network nodes 204 are individual points or devices within the network 105 that are capable of sending, receiving, and forwarding data. In an embodiment, the plurality of network nodes 204 include, but not limited to, a first network node, a second network node, a third network node and the like.
[0055] On monitoring the data packets being transmitted across the network 105, the extraction unit 310 is configured to extract relevant data from the monitored data packets. In an embodiment, the relevant data is at least one of the source Internet Protocol (IP) address and the destination IP address. As used herein, the source IP address is the IP address of the device that initiates a data transmission across the network 105. As used herein, the destination IP address is the IP address of the device that is intended to receive the transmitted data and ensures that the transmitted data reaches a correct endpoint on the network 105. In an embodiment, the source IP address and the destination IP address are related to, at least one of, but not limited to, the server 115, the plurality of network nodes 204, routers, switches, hubs, firewalls, printers, hosts, and wireless access points.
[0056] On extracting the relevant data from the monitored data packets, the parsing unit 312 is configured to parse the relevant data to check for at least one of a new source IP address and a new destination IP address. Upon parsing the relevant data, the system 125 is configured to develop and train the AI/ML model 206. The AI/ML model 206 is trained to recognize the new servers/assets in the network 105 based on the parsing of the relevant data. The trained AI/ML model 206 is configured for continuously capturing and analyzing the data packets in real-time and thereby recognizes the new servers/assets in the network 105.
[0057] Upon training of the AI/ML model 206, the identification unit 314 is uniquely configured to identify the new server/asset in the network 105 based on the parsing of the relevant data. The new server/asset can be diverse and encompass a range of physical and intangible components essential for performing network operation and delivery of communication services. The new server/asset is identified based on presence of the at least one of the new source IP address and the new destination IP address of the new server/asset in the network 105.
[0058] Further, the system 125 includes the collection unit 316 configured to collect additional information pertaining to the identified asset. The additional information includes, but not limited to multiple parameters of Operating System (OS), hardware, CPU, memory, network interfaces, middleware’s and applications running on the server 115. The collected information is stored in the database 210.
[0059] On storing the collected information of the new server into the database 210, the incorporation unit 318 is configured to incorporate the collected information pertaining to the identified asset. The incorporation unit 318 is configured to train the collected information in the AI/ML model 206 to identify the new servers/assets in the network 105. In an example, the network 105 includes 4000 servers. The database 210 includes IP addresses of all the 4000 servers of the network 105. If the IP address on the data packets being monitored by the AI/ML model 206 is already present in the database 210, the system 125 recognizes the old server and no new server is said to have been identified. On the contrary, when the new IP address is identified, the auto discovery process of the server is initiated by the system 125.
[0060] As such, the above techniques of the system 125 recognizes the new server by identifying a new IP address, once the new IP address is identified, the server discovery process is initiated. Owing to this process, the system 125 reduces user interference, provides real time monitoring, parsing and identifying details of the server 115, and effortlessly adapts to change in the network almost in real time. Furthermore, the system 125 provides enhanced network security and improved network management so as to improve processing speed and reduce requirement of memory space.
[0061] FIG. 4 is a flow chart illustrating a method 400 of asset discovery in the network 105, according to various embodiments of the present invention. The method 400 includes various steps illustrated in multiple steps for asset discovery.
[0062] At step 405, the method 400 includes the step of monitoring data packets being transmitted across the network 105 in real time by the monitoring unit 308. The data packets are continuously travelling through the plurality of network nodes 204. The plurality of network nodes 204 are individual points or devices within the network 105 that are capable of sending, receiving, and forwarding data. In an embodiment, the plurality of network nodes 204 include, but not limited to, a first network node, a second network node, a third network node and the like.
[0063] At step 410, the method 400 includes the step of extracting relevant data from the monitored data packets by the extraction unit 310. In an embodiment, the relevant data is at least one of the source Internet Protocol (IP) address and the destination IP address. The source IP address is an identifier assigned to the UE 110 that initiates a data transmission or communication. The destination IP address is the identifier assigned to the UE 110 that is intended to receive the transmitted data.
[0064] At step 415, the method 400 includes the step of parsing the relevant data to check for at least one of a new source IP address and a new destination IP address by the parsing unit 312. Upon parsing the relevant data, the system 125 is configured to develop and train the AI/ML model 206. The AI/ML model 206 is trained to recognize new servers/assets in the network 105. The trained AI/ML model 206 continuously captures and analyzes the data packets in real-time and recognizes the new servers/assets in the network 105.
[0065] At step 420, the method 400 includes the step of identifying an asset in the network 105 based on the parsing of the relevant data by the identification unit 314. The asset can be diverse and encompass a range of physical and intangible components essential for performing network operation and delivery of communication services. The asset is identified based on presence of the at least one of the new source IP address and the new destination IP address of the asset in the network 105.
[0066] Further, the method 400 includes the step of collecting additional information pertaining to the identified asset by the collection unit 316. The additional information includes, but not limited to multiple parameters of Operating System (OS), hardware, CPU, memory, network interfaces, middleware’s and applications running on the server 115. The collected information is stored in the database 210.
[0067] Further, the method 400 includes the step of incorporating the collected information pertaining to the identified asset to training the data to identify additional assets in the network 105 by the incorporation unit 318 based on storing of the collected information of the new server into the database 210.
[0068] As such, the above techniques of the present invention provide multiple advantages. The invention reduces user interference, provides real time monitoring and analysis of the data packets to recognize the new server details. This ensures the method 400 can effortlessly adapt to changes in the network 105 in real time, thereby providing enhanced network security, improved network management, improved processing speed, and reduced requirement of memory space.
[0069] A person of ordinary skill in the art will readily ascertain that the illustrated steps in FIG. 4 are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0070] In another embodiment, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions when executed by a processor 302. The processor 302 is configured to monitor data packets being transmitted across the network 105 in real time. Further, the processor 302 is configured to extract relevant data from the monitored data packets. Further, the processor 302 is configured to parse the relevant data to check for at least one of a new source Internet Protocol (IP) address and a new Destination IP address. Further, the processor 302 is configured to identify an asset in the network based on the parsing of the relevant data, the asset is identified based on presence of at least one of the new source IP address and the new destination address of the asset in the network 105.
[0071] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0072] The present invention incorporates technical advancement of detecting and identifying the new server/asset in the network whenever the new servers are added in the network and then running auto discovery process to collect details regarding the newly discovered asset/server thereby obviating the need for manual intervention. The auto discovery process is achieved through the AI/ML model feedback into the system, which continuously monitors the network data packets transmitted over the network, and specifically the source IP address and the destination IP address.
[0073] As such, the above techniques of the present invention provide multiple advantages. The invention reduces user interference, provides real time monitoring and analysis of the data packets to recognize the new server details. The present invention can effortlessly adapt to changes in the network in real time, thereby providing enhanced network security, improved network management, improved processing speed, and reduced requirement of memory space.
[0074] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS
[0075] Environment - 100;
[0076] Network – 105;
[0077] User Equipment - 110;
[0078] Server – 115;
[0079] System -125;
[0080] Distributed cache – 202;
[0081] Plurality of network nodes – 204;
[0082] AI/ML model- 206;
[0083] I/O Interface unit – 208;
[0084] Database– 210;
[0085] Processor-302;
[0086] Memory- 304;
[0087] Monitoring Unit- 308;
[0088] Extraction Unit- 310;
[0089] Parsing Unit – 312;
[0090] Identification Unit – 314;
[0091] Collection Unit- 316;
[0092] Incorporation Unit– 318.

