Description:Field of the Invention

The present disclosure generally relates to computer security. Further, the present disclosure particularly relates to a system for malware detection and classification within a Weka Framework.
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.
Given the escalating reliance on internet connectivity for a myriad of organizational, industrial, and personal activities, the threat landscape has witnessed a marked increase in the prevalence of malware. This malicious software is designed with various harmful intents, including the unauthorized theft, encryption, or deletion of data, as well as the modification or hijacking of core computing operations. Furthermore, it aims to covertly monitor user activities without obtaining permission. A notable challenge in combating malware stems from the sophisticated techniques employed by its authors, such as polymorphism, metamorphism, and obfuscation. These techniques enable the constant evolution of malware, significantly hindering detection efforts and rendering traditional security measures less effective.
The primary method for detecting malware within the cybersecurity industry, particularly among antivirus software developers, is the utilization of signature-based detection systems. These systems operate by identifying known malware samples through their unique digital signatures. While signature-based detection is renowned for its accuracy in identifying previously catalogued threats, its fundamental limitation lies in its inability to recognize new or modified malware variants. This gap in detection capability exposes systems to the risk of infection by new strains of malware and leaves them defenseless against zero-day exploits, which are attacks that exploit previously unknown vulnerabilities.
An alternative approach to malware detection involves the use of heuristic analysis. This method employs algorithms to analyze the behavior of programs, aiming to identify suspicious activities that could indicate the presence of malware. Although heuristic analysis expands the scope of detection beyond known malware signatures, it is not without its challenges. False positives, where legitimate software is incorrectly flagged as malicious, can disrupt user operations and undermine the trust in security applications.
Additionally, the application of machine learning techniques in malware detection has been explored as a means to enhance the identification of new and evolving threats. By training models on vast datasets of both benign and malicious software, these systems strive to learn patterns and characteristics indicative of malware. However, the effectiveness of machine learning models is heavily dependent on the quality and comprehensiveness of the training data. Moreover, sophisticated attackers continuously develop new strategies to evade detection, including the use of adversarial machine learning techniques.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and techniques for enhancing the security against evolving malware threats.

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 system for malware detection and classification, comprising an array of specialized units and modules, each designated with a specific function to enhance the system's capability in identifying and categorizing malicious software. Said system initiates its operation through an initialization unit responsible for configuring the operational state, ensuring that said system is prepared to process and analyze incoming data with utmost efficiency. In an embodiment, said initialization unit is further configured to validate input data integrity before configuring the operational state of said system, thereby enhancing the reliability and accuracy of the analysis to be conducted.
In an embodiment, a dynamic analysis module is included within said system, tasked with the reception and analysis of sample files. This module produces analyzed reports from said sample files, playing a pivotal role in the identification and understanding of potential malware. This dynamic analysis module is further configured to execute sample files in a controlled environment, simulating real-world interactions to gather comprehensive insights into the behavior of the files under scrutiny.
In an embodiment, a dataset module containing data for model training and malware assessment is incorporated. This module serves as a foundational element in the system's ability to learn and adapt to new malware signatures and patterns. Said dataset module further includes a feature extraction unit designed to identify and extract distinctive characteristics from the data, thereby improving the efficiency and effectiveness of model training.
In an embodiment, a machine learning training module within a Weka Framework is utilized for generating a predictive model from the dataset module. This training module leverages multiple machine learning algorithms to enhance the accuracy and reliability of the predictive model, ensuring that the system is equipped with the latest advancements in machine learning technology for malware detection.
In an embodiment, a detection module applies the predictive model to evaluate files and determine the presence of malware. This module represents the system's frontline defense against malicious software, utilizing the predictive model to scrutinize files for potential threats. The detection module further includes a real-time monitoring unit to apply the predictive model to streaming data, allowing for immediate detection of malware as it attempts to infiltrate the system.
