Description:Field of the Invention

Generally, the present disclosure relates to network security. Particularly, the present disclosure relates to enhancing security through advanced data analysis and threat detection mechanisms.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
In the realm of digital communications, the assurance of network security has become paramount. Systems are continuously developed and employed to safeguard data from unauthorized access and to maintain the integrity and availability of network resources. Among the various strategies implemented, the analysis of network traffic plays a crucial role. This involves examining the data packets that travel across a network to identify potential threats or anomalies. Traditionally, methods such as deep packet inspection have been utilized to capture and scrutinize the payloads of network packets. Such techniques allow for the detailed analysis of the content being transmitted, enabling the detection of malicious activities.
Furthermore, the storage of network packets and their associated data after analysis is essential for both immediate threat detection and future forensic analysis. A data storage unit that retains raw packets along with processed information facilitates the investigation of security incidents and aids in understanding attack patterns over time. Additionally, the evolution of machine learning technologies has introduced new capabilities in the field of network security. Machine learning models, trained on processed data from network traffic, have the potential to predict and identify security threats with high accuracy. These models are capable of learning from past data to recognize patterns indicative of network intrusions or other security concerns.
Moreover, the effective management and interpretation of the results generated by machine learning models are critical for timely and informed decision-making in network security operations. A visualization module that presents a comprehensive dashboard of the inference outcomes assists security professionals in quickly assessing the network's security status. This visualization aids in identifying detected threats, analyzing traffic for forensic purposes, ensuring compliance with regulatory standards, and preventing data loss.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for detecting and mitigating network intrusions, analyzing traffic for forensic investigations, ensuring compliance monitoring, and preventing data loss within the network security system.

Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
A network security system (100) is introduced, comprising a series of integrated modules designed for the comprehensive analysis and protection of network data. The initial component, a deep packet inspection module (102), is configured to capture and analyze network packet payloads. In an embodiment, said deep packet inspection module is further equipped to decrypt encrypted network packets before their analysis to detect malware patterns and signatures. In an embodiment, communication with said deep packet inspection module, a data storage unit (104) is established, where said data storage unit is configured to store both raw packets and processed data derived from the analysis of network packet payloads. Said data storage unit comprises a relational database system, configured to categorize said raw packets and said processed data based on predetermined criteria.
In an embodiment, a machine learning model module (106) is in communication with said data storage unit. Said machine learning model module is configured for training on the stored processed data and performing inference to detect network security threats. Said machine learning model module comprises a plurality of machine learning algorithms selected from the group consisting of neural networks, decision trees, support vector machines, and ensemble methods. In an embodiment, said machine learning model module is further configured to automatically update its inference models in response to the detection of new types of network security threats.
In an embodiment, a visualization module (108) is in communication with said machine learning model module (106), configured to present a dashboard that displays the results of said inference performed by the machine learning model module (106). Said visualization module is further configured to generate real-time alerts based on the detection of network security threats by the machine learning model module (106). Said visualization module includes customizable dashboard elements that enable forensic analysts to interactively manipulate the displayed data.
In an embodiment, said deep packet inspection module, data storage unit, machine learning model module, and visualization module operate collectively to detect and mitigate network intrusions, analyze traffic for forensic investigations, ensure compliance monitoring, and prevent data loss within the network security system. Said network security system further comprises a network interface module configured to receive network packets from a plurality of network sources.
In an embodiment, said deep packet inspection module (102) is further configured to perform protocol analysis for identifying non-compliant activities in network traffic. The method for operating a network security system to protect a network from security threats includes capturing network packet payloads using a deep packet inspection module (102), analyzing the captured network packet payloads to identify potential security threats, storing the raw packets and processed data resulting from the analysis in a data storage unit (104), training a machine learning model module (106) using the processed data stored in the data storage unit (104), performing inference using the trained machine learning model module (106) to detect network security threats, and presenting results of the inference on a dashboard through a visualization module (108).
The method for operating a network security system entails a sequence of integrated steps designed to safeguard networks against security threats, starting with the capture of network packet payloads using a deep packet inspection module capable of decrypting encrypted packets for malware detection. Following payload analysis for potential threats, both raw packets and processed data are stored in a relational database within a data storage unit, which then feeds into a machine learning model module. This module, employing a diverse array of algorithms such as neural networks and decision trees, is trained on the processed data to detect network security threats. It can autonomously update its models to counter new threats. The culmination of this process is the presentation of inference results on a dashboard, enabling real-time threat detection and visualization, thereby offering a comprehensive solution for network security management.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a network security system, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method for operating a network security system to protect a network from security threats, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates an architecture of methods for state-of-the-art cyber forensics by means of deep packet inspection with the help of machine learning models, in accordance with the embodiments of the present disclosure.

Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a network security system (100), in accordance with the embodiments of the present disclosure. The network security system (100) comprises several critical components working in conjunction to enhance network security, particularly in the context of a non-fungible token (NFT) marketplace. Each component plays a vital role in ensuring the integrity and security of data within the network, offering a robust solution to detect and mitigate security threats.
One of the key components of the network security system (100) is the deep packet inspection module (102). The primary function of the deep packet inspection module (102) involves the capture and analysis of network packet payloads. Through the process of capturing and analyzing network packets, the deep packet inspection module (102) allows for detailed scrutiny of the data flowing through the network. This detailed analysis aids in the identification of malicious activities or anomalies within the packet data, thereby serving as the first line of defense against network intrusions. The capacity of the deep packet inspection module (102) to dissect and evaluate the contents of each packet enables the system to identify potential threats at an early stage, significantly reducing the risk of security breaches.
In conjunction with the deep packet inspection module (102), the data storage unit (104) plays a crucial role within the network security system (100). The data storage unit (104) is responsible for storing raw packets as well as processed data derived from the analysis conducted by the deep packet inspection module (102). The configuration of the data storage unit (104) to accommodate both raw and processed information ensures that a comprehensive repository of network activity is maintained. This repository not only facilitates further analysis but also aids in forensic investigations, compliance monitoring, and the prevention of data loss. By providing a secure environment for the storage of critical data, the data storage unit (104) ensures that valuable information is preserved for future reference and analysis.
The machine learning model module (106), which is in communication with the data storage unit (104), represents another fundamental component of the network security system (100). The machine learning model module (106) is specifically configured for training on the stored processed data and executing inference processes to detect network security threats. The incorporation of machine learning techniques enables the system to adapt and improve its threat detection capabilities over time. By analyzing patterns and anomalies in the data, the machine learning model module (106) can identify potential security threats with increasing accuracy. This continuous learning process ensures that the network security system remains effective against evolving threats, thereby providing enhanced protection for the NFT marketplace.
Finally, the visualization module (108) serves as an interface between the machine learning model module (106) and the system administrators or security analysts. Configured to present a dashboard that displays the results of the inference performed by the machine learning model module (106), the visualization module (108) facilitates the interpretation and analysis of security data. Through the dashboard, users can obtain insights into detected threats, traffic patterns, and potential vulnerabilities within the network. The visualization module (108) plays a pivotal role in enabling timely decision-making and response actions to mitigate identified security threats. By providing a user-friendly interface for the display of complex data, the visualization module (108) ensures that critical security information is accessible and actionable.
In an embodiment, the deep packet inspection module (102) of the network security system is further configured to decrypt encrypted network packets before proceeding with the analysis to detect malware patterns and signatures. This configuration enables the deep packet inspection module (102) to extend its capabilities beyond the examination of plaintext packets, allowing for a comprehensive analysis of encrypted data. By decrypting the packets, the module can effectively identify malicious content that might otherwise remain undetected within encrypted traffic. This feature is particularly important in modern network environments where encryption is widely used for data privacy and security. The ability to decrypt and analyze encrypted packets ensures that the network security system can maintain a high level of vigilance against a wide range of security threats, including sophisticated malware that relies on encryption to evade detection.
In another embodiment, the data storage unit (104) of the network security system comprises a relational database system. This relational database system is configured to categorize raw packets and processed data based on predetermined criteria. The categorization facilitates efficient data retrieval and analysis, enabling security analysts to quickly access relevant information. By organizing data in a structured manner, the data storage unit enhances the overall efficiency of the network security system. The use of a relational database system also supports complex queries and analyses, which are crucial for in-depth security investigations and compliance monitoring. This systematic organization of data plays a critical role in the system’s ability to respond to security incidents and to perform forensic investigations.
In a further embodiment, the machine learning model module (106) of the network security system comprises a plurality of machine learning algorithms. These algorithms are selected from a group consisting of neural networks, decision trees, support vector machines, and ensemble methods. The diversity of algorithms enables the module to effectively address a wide range of network security challenges. Neural networks, for instance, excel at identifying patterns in large datasets, making them well-suited for detecting complex threats. Decision trees and support vector machines offer robust classification capabilities, useful for categorizing network traffic and identifying anomalies. Ensemble methods, which combine multiple machine learning models, enhance the predictive accuracy and reliability of the system’s threat detection capabilities. The integration of these varied algorithms allows the machine learning model module to adapt and evolve in response to emerging network security threats.
