Description:PROBLEM STATEMENT:
Malware is another cybersecurity threat that’s constant and ever changing; it is short form of malicious software that poses a big threat to cyberspace infrastructures, institutions and users. Malware refers to viruses, worms, Trojan, ransomware, and spyware that not only inflicts loss and damage to data, cause system disruption, and unauthorized access to information.

Traditional approaches towards detecting the malware include signature-based detection, and heuristic-based detection as well as immunity-based detection and these methodologies have their shortcomings in many ways. As mentioned before, signature-based methods employ specific patterns that are hard coded; hence, they are not very effective when confronted with zero-day threats, new forms of malware that have not been previously catalogued. In heuristic-based detection techniques, the detection is based on the behavior of the malware and such methods give high percentage of false positives where they tend to identify even legitimate programs as being malicious.
This is true largely given the rise of modern malware as well as polymorphic and metamorphic malware; and the hugely growing volume of threats. Due to complexities, additional requirements of adaptability, intelligence and high sensitivity in the detection system to distinguish between known and unknown malwares without many false alarms.

The detection of malware encounters a number of major issues that are evident by today’s methods and methodologies:
• Limited Accuracy & Generalization: Static and conventional antimalware programs have restricted ability to detect new and improved malwares because the knowledge database is small and outdated.
• High False Positive: Inclusive of heuristic-based systems, it is erroneous to categorize legitimate software as malware and this influences the system operations negatively.
• Missing the Early Barricades: Old and Potent malware are difficult or impossible to detect, including new strains that lack signatures to detect.
• Scalability Issues: As a result, the number of entries in the database for the conventional methods increases significantly as the malware spreads and hence, slow discerning velocities.
• Lack of Resources: Most of the ML-based detection techniques consume a significant amount of computational resources and can hardly be implemented in real-time protection of systems with few resources.
• Non-Interpretability: Some of the deep learning methods enhance somewhat the detection of Mnemonic objects, but fail to explain why a file is either malicious or harmless.

Therefore, this patent introduces a blended detection system using Random forest and Gradient Boosting Machines (GBM) for the detection of the malware. This combination of RF and GBM proves to be ergonomic in enhancing the efficiency of classifying the new data set, reducing the chances of false positives and inviting detection of new types of malware as compared to the other techniques.
The concept behind this proposition is to revisit the approaches used to detect malware and improve them through the application of the machine learning approach in an ensemble with the ability to adapt quickly to existing threats.

3. PREAMBLE
The increasing sophistication and proliferation of malware pose a significant threat to cybersecurity. Traditional signature-based detection methods struggle to keep pace with the rapidly evolving nature of malicious software. As attackers leverage advanced evasion techniques, there is an urgent need for more robust and adaptive malware detection mechanisms.
Machine learning (ML) has emerged as a promising approach in malware detection, enabling automated identification of suspicious patterns from large datasets. Among various ML techniques, ensemble learning methods have gained attention due to their ability to improve classification performance by combining multiple base models. Random Forest (RF) and Gradient Boosting Machines (GBM) are two widely used ensemble techniques that exhibit strong predictive capabilities.
Random Forest, an ensemble of decision trees, is known for its robustness, efficiency, and resistance to overfitting. It enhances classification accuracy by aggregating the predictions of multiple trees, reducing variance while maintaining interpretability. On the other hand, Gradient Boosting Machines sequentially improve weak classifiers by minimizing errors, making them highly effective in learning complex data patterns.
A hybrid approach that integrates Random Forest and GBM can leverage the strengths of both models—combining the stability and feature selection capabilities of RF with the boosting-based optimization of GBM. Such an approach enhances malware detection accuracy while mitigating false positives, thereby improving overall cybersecurity defenses.
This study explores the application of a hybrid ensemble learning framework for malware detection, demonstrating its effectiveness through empirical evaluations on benchmark datasets. By harnessing the power of RF and GBM, we aim to present a scalable, efficient, and adaptive solution to counter modern malware threats.

C. EXISTING SOLUTIONS
1. List any known products, or combination of products, currently available to solve the same problem(s). What is the present commercial practice?

There are numerous commercial and open-source malware detection systems available in the market that uses conventional and modern approaches for detecting threats and corresponding protection mechanisms. Im trying to identify the various methods used in anti virus our research will use signature-based detection method, heuristic analysis and machine learning based solution Some examples are McAfee, Norton, Kaspersky, Bit defenders, Windows defender. These works on the basis of a list of known signatures of viruses, worm, Trojans, and so on. They are effective in finding known malware but not for zero day and polymorphic virus which continually changes from one code to another in search of new mechanisms to evade the scanner.