In an embodiment, a classification module is responsible for categorizing files identified as malware by the detection module, resulting in a classified data output. This module employs a hierarchical classification scheme to categorize different levels of malware severity, providing a nuanced understanding of the threat landscape. Additionally, said classification module is further adapted to update the predictive model in response to new malware findings, ensuring that the system remains effective against evolving threats.
In an embodiment, the system is augmented with a reporting unit, configured to generate comprehensive reports detailing the analysis performed by the classification module. These reports offer valuable insights into the nature and severity of detected malware, facilitating informed decision-making and enabling effective response strategies to mitigate the impact of malicious software.
A method for malware detection and classification comprises configuring the operational state of a system, receiving and analyzing sample files through a dynamic analysis module, and maintaining a dataset for model training within a dataset module, which includes feature extraction for improved efficiency. A predictive model is generated using a machine learning training module within a Weka Framework, leveraging multiple algorithms for enhanced accuracy. This model is applied by a detection module to evaluate files and determine malware presence, incorporating real-time monitoring for immediate threat detection. Files identified as malware are categorized by a classification module using a hierarchical scheme and updating the model with new findings. This comprehensive approach ensures a robust, accurate, and adaptable system for protecting against malware threats, embodying a nuanced and sophisticated strategy for cybersecurity enhancement.

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 malware detection and classification, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method (200) for malware detection and classification, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a flow chart for malware detection and classification, 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 system (100) for malware detection and classification, in accordance with the embodiments of the present disclosure. The system (100) comprises various modules each designed to perform specific functions within the malware detection and classification process.
The initialization unit (102) within the system (100) for malware detection and classification serves a pivotal role in preparing the system for operational readiness. This preparation is crucial as it directly impacts the efficiency and effectiveness of subsequent modules within the system. Specifically, the initialization unit (102) configures the operational state of the system by establishing initial parameters and settings that dictate how the system will function. These settings can include, but are not limited to, configuring network settings for receiving sample files, setting thresholds for analysis sensitivity, and initializing the system's internal databases for logging and reporting purposes. By performing these tasks, the initialization unit (102) ensures that the system is in a state of readiness to accurately and effectively process and analyze files for malware detection. This includes preparing the system to interface with external systems or databases, optimizing performance parameters to suit the operational environment, and ensuring that all components of the system are synchronized in their operations. The operational state set by the initialization unit (102) is foundational, as it enables the system to function within the designed parameters, thus ensuring that the detection and classification of malware are performed with the highest levels of accuracy and efficiency possible. Without this initial configuration and preparation, the system might not operate as intended, leading to potential gaps in malware detection and classification capabilities.
The dynamic analysis module (104), as an integral component of the system (100), undertakes the critical task of analyzing sample files submitted to the system for evaluation. This module employs sophisticated techniques to execute and observe the behaviors of the files in a controlled environment. The objective is to identify any malicious activities or signatures that the files may exhibit during execution. This process involves monitoring the sample files for changes they might attempt to make to the system settings, files they try to modify or create, network communications they initiate, and any other operations that could indicate malicious intent. The dynamic analysis is thorough, ensuring that even the most sophisticated malware, which might only reveal its malicious nature under specific conditions or after certain actions, is identified. Once the analysis is complete, the dynamic analysis module (104) compiles detailed reports that summarize the behaviors observed, providing a comprehensive overview of each file's actions and its potential threat level. These reports are vital for the accurate detection and classification of malware, as they contain the evidence needed to ascertain the nature of the files analyzed. Through this rigorous analysis, the dynamic analysis module (104) significantly contributes to the system's ability to protect against malware by identifying threats before they can cause harm, thus maintaining the integrity and security of the operational environment.