In an embodiment, the visualization module (108) of the network security system is further configured to generate real-time alerts based on the detection of network security threats by the machine learning model module (106). This functionality ensures that security analysts are promptly informed about potential security incidents, enabling quick response and mitigation actions. The ability to generate real-time alerts is essential for maintaining the security integrity of the network. It allows analysts to take immediate action against identified threats, thereby minimizing potential damage. The real-time alerting feature complements the dashboard capabilities of the visualization module, providing a dynamic and interactive security monitoring environment.
In an embodiment, the network security system further comprises a network interface module. This module is configured to receive network packets from a plurality of network sources. The inclusion of a network interface module significantly enhances the system’s ability to monitor network traffic comprehensively. By facilitating the reception of packets from various sources, the network interface module ensures that the system has a holistic view of the network environment. This comprehensive monitoring is crucial for detecting and mitigating security threats that may originate from any part of the network. The network interface module serves as the gateway through which all incoming and outgoing network traffic passes, positioning it as a key component in the system’s security architecture.
In an embodiment, the deep packet inspection module (102) of the network security system is further configured to perform protocol analysis. This analysis aims at identifying non-compliant activities within network traffic. Protocol analysis enables the module to scrutinize the adherence of network packets to established communication protocols. Non-compliance with these protocols often indicates malicious activities or misconfigurations that could compromise network security. By performing protocol analysis, the deep packet inspection module enhances the system’s ability to detect and respond to a broad spectrum of security threats. This capability is particularly valuable for identifying attacks that exploit protocol vulnerabilities or for ensuring compliance with network policies and standards.
In an embodiment, the visualization module (108) of the network security system includes customizable dashboard elements. These elements enable forensic analysts to interactively manipulate the displayed data. Customizable dashboards provide analysts with the flexibility to tailor the data presentation according to their specific needs and preferences. This interactivity enhances the analysts’ ability to conduct in-depth investigations and to derive meaningful insights from the data. By enabling customization, the visualization module facilitates a more efficient and effective security analysis process. The ability for analysts to adjust dashboard elements ensures that critical information is highlighted and easily accessible, thereby supporting timely and informed decision-making in response to security incidents.
In an embodiment, the machine learning model module (106) of the network security system is further configured to automatically update its inference models in response to the detection of new types of network security threats. This capability ensures that the system remains effective against evolving threats by continually enhancing its threat detection models. Automatic updates allow the machine learning model module to incorporate the latest threat intelligence and patterns into its analysis, improving the accuracy and relevance of its threat detection capabilities. This adaptability is crucial for staying ahead of sophisticated and continuously changing network security threats. The ability to automatically update inference models reflects the system’s commitment to maintaining a high level of security vigilance and its capacity to protect the network environment against the latest security challenges.
FIG. 2 illustrates a method (200) for operating a network security system to protect a network from security threats, in accordance with the embodiments of the present disclosure. At step (202) the method initiates with the capturing of network packet payloads through the deep packet inspection module (102). This module scrutinizes the data flowing through the network, ensuring that no packet goes unchecked for potential threats. At step (204) the network packet payloads undergo thorough analysis to identify potential security threats. This analysis is pivotal in distinguishing benign data from malicious content potentially harmful to the network. At step (206) upon completion of the analysis, both the raw packets and the processed data derived from the analysis are stored in a data storage unit (104). This unit acts as a repository for future reference and further analysis. At step (208) the stored processed data in the data storage unit (104) is then utilized for training a machine learning model module (106). This training involves adapting the model to recognize patterns indicative of network security threats. At step (210) after the machine learning model module (106) is sufficiently trained, it performs inference to detect network security threats. This inference process utilizes the learned patterns to identify potential threats in new network data. At step (212) the results from the inference conducted by the machine learning model module (106) are presented on a dashboard through a visualization module (108). This dashboard visually represents the detected threats, enabling prompt response actions.
FIG. 3 illustrates an architecture of methods for state-of-the-art cyber forensics by means of deep packet inspection with the help of machine learning models, in accordance with the embodiments of the present disclosure. At the inception, the Deep Packet Inspection process commences with the Packet Capture, where data packets traversing the network are intercepted. This is immediately followed by Packet Analysis, a critical step wherein each packet's payload is examined for anomalies or signatures indicative of security threats. The data flow then cascades into the Data Storage component, which bifurcates and retains the information into Raw Packets and Processed Data, ensuring both immediate and future analytical access. Subsequently, the Machine Learning Models component leverages the stored Processed Data for Model Training, where algorithms learn to discern and predict threat patterns. This trained model then undertakes Model Inference, applying its predictive acumen to real-time network data to identify potential threats. The culmination of this process is depicted through the Visualization component, wherein the results from the Machine Learning Models' inference are succinctly presented on a Dashboard. This dashboard provides an intuitive and accessible graphical interface for cybersecurity professionals to monitor, evaluate, and respond to the identified threats, thereby reinforcing the network's defense mechanisms.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claims:

