A Novel Dynamic Android Malware Detection System With Ensemble Learning

Feng, Pengbin; Ma, Jianfeng; Sun, Cong; Xu, Xinpeng; Ma, Yuwan

doi:10.1109/access.2018.2844349

Cited by 162 publications

(90 citation statements)

References 25 publications

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“…Ensemble learning is a combination of several basic models of machine learning. It gives the optimal solution to the problem [15,16]. In this paper, four different ensemble learning methods are used which are Random Forest [17,18], Bagging, Gradient Boosting and AdaBoost [18].…”

Section: B Ensemble Learning Methodsmentioning

confidence: 99%

Improving Malware Detection Classification Accuracy with Feature Selection Methods and Ensemble-based Machine Learning Methods

Latha*,

Mohanasundaram

2019

IJITEE

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Malware is evolving serious threats to internet security. The classification of malware is extremely crucial in recent days. The traditional models are failed to achieve to get effective accuracy rate and the machine learning models are the basic models that accomplish the task of classification in a certain way, but in recent decades malware attacks are very drastic and difficult to achieve zero-day attacks. To compete with new malware, ensemble methods are highly effective and give better results of accuracy. In this paper, we propose a framework that combines the exploit of both feature selection methods and ensemble learning classifiers and gives better results of classification. In the experimental results, we prove that this combination of methods gives better classification with high accuracy of 100% with the Random Forest ensemble classifier

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Section: B Ensemble Learning Methodsmentioning

confidence: 99%

Improving Malware Detection Classification Accuracy with Feature Selection Methods and Ensemble-based Machine Learning Methods

Latha*,

Mohanasundaram

2019

IJITEE

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“…Another important point is that the system does not need to compute too much to deploy on mobile devices. Hence, the system should adopt models (e.g., machine learning models) to estimate the malicious behavior in a short time [11]. Machine learning (ML) methods are part of the artificial intelligencebased system in which solutions are provided to improve the decision-making process [12].…”

Section: General Definitionmentioning

confidence: 99%

“…Another category of studies has suggested methods using ensemble learning that employ other algorithms such as decision tree, SVM and RF for malware detection. However, due to the simultaneous using of multiple algorithms, these methods have a high time complexity [11]. In some studies, a framework for detecting malware has been presented, which different classification methods such as SVM are applied in them [23].…”

Section: Motivation and Open Issuesmentioning

confidence: 99%

Similarity-based Android malware detection using Hamming distance of static binary features

Taheri

Ghahramani

Javidan

et al. 2020

Future Generation Computer Systems

147

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In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid based nearest neighbors (KMNN). In our proposed methods, we can trigger the alarm if we detect an Android app is malicious. Hence, our solutions help us to avoid the spread of detected malware on a broader scale. We provide a detailed description of the proposed detection methods and related algorithms. We include an extensive analysis to asses the suitability of our proposed similaritybased detection methods. In this way, we perform our experiments on three datasets, including benign and malware Android apps like Drebin, Contagio, and Genome. Thus, to corroborate the actual effectiveness of our classifier, we carry out performance comparisons with some state-of-the-art classification and malware detection algorithms, namely Mixed and Separated solutions, the program dissimilarity measure based on entropy (PDME) and the FalDroid algorithms. We test our experiments in a different type of features: API, intent, and permission features on these three datasets. The results confirm that accuracy rates of proposed algorithms are more than 90% and in some cases (i.e., considering API features) are more than 99%, and are comparable with existing state-of-the-art solutions. * Corresponding author Email addresses: r.taheri@sutech.ac.ir (Rahim Taheri), m.ghahramani@sutech.ac.ir (Meysam Ghahramani), r.javidan@sutech.ac.ir (Reza Javidan), mohammad.shojafar@ryerson.ca (Mohammad Shojafar),

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“…In Ref. 16, the authors proposed an e®ective dynamic analysis framework, called EnDroid, with the aim of implementing highly precise malware detection based on multiple types of dynamic behavior features. These features cover system-level behavior trace and common application-level malicious behaviors like personal information stealing, premium service subscription, and malicious service communication.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

Rana

Sung

2020

Vietnam J. Comp. Sci.

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Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.

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A Novel Dynamic Android Malware Detection System With Ensemble Learning

Cited by 162 publications

References 25 publications

Improving Malware Detection Classification Accuracy with Feature Selection Methods and Ensemble-based Machine Learning Methods

Improving Malware Detection Classification Accuracy with Feature Selection Methods and Ensemble-based Machine Learning Methods

Similarity-based Android malware detection using Hamming distance of static binary features

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

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