Description:Field of the Invention

Generally, the present disclosure relates to automated testing systems. Particularly, the present disclosure relates to a system for automated security regression testing comprising a neural fuzzer, a test target module, and a test result storage module.
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 domain of software development and cybersecurity, ensuring the integrity and security of software systems constitutes a critical concern. The continuous evolution of technology and the complexity of modern software applications necessitate advanced methodologies for detecting and mitigating security vulnerabilities. Automated security testing, particularly regression testing, plays a pivotal role in this context by systematically examining software to ensure that recent program or code changes have not adversely affected existing functionalities or introduced new vulnerabilities.
One traditional approach to automated security testing involves the use of static analysis tools that scrutinize code for known vulnerability patterns without executing the program. While effective in identifying certain types of vulnerabilities, static analysis tools are limited by their inability to detect runtime issues and more sophisticated attack vectors that emerge only during the application's execution. Dynamic analysis, including dynamic testing techniques such as fuzz testing, complements static analysis by executing the software with varying inputs to uncover vulnerabilities that manifest during runtime.
Fuzz testing, or fuzzing, has emerged as a powerful dynamic testing method that involves feeding a software program with invalid, malformed, or unexpected data inputs. The primary objective of fuzzing is to trigger unhandled exceptions, crashes, or failures that indicate potential security vulnerabilities. Traditional fuzzing techniques, however, often rely on random or semi-random data generation strategies, leading to inefficient test coverage and the potential overlook of critical vulnerabilities.
The advent of machine learning and artificial intelligence technologies has offered promising enhancements to fuzz testing methodologies. By applying machine learning algorithms, it becomes possible to intelligently generate test cases that more effectively traverse the input space of a software program. Such neural fuzzers are capable of learning from past testing data, identifying patterns, and predicting areas within the input space that are more likely to reveal vulnerabilities. This targeted approach not only increases the efficiency of the testing process but also enhances the likelihood of uncovering significant security flaws.
Despite these advancements, the integration of machine learning-based fuzz testing into practical security testing workflows presents several challenges. Firstly, the effective deployment of neural fuzzers requires seamless interfacing with the software under test, including web applications and API servers. This interfacing is crucial for delivering the intelligently generated test cases to the target software components and ensuring comprehensive coverage of the application's functionality. Secondly, the management and analysis of the vast amounts of data produced during the testing process pose significant challenges. A robust test result storage module is essential for organizing, storing, and analyzing test reports and outcomes. Such a module enables the continuous refinement of testing strategies and the maintenance of an up-to-date vulnerability database, which is instrumental in informing future testing cycles and security measures.
Additionally, the dynamic landscape of cybersecurity threats necessitates that automated security testing systems remain adaptable and scalable. The capacity to update and evolve in response to emerging vulnerabilities and sophisticated attack vectors is paramount for maintaining the security integrity of software systems. As such, the development and deployment of advanced automated security regression testing systems that incorporate neural fuzzing, effective interfacing mechanisms, and comprehensive test result management capabilities are imperative.
In light of the above discussion, there exists an urgent need for solutions that overcome the challenges associated with conventional systems and techniques for automated security regression testing. The integration of machine learning methods for the generation of test cases, combined with effective interfacing with web applications and API servers, and robust test result storage and analysis capabilities, represents a significant advancement in addressing the complexities of modern software security testing. The proposed system aims to provide a comprehensive and efficient solution for identifying potential security vulnerabilities, thereby enhancing the overall security posture of software systems.
Summary
Generally, the present disclosure relates to automated testing systems. Particularly, the present disclosure relates to a system for automated security regression testing comprising a neural fuzzer, a test target module, and a test result storage module.
