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METHOD FOR DETECTING MALWARE IN ANDROID APPLICATIONS

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METHOD FOR DETECTING MALWARE IN ANDROID APPLICATIONS

ORDINARY APPLICATION

Published

date

Filed on 6 November 2024

Abstract

ABSTRACT A method (100) for detecting malware in Android applications. Further, the method comprising collecting a dataset of Android application features, including permissions and API calls. Further, the method (100) comprising the steps of applying a frequent pattern growth algorithm to extract co-existing features from the dataset. Further, the method (100) comprising the steps of training a machine learning model using the extracted features to classify the applications as either benign or malicious. Further, the method (100) comprising the steps of evaluating the trained model's performance using a test dataset to determine its accuracy, precision, and recall. Further, the method (100) comprising the steps of deploying the trained model in a real-time environment to continuously monitor and detect malware in newly installed Android applications.

Patent Information

Application ID202411084822
Invention FieldCOMPUTER SCIENCE
Date of Application06/11/2024
Publication Number46/2024

Inventors

NameAddressCountryNationality
B.VENU MADHAVI REDDYLOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia
KOMMANABOINA JEEVANA PRIYALOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia
Dr. ANSHU SHARMALOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia
KOMMISETTI GANESH MANIKANTALOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia
BURABATHULA LOKESH SAILOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia
SHIV DAYAL DHAKARLOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia
PASUPULETI CHARAN TEJA SRI SURYALOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia

Applicants

NameAddressCountryNationality
LOVELY PROFESSIONAL UNIVERSITYJALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411IndiaIndia

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of cybersecurity and, in particular, pertains to a method for detecting Android malware using machine learning techniques that analyze the co-existence of application features to enhance detection accuracy and minimize false positives.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The increasing reliance on mobile applications has led to a significant rise in the number of malicious software targeting Android devices. Malware can com , Claims:CLAIM:
1. A method (100) for detecting malware in Android applications, the method comprising the steps of:
collecting a dataset of Android application features, including permissions and API calls;
applying a frequent pattern growth algorithm to extract co-existing features from the dataset;
training a machine learning model using the extracted features to classify the applications as either benign or malicious;
evaluating the trained model's performance using a test dataset to determine its accuracy, precision, and recall; and
deploying the trained model in a real-time environment to continuously monitor and detect malware in newly installed Android applications.

2. The method (100) as claimed in claim 1, wherein the machine learning model is selected from the group consisting of random forest, support vector machine, and logistic regression.

Documents

NameDate
202411084822-COMPLETE SPECIFICATION [06-11-2024(online)].pdf06/11/2024
202411084822-DECLARATION OF INVENTORSHIP (FORM 5) [06-11-2024(online)].pdf06/11/2024
202411084822-DRAWINGS [06-11-2024(online)].pdf06/11/2024
202411084822-FIGURE OF ABSTRACT [06-11-2024(online)].pdf06/11/2024
202411084822-FORM 1 [06-11-2024(online)].pdf06/11/2024
202411084822-FORM-9 [06-11-2024(online)].pdf06/11/2024
202411084822-POWER OF AUTHORITY [06-11-2024(online)].pdf06/11/2024
202411084822-PROOF OF RIGHT [06-11-2024(online)].pdf06/11/2024
202411084822-REQUEST FOR EARLY PUBLICATION(FORM-9) [06-11-2024(online)].pdf06/11/2024

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