The dataset module (106) houses an extensive collection of data essential for the training of machine learning models and the assessment of malware. This data is comprised of a wide range of examples, including known malware signatures, benign file characteristics, and behavioral patterns associated with malicious and non-malicious software. The diversity and comprehensiveness of the data within the dataset module (106) are critical, as they directly influence the accuracy and effectiveness of the predictive models developed by the system. The data is continuously updated to include new malware variants and benign software examples, ensuring that the machine learning models remain effective in the face of evolving malware threats. The dataset module (106) facilitates the training of predictive models by providing a rich source of information against which the models can learn and adapt. This learning process involves analyzing the data to identify patterns and characteristics that distinguish malicious software from non-malicious software. By training on a diverse and comprehensive dataset, the predictive models developed are capable of recognizing a wide array of malware types, including new and previously unseen variants. The role of the dataset module (106) in providing this foundational data is indispensable, as it ensures that the machine learning models are equipped with the knowledge needed to accurately detect and classify malware, thereby enhancing the overall security posture of the operational environment.
Within the system (100), the machine learning training module (108), operating under the Weka Framework, is tasked with the development of predictive models that are central to the system's capability to detect and classify malware. The Weka Framework, known for its robust suite of machine learning algorithms and tools, provides a versatile environment for model development. The machine learning training module (108) leverages this environment to process the data from the dataset module (106), applying sophisticated algorithms to learn from the characteristics, behaviors, and patterns present in the data. This learning process is iterative, involving the refinement of the model through continuous adjustment of parameters and evaluation of model performance against known outcomes. The objective is to develop a model that can accurately predict whether a file is malicious or benign based on its characteristics and behaviors as observed during analysis. The predictive model thus generated is a result of extensive training and optimization, ensuring it possesses a high degree of accuracy in malware detection. This accuracy is crucial, as it directly impacts the system's ability to effectively identify and respond to potential threats. The machine learning training module (108) also ensures that the predictive model is adaptable, enabling it to update its knowledge base as new data becomes available, thereby maintaining its effectiveness over time. The development of predictive models within the Weka Framework by the machine learning training module (108) is a complex process that underscores the system's advanced capabilities in utilizing machine learning for the purpose of enhancing cybersecurity measures.
The detection module (110) plays a crucial role in the operational efficacy of the system (100) by applying the predictive model to evaluate files for the presence of malware. Upon receiving files for analysis, the detection module (110) utilizes the predictive model to scrutinize the files' characteristics and behaviors, comparing them against the learned patterns of malicious and benign software. This comparison enables the detection module (110) to ascertain the likelihood of a file being malware. The process involves a detailed assessment of each file, taking into consideration various attributes and actions that have been identified as indicative of malware. Should the evaluation process identify a file as potentially malicious, the detection module (110) flags the file for further action, such as quarantine, deletion, or detailed analysis by security experts. This module is essential for the proactive identification of threats, allowing for immediate action to be taken to mitigate potential damage. The effectiveness of the detection module (110) is heavily reliant on the accuracy of the predictive model developed by the machine learning training module (108). Through precise detection capabilities, the detection module (110) significantly contributes to the system's overall goal of maintaining a secure and malware-free operational environment. Its ability to rapidly and accurately evaluate files is a key defense mechanism against the proliferation of malware, safeguarding sensitive information and critical systems from unauthorized access and damage.
The classification module (112) complements the detection efforts by categorizing files identified as malware, enhancing the system's (100) response strategy to cybersecurity threats. Once the detection module (110) flags a file as malware, the classification module (112) undertakes the task of categorizing the malware based on predefined criteria, such as type of malware (e.g., virus, worm, trojan), severity of threat, and potential impact. This categorization is critical for determining the appropriate response to the identified malware. By classifying the malware, the classification module (112) facilitates a more targeted and effective approach to mitigating the threat it poses. The process of classification not only aids in the immediate response to threats but also contributes to the broader understanding of malware trends and behaviors, which is invaluable for the ongoing refinement of detection and prevention strategies. The classified data output produced by the classification module (112) provides a detailed overview of the nature and characteristics of the malware, enabling security professionals to make informed decisions regarding countermeasures and protections.