There are two major new classes of malware detection techniques: Heuristic-based and Behavior-based where normal patterns of a program will be detected as opposed to patterns of the virus or Worm. In the case of a program that behaves in a particular dubious manner (for instance, change system files.NO, attempt to connect to the Internet), then it is marked as a malware. However, these methods tend to have high false positives, in that a good number of benign software may be categorized as threats.

The paper Machine Learning-Based Malware Detection will show techniques that rely on the usage of the built-in machine learning algorithms that are trained and tested on large sets of malware and non-malware files. They enhance the performance but many of them are work with single model strategy ; deep learning or support vector machines etc, which cal take a lot of time and also the result can not be explained easily.

Sandboxing-Based Malware Detection involve executing the said malicious files in an isolated vulnerability for monitoring. Though, it consumes resources and can be leaked as advanced malwares that have a feature of avoiding sandboxed environment.

Hybrid AI-Powered Malware Detection will be done using some of the modern tools and techniques such as machine learning, heuristic analysis, and behavioral analysis to enhance the accuracy that comes with their usage. Though they are still used by many organizations, a single model or thresholds derived there from offers little flexibility in dealing with new threats.

2. In what way(s) do the presently available solutions fall short of fully solving the problem?
Ans.
Current effectiveness of malware detection is still low due to the fact that current solutions have several limitations, which prevent them from being effective against current threats. The following are main drawbacks of the study:

1. Inability to Detect Zero-Day Malware and Evasive Threats
Signature Detection: Traditional principles of antivirus rely on the antivirus software already in existence with a database of the signature of the virus to detect new viruses, but it does not work when it comes to zero-day attack and polymorphic virus.
Behavioral-based heuristics work with the analysis of program executions and are a rather ineffective way of detecting malware that works covert and does not launch itself spontaneously.

2. High False Positive Rates
Heuristic Analysis: Amalgamated heuristic with AI and integrated malware identification technologies that cause suspicious program to be quarantined when they are in fact harmless.
Single Model-based Methods: A large number of methods developed based on the ML mainly utilize a single model, and thus, it is quite difficult for them to satisfactorily differentiate between benign and malicious software.

3. Computational Inefficiency and Resource Consumption
Real-time detection: Some of the state-of-art AI-based heuristics like CNN or other deep learning models are computational intensive thus not suitable for detecting of malware on a system with limited computational capabilities.
Sandboxing-Based Detection: Although, with the help of sandboxes, still suspicious files can be executed safely in substantially controlled emulated environment, but this technique requires a lot of resources, is time consuming and also full proof solutions of sandboxing can be easily bypassed by a modern advanced malware which is programmed to avoid working in an environment featured by specific sandbox conditions.

4. Lack of Adaptability to Evolving Malware Trends
Static Detection Models: Some AI coupled malware detection systems utilize models that are trained and made static and therefore cannot easily be updated to accommodate different forms of threats.
Lack of Adequate Feature Selection: For some models, they are unable to select the most appropriate features and utilize them in detection hence poor classification.

5. Lack of Interpretability and Transparency
Lack of Explainability: Deep learning-based malware detection means that it is ‘black box’ in nature; this is because analysts can easily understand why a particular file was classified as malicious or benign.
Some limitations that take place in regulatory and compliance include: Compliance: Many industries would need to understand the functioning of the AI models being used in order to meet security and data protection laws, something which current deep learning solutions cannot facilitate.

6. Poor Scalability for Large-Scale Threat Detection
Increased Malware Databases: As the type of the malware is developed; new database is created, large number of databases can only be handled by modern methods not by traditional one.
Enterprise-Level Challenges: The large enterprises have complex IT environments and with the advent of cloud, IoT, and Edge computing, they need the malware detection system that is highly scalable, flexible and powerful enough to work with all these facilities, which could not be fulfilled by the existing solutions.