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.
The present disclosure describes an advanced system and method for automated security regression testing designed to enhance the security and resilience of software systems through comprehensive analysis and testing. At the core of this system is a neural fuzzer, a cutting-edge tool that leverages machine learning techniques to navigate through the vast input space of a software program. This neural fuzzer is adept at generating test cases that uncover potential security vulnerabilities by introducing unexpected or erroneous data inputs. To facilitate these tests, the system includes a test target module specifically configured to interface seamlessly with web applications and API servers, thereby ensuring that the generated test cases are applied effectively. Moreover, the system features a test result storage module dedicated to archiving test reports and maintaining an up-to-date vulnerability database, which is continuously enriched by the findings from executed test cases.
In an embodiment, the neural fuzzer is further refined to dynamically adjust its test case generation process based on real-time feedback obtained during testing sessions. This adaptability not only improves the efficiency of the testing process but also deepens the system's understanding of the target application's architecture and potential security weak points. By refining its approach in this manner, the neural fuzzer can more accurately identify and exploit vulnerabilities within the software.
In an embodiment, the neural fuzzer employs sophisticated neural network algorithms to generate test cases. These algorithms are trained to recognize patterns and rules derived from the operational behavior of the target application, allowing for the creation of highly targeted and complex test scenarios. This capability enables the neural fuzzer to simulate a wide array of attack vectors, thereby uncovering vulnerabilities that might otherwise remain undetected.
In an embodiment, the system is specifically tailored to handle the complexities associated with large-scale software systems. The neural fuzzer is designed to thoroughly examine extensive input spaces, enabling it to identify potential security issues across the entirety of a complex system. This exhaustive approach ensures that the system can effectively safeguard against vulnerabilities in large and intricate software environments.
In an embodiment, the test result storage module plays a critical role in optimizing resource allocation. By automating the generation, execution, and analysis of test cases, the module not only streamlines the testing process but also allows security teams to concentrate on more strategic aspects of threat mitigation and vulnerability remediation. This automation significantly enhances the efficiency and effectiveness of security operations.
In an embodiment, the neural fuzzer is engineered to bolster the software system's defense mechanisms against both known and emergent security threats. Through continuous and real-time testing, the fuzzer promptly identifies and addresses security vulnerabilities as they surface, thereby significantly improving the software's resilience to attacks.
In an embodiment, the seamless integration of the neural fuzzer and the test target module into existing software development and deployment pipelines underscores the system's proactive approach to security. By embedding automated security testing into the software development lifecycle, the system ensures ongoing vigilance against potential vulnerabilities from the earliest stages of development through to deployment.
In an embodiment, the neural fuzzer's efficacy is further enhanced by the use of high-quality training data. This ensures that the generated test cases are both relevant and effective in uncovering real and impactful security vulnerabilities, thus preventing the waste of resources on inadequate or irrelevant testing scenarios.
In an embodiment, to accommodate the significant computational demands of testing large and complex software systems, the neural fuzzer is designed to operate efficiently with specialized hardware accelerators or within high-performance computing clusters. This capability ensures that the system can maintain high levels of performance and scalability, critical factors for thorough and effective security testing.
Lastly, a method for automated security regression testing encapsulates the processes of generating test cases using the neural fuzzer, applying these cases via the test target module, and updating a vulnerability database with the test results stored by the test result storage module. This methodological approach underscores the system's comprehensive and dynamic nature, ensuring that it remains at the forefront of software security testing technology. Through continuous adaptation and integration within software development processes, this system and method offer a robust solution for enhancing the security posture of software systems in the face of evolving cyber threats.