In an embodiment, the initialization unit (102) of the system for malware detection and classification is further configured to validate input data integrity before configuring the operational state of said system. This validation process is crucial for ensuring that the data entering the system is accurate, complete, and free from corruption. By implementing such a validation mechanism, the initialization unit (102) enhances the reliability and effectiveness of the system's subsequent malware detection and classification processes. The validation of input data integrity involves checking for data consistency, completeness, and security, thereby preventing the analysis of compromised or incomplete data that could lead to inaccurate detection results. This added layer of data validation by the initialization unit (102) serves as a preliminary safeguard, ensuring that only verified data is processed by the system, thereby maintaining the system’s integrity and operational efficiency.
In an embodiment, the dynamic analysis module (104) of the system is further configured to execute sample files in a controlled environment to simulate real-world interactions. This enhancement enables the dynamic analysis module (104) to observe the behavior of sample files under conditions that closely mimic their potential behavior in an actual operational environment. By executing sample files in such a controlled setting, the dynamic analysis module (104) can accurately identify malicious behaviors that may not be evident through static analysis alone. This approach provides a more comprehensive analysis of the sample files, enabling the detection of sophisticated malware that employs evasion techniques or activates under specific conditions. The ability to simulate real-world interactions significantly improves the system's capability to identify and classify malware based on behavioral analysis, thereby enhancing the overall security measures implemented by the system.
In an embodiment, the dataset module (106) of the system further includes a feature extraction unit designed to identify and extract distinctive characteristics from the data for improved model training efficiency. The incorporation of a feature extraction unit within the dataset module (106) enables the system to efficiently process and analyze the vast amount of data available for model training. By identifying and extracting key features from the data, the feature extraction unit enhances the machine learning training module's (108) ability to focus on the most relevant information for malware detection and classification. This selective focus on distinctive characteristics improves the efficiency of model training, leading to the development of more accurate and effective predictive models. The feature extraction unit thus plays a critical role in optimizing the data preparation process for machine learning, contributing to the overall effectiveness of the malware detection and classification system.
In an embodiment, the machine learning training module (108) within the Weka Framework is further adapted to utilize multiple machine learning algorithms to enhance the accuracy of the generated predictive model. This adaptation allows for a more flexible approach to model development, as the use of multiple algorithms can address the diverse and complex nature of malware threats. By leveraging a variety of machine learning algorithms, the machine learning training module (108) can develop predictive models that are more robust and capable of detecting a wider range of malware with greater precision. The flexibility to utilize multiple algorithms enables the system to tailor the predictive model to the specific characteristics of the malware being analyzed, thereby significantly improving the model's effectiveness in accurately identifying and classifying malware threats.
In an embodiment, the detection module (110) of the system further includes a real-time monitoring unit designed to apply the predictive model to streaming data. This addition enables the detection module (110) to analyze data in real-time, allowing for the immediate identification and mitigation of malware threats as they occur. The real-time monitoring unit enhances the system's capability to protect against malware by providing continuous surveillance of the operational environment. By applying the predictive model to streaming data, the detection module (110) can detect and respond to malware threats more quickly, reducing the potential damage caused by malware infections. The inclusion of a real-time monitoring unit thus significantly strengthens the system's defensive capabilities, ensuring a higher level of security and protection against malware.
In an embodiment, the classification module (112) of the system is further configured to employ a hierarchical classification scheme to categorize different levels of malware severity. This hierarchical approach allows for a more nuanced classification of malware, enabling the system to distinguish between varying degrees of threat posed by identified malware. By categorizing malware into different levels of severity, the classification module (112) facilitates a more targeted and effective response to malware threats. This method of classification provides valuable insights into the nature and potential impact of the malware, allowing security professionals to prioritize their response efforts based on the severity of the threat. The implementation of a hierarchical classification scheme by the classification module (112) thus enhances the system's ability to manage and mitigate malware infections more efficiently.