A network security system (100), comprising:
a deep packet inspection module (102) configured to capture and analyze network packet payloads;
a data storage unit (104) in communication with said deep packet inspection module, said data storage unit being configured to store raw packets and processed data derived from said analysis of network packet payloads;
a machine learning model module (106) in communication with said data storage unit, said machine learning model module being configured for training on the stored processed data and performing inference to detect network security threats; and
a visualization module (108) in communication with said machine learning model module (106), said visualization module (108) being configured to present a dashboard that displays the results of said inference performed by the machine learning model module (106), wherein said deep packet inspection module, data storage unit, machine learning model module, and visualization module operate collectively to detect and mitigate network intrusions, analyze traffic for forensic investigations, ensure compliance monitoring, and prevent data loss within the network security system.
The network security system of claim 1, wherein said deep packet inspection module (102) is further configured to decrypt encrypted network packets before analysis to detect malware patterns and signatures.
The network security system of claim 1, wherein said data storage unit (104) comprises a relational database system configured to categorize said raw packets and said processed data based on predetermined criteria.
The network security system of claim 1, wherein said machine learning model module (106) comprises a plurality of machine learning algorithms selected from the group consisting of neural networks, decision trees, support vector machines, and ensemble methods.
The network security system of claim 1, wherein said visualization module (108) is further configured to generate real-time alerts based on the detection of network security threats by the machine learning model module (106).
The network security system of claim 1, further comprising a network interface module configured to receive network packets from a plurality of network sources.
The network security system of claim 1, wherein said deep packet inspection module (102) is further configured to perform protocol analysis for identifying non-compliant activities in network traffic.
The network security system of claim 1, wherein said visualization module (108) includes customizable dashboard elements that enable forensic analysts to interactively manipulate the displayed data.
The network security system of claim 1, wherein said machine learning model module (106) is further configured to automatically update its inference models in response to the detection of new types of network security threats.
A method for operating a network security system to protect a network from security threats, comprising:
capturing network packet payloads using a deep packet inspection module (102);
analyzing the captured network packet payloads to identify potential security threats;
storing the raw packets and processed data resulting from the analysis in a data storage unit (104);
training a machine learning model module (106) using the processed data stored in the data storage unit (104);
performing inference using the trained machine learning model module (106) to detect network security threats; and
presenting results of the inference on a dashboard through a visualization module (108).