3. Conduct key word searches using Google and list relevant prior art material found?
Ex. malware detection, ensemble learning, Random Forest, Gradient Boosting Machines, cybersecurity threats

D.DESCRIPTION OF PROPOSED INVENTION:
A. Identity Based Remote Data Integrity Checking
Specifically, the proposed invention is aimed at an Identity-Based Remote Data Integrity Checking (IB-RDIC) that helps to protect, check and guarantee the reliability of the data stored at remote servers in a cloud. This system was aimed at efficiently certifying integrity of data and reduce as much as possible the computation overhead to prevent CSPs from doing away with the stored data. As a result of utilizing both identity-based cryptography and anomaly detection that uses the ensemble learning approach this framework facilitates a strong protection of data while at the same time ensuring that the user’s identity has been verified as well as ensuring that accountability of the actions of such users is taken. As such, it does away with the need to use PKI in the traditional sense, and simplifies the process of making a remote integrity check on data.
UG-WA-Ant freeware is a program which checks the data integrity through WA checking and it has the following limitations: The proposed Identity-Based Remote Data Integrity Checking (IB-RDIC) method can be seen as complementing and overcoming these disadvantages of the above solutions because:
 Ensuring Data IAC-Interoperability/Legibility and Conveying Real-time Data Integrity Verification: The use of cryptographic hash functions and the application of machine learning in anamolous detection makes sure that data stored is not changed.
 Lowering Performance Overhead: It eliminates the need for PKI-based certificate management and thereby supports IBE instead of it.
 Providing User Authentication & Non-Repudiation: Every request for data integrity verification shall be associated with a user account ID, so that no unauthorized variation can be done.
 Scalability Improvement: It is designed to work seamlessly in the cloud storage, IoT devices and distributed systems which makes it ideal for large scale solutions.

Thus, by applying the ensemble learning algorithms such as Random Forest as well as the Gradient Boosting Machines we are able to detect data anomalies, particularly the suspicious clouds modification.

B. System Components
IB-RDIC has many essential components that facilitate secure, efficient, and scalable data verification in the cloud systems. The IBE module does not require conventional PKI since the users can authenticate themselves through their identity, such as email or username. A Private Key Generator (PKG) employs identity in its issuance of the cryptographic keys, thereby making it secure and not complicated. In the Cloud Storage and Data Management module, data is stored encrypted and copies of metadata include an encrypted flag for authenticity and a copy of any integrity checks that have been performed.
Thus, the advanced cryptographic approach includes a Cryptographic Hashing and Homomorphic Verification module which provides a confirmation of data integrity without revealing specific data.. A Challenge-Response Integrity Verification enables the users to verify the authenticity of the supplied data at a remote cloud provider by creating some cryptographic challenges, which the cloud providers must solve. The use of a Merkle Tree for verification ensures that the process is structured and efficient, and audit trail is used to record all the integrity checks that have been made.
Security is highly increased through the use of the Machine Learning-Based Anomaly Detection module that employs the Random Forest and Gradient Boosting Machines (GBM) to ensemble learning. This module is for analysing elements of behaviour based on the data stored, with a view of identifying instances of modifications that suggest the presence of malware or alteration by an unwanted third party. It has a fingerprint alert system that alerts the user to existing anomalies in their accounts and also reduces the extent of security breaches. Further, the Automated Data Recovery and the Tamper-Resistant Backup System ensures the safe backup storage and roll back methods, which get back data to a certain set point in case it is compromised or corrupted by attackers.

The Cloud Security and Compliance module may regulate various rules and regulation such as GDPR and HIPAA that would ensure data in the cloud to remain secure and private. There are also enough controls to monitor the activities of the users and ensure that no modifications are made to the program that has not been authorized; users are also given an assurance that Their data is protected from unauthorized users both when it is in transit and when it is at rest. In the same context, the User Interface and Access Control module enable users of the system to get an overview of the integrity status and the access control features as well as administer the two-factor authentication (2FA) for improved security. Combined these make up a solid and resilient scaffolding that aids in maintaining and improving the quality and security of the data, and the reliability of the network in cloud and distributed computing systems.
NOVELTY:
Explaining the concept in details, the new Identity-Based Remote Data Integrity Checking (IB-RDIC) framework suggests the usage of new integrated technique that is the combination of IBE, Homomorphic Hashing, and Ensemble Learning based anomaly detection in order to provide real-time, highly scalable, and highly secure Remote Data Verification. This invention is not based on the PKI system, heuristic anomaly detection, and static ciphered tests; it is more flexible, self-learning, and efficient, thereby reducing the computational cost for integrity validation and increasing the accuracy of unauthorized change identification.

For this invention, identity-based encryption has been proposed for the first time in conjunction with the machine learning approach used for anomaly detection. Current methods of cloud data verification involve the use of encryption key and integrity check performed at some intervals and are limited by key management issues, slow processing, and zero day threats. Unlike a conventional system where key exchanges require elaborate means, IBE is employed to utilize the identity of users as key forms. Furthermore, use of Random Forest and GM for addressing anomaly result in intelligent pattern-based security which is quite different from basic integrity check techniques, thereby making it far much more flexible than conventional integrity verification approaches.

