Android Malware Detection Using Machine Learning and Reverse Engineering

Kędziora, Michał; Gawin, Paulina; Szczepanik, Michał; Jóźwiak, Ireneusz J.

doi:10.5121/csit.2018.81709

Cited by 6 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The highest accuracy achieved is 89%. Michal et al [11] use reverse engineering and applied three attribute selection algorithms and five classification algorithm. The result of naive Bayes and random forest comes out as the best.…”

Section: Related Workmentioning

confidence: 99%

Android Malware Analysis Using Machine Learning Classifiers

Jain¹,

Khandelwal²,

Jain³

et al. 2021

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Android Malware Analysis Using Machine Learning Classifiers

Jain¹,

Khandelwal²,

Jain³

et al. 2021

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

“…During first research by [18] use machine learning and reverse engineering technique to detect the android malware detection. The authors focused on static approach based on an automatic analysis of decompiled mobile application codes.…”

Section: Related Workmentioning

confidence: 99%

Android Malware Detection System using Machine Learning

Nasri¹

2020

IJATCSE

View full text Add to dashboard Cite

During the past year until now, the amount of malware targeting Android operating system has been rising dramatically. Therefore, Android malware detection are required to detect the malware before getting more serious. The static analysis examines the full code of application meticulously while dynamic analysis identifies the malware applications by monitoring it behaviors. This study proposed a malware detection system by using machine learning approach and aims to detect malware that has attacked Android operating system. In this research paper, the Android malware detection system are trained using five types of classifiers meanwhile WEKA is used for simulation process. The dataset used contains 10k of malware and 10k of benign. The outcomes presented Random Forest classifiers attained highest accuracy result, 89.36% compared to Naïve Bayes which 89.2%. TPR is viewed as detection rate which precisely predicted malware process while FPR is choosed as detection rate which inaccurately predicted normal as malware. To evaluate detection exactness which is good or bad, the area under the curve (AUC) have been applied through this study. The results show that Naïve Bayes has the lowest model complexity as it uses minimal time to build the model. Hence, it can be concluded that achieving reasonable accuracy and effectiveness in classifying unknown malware helps to determine the performance of the classifiers.

show abstract

“…While a customer wishes to acquire any app through the Google-based Play Store, the android app will be initially retrieved, and afterward, the customer is requested to apply it following agreeing on all rights. A person could indeed install any app unless they agree to all of the designer's authorization [21]- [23]. Hackers frequently request authorization to examine a user's webcam, voice, texts, and emails, as well as other personal data.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Android Malware Detection using Decision Tree and KNN

Anu.V

2022

IJRITCC

View full text Add to dashboard Cite

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

show abstract

Android Malware Detection Using Machine Learning and Reverse Engineering

Cited by 6 publications

References 4 publications

Android Malware Analysis Using Machine Learning Classifiers

Android Malware Analysis Using Machine Learning Classifiers

Android Malware Detection System using Machine Learning

A Hybrid Model for Android Malware Detection using Decision Tree and KNN

Contact Info

Product

Resources

About