,CLAIMS:CLAIMS
We Claim:
1. A method (400) of asset discovery in a network (105), the method (400) comprising the steps of:
monitoring (405), by one or more processors (302), data packets being transmitted across the network (105) in real time;
extracting (410), by the one or more processors (302), relevant data from the monitored data packets;
parsing (415), by the one or more processors (302), the relevant data to check for at least one of a new source Internet Protocol (IP) address and a new destination IP address; and
identifying (420), by the one or more processors (302), an asset in the network (105) based on the parsing of the relevant data, the asset is identified based on presence of at least one of the new source IP address and the new destination IP address of the asset in the network.

2. The method (400) as claimed in claim 1, wherein the relevant data is at least one of a source Internet Protocol (IP) address and a destination IP address.

3. The method (400) as claimed in claim 1, wherein on identification, the method further comprises the step of collecting, by the one or more processors (302), additional information pertaining to the identified asset, wherein the collected information is stored in a database (210).

4. The method (400) as claimed in claim 3, wherein subsequent to storing the collected information in the database (210), the method (400) further comprises the step of incorporating, by the one or more processors (302), the collected information pertaining to the identified asset to a training data to identify additional assets in the network (105).

5. A system (125) for asset discovery in a network (105), the system (125) comprising:
a monitoring unit (308) configured to monitor data packets being transmitted across the network in real time;
an extraction unit (310) configured to extract relevant data from the monitored data packets;
a parsing unit (312) configured to parse the relevant data to check for at least one of a new source Internet Protocol (IP) address and a new Destination IP address; and
an identification unit (314) configured to identify an asset in the network based on the parsing of the relevant data, the asset is identified based on presence of at least one of the new source IP address and the new destination address of the asset in the network.

6. The system (125) as claimed in claim 5, wherein the relevant data is at least one of a source Internet Protocol (IP) address and a destination IP address.

7. The system (125) as claimed in claim 5, wherein the system (125) further comprising:
a collection unit (316) configured to collect additional information pertaining to the identified asset, wherein the collected information is stored in a database (210); and
an incorporation unit (318) configured to incorporate the collected information pertaining to the identified asset to training data to identify additional assets in the network (105).