NETWORK SECURITY SYSTEM

Disclosed is a network security system comprising: a deep packet inspection module configured to capture and analyze network packet payloads; a data storage unit in communication with said deep packet inspection module, said data storage unit being configured to store raw packets and processed data derived from said analysis of network packet payloads; a machine learning model module in communication with said data storage unit, said machine learning model module being configured for training on the stored processed data and performing inference to detect network security threats; and a visualization module in communication with said machine learning model module, said visualization module being configured to present a dashboard that displays the results of said inference performed by the machine learning model module, wherein said deep packet inspection module, data storage unit, machine learning model module, and visualization module operate collectively to detect and mitigate network intrusions, analyze traffic for forensic investigations, ensure compliance monitoring, and prevent data loss within the network security system.
Fig. 1

Drawings
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FIG. 1
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FIG. 2
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FIG. 3

, Claims:I/We claims:

A network security system (100), comprising:
a deep packet inspection module (102) configured to capture and analyze network packet payloads;
a data storage unit (104) in communication with said deep packet inspection module, said data storage unit being configured to store raw packets and processed data derived from said analysis of network packet payloads;
a machine learning model module (106) in communication with said data storage unit, said machine learning model module being configured for training on the stored processed data and performing inference to detect network security threats; and
a visualization module (108) in communication with said machine learning model module (106), said visualization module (108) being configured to present a dashboard that displays the results of said inference performed by the machine learning model module (106), wherein said deep packet inspection module, data storage unit, machine learning model module, and visualization module operate collectively to detect and mitigate network intrusions, analyze traffic for forensic investigations, ensure compliance monitoring, and prevent data loss within the network security system.
The network security system of claim 1, wherein said deep packet inspection module (102) is further configured to decrypt encrypted network packets before analysis to detect malware patterns and signatures.
The network security system of claim 1, wherein said data storage unit (104) comprises a relational database system configured to categorize said raw packets and said processed data based on predetermined criteria.
The network security system of claim 1, wherein said machine learning model module (106) comprises a plurality of machine learning algorithms selected from the group consisting of neural networks, decision trees, support vector machines, and ensemble methods.
The network security system of claim 1, wherein said visualization module (108) is further configured to generate real-time alerts based on the detection of network security threats by the machine learning model module (106).
The network security system of claim 1, further comprising a network interface module configured to receive network packets from a plurality of network sources.
The network security system of claim 1, wherein said deep packet inspection module (102) is further configured to perform protocol analysis for identifying non-compliant activities in network traffic.
The network security system of claim 1, wherein said visualization module (108) includes customizable dashboard elements that enable forensic analysts to interactively manipulate the displayed data.
The network security system of claim 1, wherein said machine learning model module (106) is further configured to automatically update its inference models in response to the detection of new types of network security threats.
A method for operating a network security system to protect a network from security threats, comprising:
capturing network packet payloads using a deep packet inspection module (102);
analyzing the captured network packet payloads to identify potential security threats;
storing the raw packets and processed data resulting from the analysis in a data storage unit (104);
training a machine learning model module (106) using the processed data stored in the data storage unit (104);
performing inference using the trained machine learning model module (106) to detect network security threats; and
presenting results of the inference on a dashboard through a visualization module (108).

NETWORK SECURITY SYSTEM