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 system for automated security regression testing, in accordance with the embodiments of the present disclosure;
FIG. 2 illustrates a method for automated security regression testing, in accordance with the embodiments of the present disclosure; and
FIG. 3 illustrates an architecture of automated security regression testing based on neural fuzzing, 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.
Generally, the present disclosure relates to automated testing systems. Particularly, the present disclosure relates to a system for automated security regression testing comprising a neural fuzzer, a test target module, and a test result storage module.
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 system 100 for automated security regression testing, in accordance with the embodiments of the present disclosure. Said system 100 comprises a neural fuzzer (102) configured to generate test cases by applying machine learning methods to traverse the input space of a software program. The traversal aims to identify potential security vulnerabilities through unexpected or erroneous data input. Additionally, the system includes a test target module (104). Said module is configured to interface with a web application and an API server for the purpose of receiving the generated test cases. Furthermore, a test result storage module (106) forms part of the system. Said module is configured to store test reports and maintain a vulnerability database. The maintenance of said database is informed by the outcomes of the executed test cases.
In an embodiment, the neural fuzzer (102) utilizes advanced machine learning algorithms. These algorithms enable the efficient exploration of vast input spaces associated with complex software programs. By leveraging such algorithms, the neural fuzzer (102) effectively uncovers a wide array of potential security vulnerabilities. These vulnerabilities might otherwise remain undetected through conventional testing methods.
In another embodiment, the test target module (104) exhibits compatibility with various web application frameworks and API protocols. This compatibility ensures that the system can be applied across a diverse range of software environments. By facilitating seamless integration with different web applications and API servers, the test target module (104) enhances the system's utility in identifying security vulnerabilities in a multitude of software contexts.
In a further embodiment, the test result storage module (106) employs a structured approach to organize test reports. Such an approach allows for the efficient retrieval of information related to identified vulnerabilities. Moreover, the module incorporates mechanisms for updating the vulnerability database based on new findings from subsequent testing cycles. This continuous updating process ensures that the database remains a relevant and valuable resource for addressing software security concerns.
In an embodiment, the neural fuzzer (102) is further enhanced by the integration of adaptive learning techniques. These techniques enable the fuzzer to refine its test case generation strategy over time. As a result, the efficiency in identifying security vulnerabilities improves as more test cases are executed and analyzed. This adaptive capability signifies a departure from static testing methods, offering a dynamic approach to security testing that evolves in response to newly discovered threat patterns.
In another embodiment, the test target module (104) includes functionalities for simulating user interactions with the web application or API server. By simulating a range of user behaviors, the module facilitates a comprehensive assessment of the software's security posture under varied operational conditions. This simulation of user interactions plays a critical role in uncovering security vulnerabilities that might only be exposed through specific sequences of actions or inputs.
In a subsequent embodiment, the test result storage module (106) features advanced data analytics capabilities. These capabilities enable the detailed analysis of test reports with the aim of identifying trends and patterns in the security vulnerabilities discovered. Through such analysis, insights can be gleaned regarding common security weaknesses or frequently exploited vulnerabilities across different software programs. These insights can then inform targeted improvements to software security measures.
In an embodiment, the system incorporates a feedback loop between the test result storage module (106) and the neural fuzzer (102). Through this feedback loop, information from executed test cases and identified vulnerabilities is utilized to inform the generation of future test cases. This iterative process enhances the relevance and effectiveness of the test cases generated by the neural fuzzer (102), leading to more efficient identification of security vulnerabilities over time.
In another embodiment, the system is designed to operate autonomously, with minimal human intervention required. Automated scheduling of regression tests and the autonomous operation of the neural fuzzer, test target module, and test result storage module (106) contribute to a highly efficient testing process. This autonomy not only reduces the resource burden associated with manual testing but also ensures that security testing can be conducted continuously and systematically.
In a further embodiment, the vulnerability database maintained by the test result storage module (106) is structured to facilitate easy access to vulnerability records. Such structuring includes categorization of vulnerabilities by type, severity, and the software components affected. This organization of the database enables quick identification of critical vulnerabilities that require immediate attention, thereby enhancing the efficiency of security remediation efforts.
In an embodiment, the system is equipped with interfaces for integration with external security tools and platforms. This integration capability allows for the enrichment of the testing process with external data sources and security intelligence. By leveraging external insights and tools, the system's capacity to identify and address complex security vulnerabilities is significantly enhanced.