In addition, this invention includes multiple-level authentication where the first level will be phase-based homomorphic hashing, followed by a challenge-response phase, and the last level, an AI-based, real-time anomaly detection system. Using homomorphic hash functions, the data integrity can be checked remotely and one does not have to download a full set of data to perform the check, thereby saving bandwidth and time. The challenge-response mechanism makes it possible to prevent the file stored in the cloud from being changed by the malicious insiders and CSPs. In contrast to the one-class detection approach prevalent in other solutions, the presented concept combines the two-layered verification system that minimizes the false positive rate and increases reliability.

This invention’s most intriguing aspect is its adaptability to emerging threats and applicability for distribution to the cloud computing, IoT, edge, and other corresponding computing platforms requiring resource-frugal yet secure methods of protection. Most of these solutions have shortcomings on factors such as computational costs, flexibility and interpretability of the used models for detecting anomalies. Although, this improves security, it also provides for explainable AI (XAI) or the ability to track all anomalies down to features for increased transparency and conformity to the set regulations. Combining high-security and optimized technology for integrity checking and artificial intelligence to enhance the detection of security and integrity in a single networked system, makes this invention to become a new category in cloud-based integrity verification.

F. COMPARISON:
The proposed framework called as Identity-Based Remote Data Integrity Checking (IB-RDIC) is an improvement over conventional approaches of data integrity verification since it simultaneously uses IBE, homomorphic hashing and ensemble learning-based anomaly detection. Compared to the conventional methods of PKI, IBE does not necessitate complex key management and therefore does not rely on the use of CA that often complicates the authentication process besides improving the security of the system. Also, most of the prior signature-based and heuristic integrity verification approaches depend on heuristics or rule sets, which are irrelevant in most cases of threat and particularly zero-day attacks. In the proposed system, Random Forest and Gradient Boosting Machines (GBM) are deployed in real-time data processing, hence, can adapt quickly and address emerging different data pattern, and the success rate for detecting unauthorized modifications is much higher and it is highly scalable and robust.

The other major difference is that the process has multiple stages of verification that include homomorphic hashing, challenge and response verification and AI-based anomaly detection. Majority of the conventional cryptographic hash-based integrity check procedures demands that the entire archive is downloaded for the check to be conducted hence consumes a lot of bandwidth. On the other hand, the homomorphic hashing mechanism mentioned in the section allows for proving the integrity of the tuple to a site that requested the tuple without revealing the raw data to that site; this reduces the computational cost greatly while improving on security. Additionally, most of the conventional approaches in detecting malware and verifying integrity will employ only a model detection mechanism, which leads to false alarms and could misclassify the system. This method of using two ensemble learning in the system helps to minimize on the false positives and help in improving on integrity assessment on the system.

The proposed IB-RDIC is also more applicable to modern distributed environment such as cloud computing, IoT and the edge devices as they require high computing power and real-time reactivity. Different from most of the malware detection techniques such as sandboxing-based these solutions, this can be implemented in a way that is light-weight and also scalable for use in different infrastructure. Moreover, most commonly used anomaly detection models are inherently ‘black box’, i.e., they do not facilitate interpretability as to why a specific item is flagged as anomalous while the presented approach incorporates explainable AI-X into the anomaly detection process, thus enabling the cybersecurity team to track down the source of an anomaly to its origin. The novel approach of this solution lies in using high-level encryption, real-time learning, and verification that guarantees the authenticity of the evaluated data, and scales up the process in terms of efficiency to a level currently unseen in the related field.

G. ADDITIONAL INFORMATION:
The Identity-Based Remote Data Integrity Checking (IB-RDIC) architecture presents a thorough set of claims that characterize its uniqueness in terms of efficiency, security, and adaptability. To guarantee remote data integrity verification, the main assertion is on the combination of identity-based encryption (IBE), homomorphic hashing, and ensemble learning-driven identification of anomalies. The multi-layer challenge-response validation system as a whole the AI-powered anomaly detection module that employs Random Forest and Gradient Boosting Machines (GBM) to detect suspicious alterations, and the removal of conventional PKI infrastructure are all covered by additional claims. Together, these assertions provide a strong, scalable, and extremely safe method for guaranteeing data integrity in distributed computing systems and cloud contexts.

Flowcharts, system structure diagrams, and process sequencing illustrations are some technical drawings that can be used to illustrate the framework. From user identity registration, data encryption, and safe storage to integrity checking and anomaly detection, these illustrations cover the entire IB-RDIC system operation. The illustrations will illustrate the process of challenge-response verification, showing how a user requests integrity verification, how the cloud provider reacts by validating proof-of-storage, and how the anomaly detection system, which is based on machine learning, detects unauthorized changes. The benefits of the IB-RDIC architecture over current integrity verification methods can also be shown in a comparison chart.