In an embodiment, the classification module (112) of the system is further adapted to update the predictive model in response to new malware findings. This adaptability ensures that the predictive model remains effective and relevant in the face of evolving malware threats. By incorporating new malware findings into the predictive model, the classification module (112) enables the system to continuously improve its detection and classification capabilities. This ongoing update process ensures that the predictive model adapts to the changing landscape of malware threats, maintaining the system's effectiveness in identifying and classifying new and emerging malware. The ability to update the predictive model in response to new findings is crucial for ensuring that the system remains at the forefront of malware detection and classification technology.
In an embodiment, the system further comprises a reporting unit configured to generate comprehensive reports detailing the analysis performed by the classification module (112). The reporting unit plays a vital role in documenting and communicating the findings of the malware detection and classification process. By generating detailed reports, the reporting unit provides a thorough overview of the malware threats identified and classified by the system. These reports serve as a valuable resource for security professionals, offering insights into the nature of the threats and the effectiveness of the response strategies employed. The comprehensive reports produced by the reporting unit enable informed decision-making and strategy formulation, contributing to the overall effectiveness of the malware detection and classification efforts. The inclusion of a reporting unit thus enhances the system's capability to not only detect and classify malware but also to document and share findings in a manner that supports ongoing security operations.
FIG. 2 illustrates a method (200) for malware detection and classification, in accordance with the embodiments of the present disclosure. At step (202) the process initiates with the configuration of an operational state for a malware detection and classification system, setting up the necessary parameters for the system to function effectively in detecting and classifying malware. At step (204) sample files are then received and subjected to thorough analysis by a dynamic analysis module, designed to examine their behavior for indicators of malicious activity, contributing to the generation of detailed analyzed reports. At step (206) a dataset module is maintained, filled with extensive data crucial for training machine learning models and for conducting comprehensive malware assessments, ensuring the system is equipped with current and relevant information. At step (208) utilizing the data within the dataset module, a machine learning training module, operating within a Weka Framework, generates a predictive model. This model is crafted using sophisticated machine learning algorithms to predict malware presence accurately. At step (210) the predictive model is subsequently applied by a detection module to evaluate files for malware, using learned data patterns to accurately identify potential threats and determine the presence of malware within the analyzed files. At step (212) files flagged as containing malware undergo categorization by a classification module, resulting in a classified data output. This module organizes the malware based on predefined criteria, enhancing the system’s response to detected threats.
FIG. 3 illustrates a flow chart for malware detection and classification, in accordance with the embodiments of the present disclosure. The process begins with an initialization phase, where the system is prepared for operation. Sample files are then processed through a Cuckoo Sandbox, an automated system that analyzes files in a secure environment to detect any malicious behavior. The outcomes from the Cuckoo Sandbox are compiled into analyzed reports by a Report Handler, which aggregates the findings into a coherent format. This information is subsequently used alongside a comprehensive macro dataset for training within the Weka Framework, a software that provides a suite of machine learning algorithms for developing predictive models, herein referred to as the 'Macro Model'. Once the Macro Model is established, it is then employed in a detection phase, again within the Weka Framework, to evaluate files for potential malware. If malware is detected, the flow progresses to a classification phase where the identified malware is categorized, and a classified data output is generated. This output provides detailed insights into the nature of the malware, assisting in strategizing appropriate countermeasures. The process concludes once all files have been appropriately analyzed, evaluated, and categorized, thereby maintaining the system's integrity and safeguarding against malware threats.
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 malware detection and classification, comprising:
an initialization unit (102) for configuring the operational state of said system;
a dynamic analysis module (104) configured to receive and analyze sample files, and to produce analyzed reports therefrom;
a dataset module (106) containing data for model training and malware assessment;
a machine learning training module (108) within a Weka Framework for generating a predictive model from said dataset module;
a detection module (110) for applying said predictive model to evaluate files, and to determine the presence of malware; and
a classification module (112) for categorizing files identified as malware by said detection module, resulting in a classified data output.
The system of claim 1, wherein said initialization unit (102) is further configured to validate input data integrity before configuring the operational state of said system.
The system of claim 1, wherein said dynamic analysis module (104) is further configured to execute sample files in a controlled environment to simulate real-world interactions.