In an embodiment, the neural fuzzer (102) of the system is further configured to adaptively refine test case generation based on ongoing input received during testing. This refinement process enhances the understanding of the target application's structure and potential attack surfaces. By continually integrating feedback from each testing cycle, the neural fuzzer (102) adjusts its approach to efficiently traverse the application’s input space. This adaptive strategy allows the system to focus on areas more likely to contain vulnerabilities, thereby optimizing the testing process. The ability to dynamically refine test case generation based on real-time feedback represents a significant advancement over static testing methods. It enables the neural fuzzer (102) to evolve its understanding of the software's architecture and potential weaknesses, ensuring that the generated test cases are increasingly effective in uncovering security issues. This method not only increases the efficiency of the testing process but also contributes to a deeper comprehension of the target application’s security landscape, facilitating the identification of complex attack vectors that might otherwise be overlooked.
In another embodiment, the neural fuzzer (102) employs neural networks to create test cases informed by rules and patterns learned from the operational characteristics of the target application. This employment enables the generation of more sophisticated and targeted test cases. By analyzing the application's behavior and learning its unique operational characteristics, the neural fuzzer (102) can generate test cases that are highly relevant to the specific context of the application. This tailored approach ensures that the test cases are not only diverse but also focused on areas of the software most susceptible to security breaches. The use of neural networks allows the fuzzer to leverage complex pattern recognition capabilities, enabling it to identify and exploit subtle vulnerabilities and intricate attack surfaces that simpler fuzzing techniques might miss. This capability enhances the system’s effectiveness in identifying security vulnerabilities, thereby providing a more robust security assessment of the target application.
In a further embodiment, the neural fuzzer (102) is adapted to perform testing on large-scale, complex software systems to comprehensively analyze extensive input spaces and uncover potential security vulnerabilities throughout the system. This adaptation enables the fuzzer to handle the complexities and vastness of modern software systems, which often contain millions of lines of code and numerous interconnected components. By designing the neural fuzzer (102) to efficiently navigate these large input spaces, the system ensures that testing is thorough and encompasses the entire software ecosystem. This comprehensive analysis is critical for identifying security vulnerabilities that may be deeply embedded within the software or arise from interactions between disparate system components. The capability to perform detailed testing on large-scale systems is essential for maintaining the security integrity of complex software architectures, ensuring that potential vulnerabilities are identified and addressed before they can be exploited by malicious actors.
In an embodiment, the test result storage module (106) is configured to facilitate efficient allocation of resources by automating the creation, execution, and analysis of test cases. This automation frees security teams to focus on strategic threat mitigation and vulnerability remediation tasks. By streamlining the process of managing test cases and their results, the module significantly reduces the manual effort required to conduct comprehensive security testing. This efficiency enables security teams to allocate their expertise where it is most needed: in interpreting the results of automated tests to identify and remediate vulnerabilities. The automation of routine testing tasks not only enhances the productivity of security teams but also ensures that testing can be conducted continuously and at scale, improving the overall security posture of the software system.
In another embodiment, the neural fuzzer (102) is further configured to improve software system resilience against emergent and unknown security vulnerabilities by employing continuous, real-time testing. This employment identifies and addresses security issues as they arise, ensuring that the software system remains protected against evolving threats. The continuous testing framework provided by the neural fuzzer (102) allows for the immediate detection and mitigation of vulnerabilities, significantly reducing the window of opportunity for attackers. This proactive approach to security testing is crucial in today's rapidly changing threat landscape, where new vulnerabilities can emerge at any time. By continuously monitoring the system for potential security breaches, the neural fuzzer (102) plays a critical role in maintaining the resilience of the software system against both known and unknown threats.
In an embodiment, the neural fuzzer (102) and the test target module (104) are integrated into existing software development and deployment pipelines. This integration enables proactive security testing throughout the software development lifecycle. By embedding security testing directly into the development process, the system ensures that vulnerabilities are identified and addressed at the earliest possible stage. This proactive approach significantly reduces the risk of security issues making it into production environments, where they can cause significant damage. The integration of security testing into the development pipeline also fosters a culture of security awareness among developers, encouraging the adoption of secure coding practices and enhancing the overall security of the software system.