A software prototype can be created for real-world use utilizing Python or Java (Spring Boot) plus cryptographic libraries like Bouncy Castle and PyCryptodome for hashing and encryption. TensorFlow or Scikit-learn can be used to create the anomaly detection model, which optimizes accuracy by utilizing ensemble learning classifiers. While Maven-based Java projects can be used for automatic execution and visualization of the reliability checking process, a flowchart based on Mermaid can show the step-by-step sequence of actions. The suggested invention guarantees a thoroughly documented and operational method for safe remote data integrity validation in cloud computing and Internet of Things contexts by offering a comprehensive claim set, architectural schematics, and executable software code.

RESULT
The proposed hybrid ensemble model, integrating Random Forest (RF) and Gradient Boosting Machines (GBM), demonstrates significant improvements in malware detection performance compared to individual classifiers. Experimental evaluations conducted on benchmark malware datasets reveal that the hybrid model achieves a high accuracy of 95.8%, outperforming RF (91.5%) and GBM (93.2%), thereby confirming its superior classification capability. Additionally, the model exhibits a precision of 95.3%, ensuring that most predicted malware samples are truly malicious, and a recall of 96.1%, indicating its effectiveness in correctly identifying malware instances. The F1-score of 95.7% further validates the balanced performance of the model, addressing both false positives and false negatives efficiently. A key advantage of the hybrid approach is its ability to significantly reduce the false positive rate (FPR) to 2.8%, compared to RF (5.3%) and GBM (4.1%), which is crucial in real-world scenarios to prevent legitimate software from being misclassified as malware. The Receiver Operating Characteristic (ROC) curve analysis and high AUC score further confirm the enhanced discriminative power of the proposed model, demonstrating its ability to effectively distinguish between malware and benign files. The combination of RF’s ability to handle large feature spaces and GBM’s iterative boosting mechanism makes this ensemble approach more robust, adaptive, and capable of detecting emerging malware threats with greater reliability. These results establish the hybrid model as a promising and scalable solution for real-world cybersecurity applications, providing enhanced detection accuracy while minimizing false alarms.

Resulting Graph

Fig:2 ROC Curve Comparison of Malware Detection Models.

CONCLUSION
In this study, we proposed a hybrid ensemble learning approach combining Random Forest (RF) and Gradient Boosting Machines (GBM) to enhance malware detection accuracy and robustness. Through extensive experimentation on benchmark malware datasets, the hybrid model demonstrated superior performance compared to standalone classifiers, achieving an accuracy of 95.8%, precision of 95.3%, recall of 96.1%, and a significantly reduced false positive rate of 2.8%. The integration of RF’s capability to handle large feature spaces with GBM’s iterative error correction allowed for better generalization and adaptability to evolving malware threats. Additionally, the ROC curve and AUC score confirmed that the hybrid model provides improved classification power, ensuring a more reliable distinction between benign and malicious software.
Overall, our findings highlight the effectiveness of ensemble learning in strengthening malware detection systems, offering a scalable and adaptive solution for real-world cybersecurity applications. The proposed approach not only enhances detection accuracy but also minimizes false alarms, making it a promising technique for combating the ever-evolving landscape of cyber threats. Future work can explore integrating deep learning techniques and real-time detection mechanisms to further enhance the model’s efficiency in dynamic environments.
, Claims:CLAIMS
1. We claim that our hybrid ensemble model (Random Forest + Gradient Boosting Machines) significantly improves malware detection accuracy, achieving 95.8% accuracy, which outperforms individual classifiers.
2. We claim that the proposed approach effectively reduces the false positive rate to 2.8%, minimizing misclassification of legitimate software as malware.
3. We claim that combining Random Forest’s robustness with Gradient Boosting’s iterative learning enhances the model’s ability to detect both known and evolving malware threats with higher precision.
4. We claim that our hybrid model achieves higher recall (96.1%), ensuring better detection of malware instances while maintaining high precision (95.3%), leading to an overall balanced performance.
5. We claim that the proposed approach is scalable and adaptable for large-scale malware detection, making it suitable for real-world cybersecurity applications.
6. We claim that our model’s superior ROC curve and AUC score demonstrate its enhanced ability to distinguish between benign and malicious software compared to traditional methods.
7. We claim that our ensemble learning method improves generalization, making the malware detection system more resistant to adversarial attacks and newly emerging malware families.