The system of claim 1, wherein said dataset module (106) further includes a feature extraction unit to identify and extract distinctive characteristics from said data for improved model training efficiency.
The system of claim 1, wherein said machine learning training module (108) within the Weka Framework is further adapted to utilize multiple machine learning algorithms to enhance the accuracy of the generated predictive model.
The system of claim 1, wherein said detection module (110) further includes a real-time monitoring unit to apply said predictive model to streaming data.
The system of claim 1, wherein said classification module (112) is further configured to employ a hierarchical classification scheme to categorize different levels of malware severity.
The system of claim 1, wherein said classification module (112) is further adapted to update said predictive model in response to new malware findings.
The system of claim 1, further comprising a reporting unit configured to generate comprehensive reports detailing the analysis performed by said classification module (112).
A method for malware detection and classification, comprising:
configuring an operational state of a malware detection and classification system; receiving and analyzing sample files to produce analyzed reports via a dynamic analysis module; maintaining a dataset for model training and malware assessment within a dataset module;
generating a predictive model from said dataset module using a machine learning training module within a Weka Framework;
applying said predictive model to evaluate files and determine the presence of malware using a detection module; and
categorizing files identified as malware resulting in a classified data output with a classification module.

SYSTEM AND METHOD FOR MALWARE DETECTION AND CLASSIFICATION

The present disclosure provides a system for malware detection and classification, comprising an initialization unit for configuring the operational state of said system; a dynamic analysis module configured to receive and analyze sample files, and to produce analyzed reports therefrom; a dataset module containing data for model training and malware assessment; a machine learning training module within a Weka Framework for generating a predictive model from said dataset module; a detection module for applying said predictive model to evaluate files, and to determine the presence of malware; and a classification module for categorizing files identified as malware by said detection module, resulting in a classified data output.
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 system (100) for malware detection and classification, comprising:
an initialization unit (102) for configuring the operational state of said system;
a dynamic analysis module (104) configured to receive and analyze sample files, and to produce analyzed reports therefrom;
a dataset module (106) containing data for model training and malware assessment;
a machine learning training module (108) within a Weka Framework for generating a predictive model from said dataset module;
a detection module (110) for applying said predictive model to evaluate files, and to determine the presence of malware; and
a classification module (112) for categorizing files identified as malware by said detection module, resulting in a classified data output.
The system of claim 1, wherein said initialization unit (102) is further configured to validate input data integrity before configuring the operational state of said system.
The system of claim 1, wherein said dynamic analysis module (104) is further configured to execute sample files in a controlled environment to simulate real-world interactions.
The system of claim 1, wherein said dataset module (106) further includes a feature extraction unit to identify and extract distinctive characteristics from said data for improved model training efficiency.
The system of claim 1, wherein said machine learning training module (108) within the Weka Framework is further adapted to utilize multiple machine learning algorithms to enhance the accuracy of the generated predictive model.
The system of claim 1, wherein said detection module (110) further includes a real-time monitoring unit to apply said predictive model to streaming data.
The system of claim 1, wherein said classification module (112) is further configured to employ a hierarchical classification scheme to categorize different levels of malware severity.
The system of claim 1, wherein said classification module (112) is further adapted to update said predictive model in response to new malware findings.
The system of claim 1, further comprising a reporting unit configured to generate comprehensive reports detailing the analysis performed by said classification module (112).
A method for malware detection and classification, comprising:
configuring an operational state of a malware detection and classification system; receiving and analyzing sample files to produce analyzed reports via a dynamic analysis module; maintaining a dataset for model training and malware assessment within a dataset module;
generating a predictive model from said dataset module using a machine learning training module within a Weka Framework;
applying said predictive model to evaluate files and determine the presence of malware using a detection module; and
categorizing files identified as malware resulting in a classified data output with a classification module.

SYSTEM AND METHOD FOR MALWARE DETECTION AND CLASSIFICATION