In an embodiment, the neural fuzzer (102) is trained using quality-assured training data to mitigate the generation of inadequate test cases and to ensure the discovery of security vulnerabilities. This training ensures that the neural fuzzer (102) operates at peak efficiency, generating high-quality test cases that are effective in uncovering potential security issues. The use of quality-assured data for training is crucial for developing a sophisticated understanding of various attack vectors and vulnerability patterns. This understanding allows the fuzzer to simulate advanced attack scenarios accurately, thereby enhancing the system's ability to detect vulnerabilities. The focus on quality in the training phase ensures that the neural fuzzer (102) remains a reliable and effective tool for identifying security weaknesses in software systems.
In another embodiment, the neural fuzzer (102) is configured to operate in conjunction with specialized hardware accelerators or high-performance computing clusters. This configuration manages the computational demands associated with testing large, complex software systems. The use of specialized hardware accelerators or computing clusters enables the neural fuzzer (102) to perform intensive computations at high speeds, facilitating the rapid generation and execution of test cases. This capability is particularly important when dealing with large-scale software systems, where the volume of potential input combinations can be enormous. By leveraging advanced computing resources, the neural fuzzer (102) can maintain a high level of testing thoroughness and efficiency, even in the face of challenging computational demands, ensuring that security testing does not become a bottleneck in the software development process.
FIG. 2 illustrates a method 200 for automated security regression testing, in accordance with the embodiments of the present disclosure. At step 202, generate test cases by applying machine learning methods with a neural fuzzer (102) to traverse the input space of a software program, identifying potential security vulnerabilities. At step 204, interface with a web application and an API server using a test target module (104) to apply the generated test cases for assessment. At step 206, store test reports and update a vulnerability database with a test result storage module (106) based on the outcomes of the executed test cases.
FIG. 3 illustrates an architecture of automated security regression testing based on neural fuzzing, in accordance with the embodiments of the present disclosure. The system comprises three primary components: the security testing framework, test targets, and test result storage. within the security testing framework, a neural fuzzer operates in conjunction with a regression tester to generate and apply test cases utilizing machine learning methods. these test cases traverse the input space of a software program, probing for security vulnerabilities. the generated test cases are then communicated to the test targets, which consist of a web application and an API server, where the tests are executed to evaluate the security of the systems. following test execution, the outcomes are forwarded to the test result storage component. this component includes facilities for compiling the test reports and updating a vulnerability database, which records and tracks identified security flaws based on the results from the testing phase.
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 system (100) for automated security regression testing comprising: a neural fuzzer (102) configured to generate test cases by applying machine learning methods to traverse the input space of a software program to identify potential security vulnerabilities through unexpected or erroneous data input; a test target module (104) configured to interface with a web application and an API server to receive the generated test cases; and a test result storage module (106) configured to store test reports and maintain a vulnerability database informed by the outcomes of the executed test cases.
The system (100) of claim 1, wherein the neural fuzzer (102) is further configured to adaptively refine test case generation based on ongoing input received during testing to enhance the understanding of the target application's structure and potential attack surfaces.
The system (100) of claim 1, wherein the neural fuzzer (102) employs neural networks to create test cases informed by rules and patterns learned from the operational characteristics of the target application, thereby enabling the generation of more sophisticated and targeted test cases.
The system (100) of claim 1, wherein the neural fuzzer (102) is adapted to perform testing on large-scale, complex software systems to comprehensively analyze extensive input spaces and uncover potential security vulnerabilities throughout the system.
The system (100) of claim 1, wherein the test result storage module (106) is configured to facilitate efficient allocation of resources by automating the creation, execution, and analysis of test cases, thus freeing security teams to focus on strategic threat mitigation and vulnerability remediation tasks.
The system (100) of claim 1, wherein the neural fuzzer (102) is further configured to improve software system resilience against emergent and unknown security vulnerabilities by employing continuous, real-time testing to identify and address security issues as they arise.
The system (100) of claim 1, wherein the neural fuzzer (102) and the test target module (104) are integrated into existing software development and deployment pipelines to enable proactive security testing throughout the software development lifecycle.
The system (100) of claim 1, wherein the neural fuzzer (102) is trained using quality-assured training data to mitigate the generation of inadequate test cases and to ensure the discovery of security vulnerabilities.
The system (100) of claim 1, wherein the neural fuzzer (102) is configured to operate in conjunction with specialized hardware accelerators or high-performance computing clusters to manage the computational demands associated with testing large, complex software systems.
A method (200) for automated security regression testing, the method (200) comprising: generating, by a neural fuzzer (102), test cases based on machine learning methods to traverse the input space of a software program; interfacing, by a test target module (104), with a web application and an API server to apply the generated test cases; and storing, by a test result storage module (106), test reports and updating a vulnerability database based on the results of the executed test cases.

