Black Box Analysis of Android Malware Detectors

Nellaivadivelu, Guruswamy

doi:10.31979/etd.uzt8-w8qh

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Detecting malicious code in the system has also been a focus. Datadriven hybrid data assimilation is presented by Nellaivadivelu et al [18] as a multilayer perceptron-based approach for capturing state variables in Decision Analysis (DA) situations. For the purposes of observation, context, and analysis, this research drew on the solver package and forecast model sources based on satellites optimised for software.…”

Section: Related Workmentioning

confidence: 99%

Investigation of Android Malware Using Deep Learning Approach

Raymond¹,

Raj²

2023

Intelligent Automation &Amp; Soft Computing

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In recent days the usage of android smartphones has increased extensively by end-users. There are several applications in different categories banking/finance, social engineering, education, sports and fitness, and many more applications. The android stack is more vulnerable compared to other mobile platforms like IOS, Windows, or Blackberry because of the open-source platform. In the Existing system, malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware. The attackers target the victim with various attacks like adware, backdoor, spyware, ransomware, and zero-day exploits and create threat hunts on the day-to-day basics. In the existing approach, there are various traditional machine learning classifiers for building a decision support system with limitations such as low detection rate and less feature selection. The important contents taken for building model from android applications like Intent Filter, Permission Signature, API Calls, and System commands are taken from the manifest file. The function parameters of various machine and deep learning classifiers like Nave Bayes, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Ada Boost, and Multi-Layer Perceptron (MLP) are done for effective results. In our proposed work, we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression, and rank the best features for detection of applications and classify as malicious or benign can be used as threat model by online antivirus scanners.

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Section: Related Workmentioning

confidence: 99%

Investigation of Android Malware Using Deep Learning Approach

Raymond¹,

Raj²

2023

Intelligent Automation &Amp; Soft Computing

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“…Due to the increasing number of malignant application in 2016, working on detection models of malwares has accelerated. The studies based on source code analysis in static analysis type [14][15][16], and Sandbox usage [17], client-server structure [18], behaviour based surveillance [19], API calls [20] monitoring researches in dynamic analysis were made. By using different approaches, it was tried to achieve more robust results and to increase the safety of mobile users.…”

Section: Related Workmentioning

confidence: 99%

K-Nearest Neighbour Classifier Usage for Permission Based Malware Detection in Android

Arslan

Yurttakal²

2020

iij

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ABSTRACT Android application platform is making rapid progress in these days. This development has made it the target of malicious application developers. This situation provides a numerical increase in malware apps, diversity in techniques, and rise of damage. Therefore, it is very critical to detect these software and escalation the security of mobile users. Static and dynamic analysis, behaviour scrutiny, machine learning methods are used to ensure security. In this study, K-nearest Neighbourhood (KNN) classifier, one of the machine learning methods, is used. Thus, it is aimed to detect malignant mobile software successfully and quickly. The tests is conducted with dataset includes 492 malware and 697 benign applications. In the proposed algorithm, neighbour number 5 and distance metric is preferred as Minkowski. 80% of dataset randomly selected is reserved for training and 20% for testing. As a result, while 94.1% accuracy is achieved, precision 91.2%, recall 92.7% recall and f1-measure is 92.4%. The high value obtained in f1-measure shows that the proposed model is successful in detecting both malware and benevolent software. The success of using KNN algorithm in classification of malicious apps in the Android has been demonstrated.

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Detection Success Assessment of Machine Learning Algorithms Through Manifest File Permissions Demanded by Malicious Android Wares

Aytaç Doğanay,

Bülbül

2023

2023 International Conference on Machine Learning and Applications (ICMLA)

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Black Box Analysis of Android Malware Detectors

Cited by 3 publications

References 6 publications

Investigation of Android Malware Using Deep Learning Approach

Investigation of Android Malware Using Deep Learning Approach

K-Nearest Neighbour Classifier Usage for Permission Based Malware Detection in Android

Detection Success Assessment of Machine Learning Algorithms Through Manifest File Permissions Demanded by Malicious Android Wares

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