SYSTEM FOR AUTOMATED SECURITY REGRESSION TESTING

The present disclosure provides a system for automated security regression testing. The system comprises a neural fuzzer configured to generate test cases by applying machine learning methods to traverse the input space of a software program to identify security vulnerabilities through unexpected or erroneous data input; a test target module configured to interface with a web application and an API server to receive the generated test cases; and a test result storage module configured to store test reports and maintain a vulnerability database informed by the outcomes of the executed test cases.

Fig. XX

Drawings
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FIG.1

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FIG. 2

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FIG. 3

, Claims:I/We claims:

A system (100) for automated security regression testing comprising: a neural fuzzer (102) configured to generate test cases by applying machine learning methods to traverse the input space of a software program to identify potential security vulnerabilities through unexpected or erroneous data input; a test target module (104) configured to interface with a web application and an API server to receive the generated test cases; and a test result storage module (106) configured to store test reports and maintain a vulnerability database informed by the outcomes of the executed test cases.
The system (100) of claim 1, wherein the neural fuzzer (102) is further configured to adaptively refine test case generation based on ongoing input received during testing to enhance the understanding of the target application's structure and potential attack surfaces.
The system (100) of claim 1, wherein the neural fuzzer (102) employs neural networks to create test cases informed by rules and patterns learned from the operational characteristics of the target application, thereby enabling the generation of more sophisticated and targeted test cases.
The system (100) of claim 1, wherein the neural fuzzer (102) is adapted to perform testing on large-scale, complex software systems to comprehensively analyze extensive input spaces and uncover potential security vulnerabilities throughout the system.
The system (100) of claim 1, wherein the test result storage module (106) is configured to facilitate efficient allocation of resources by automating the creation, execution, and analysis of test cases, thus freeing security teams to focus on strategic threat mitigation and vulnerability remediation tasks.
The system (100) of claim 1, wherein the neural fuzzer (102) is further configured to improve software system resilience against emergent and unknown security vulnerabilities by employing continuous, real-time testing to identify and address security issues as they arise.
The system (100) of claim 1, wherein the neural fuzzer (102) and the test target module (104) are integrated into existing software development and deployment pipelines to enable proactive security testing throughout the software development lifecycle.
The system (100) of claim 1, wherein the neural fuzzer (102) is trained using quality-assured training data to mitigate the generation of inadequate test cases and to ensure the discovery of security vulnerabilities.
The system (100) of claim 1, wherein the neural fuzzer (102) is configured to operate in conjunction with specialized hardware accelerators or high-performance computing clusters to manage the computational demands associated with testing large, complex software systems.
A method (200) for automated security regression testing, the method (200) comprising: generating, by a neural fuzzer (102), test cases based on machine learning methods to traverse the input space of a software program; interfacing, by a test target module (104), with a web application and an API server to apply the generated test cases; and storing, by a test result storage module (106), test reports and updating a vulnerability database based on the results of the executed test cases.

SYSTEM FOR AUTOMATED SECURITY REGRESSION TESTING