Android Malware Detection Using Hybrid-Based Analysis &amp; Deep Neural Network

Hadiprakoso, Raden Budiarto; Buana, I Komang Setia; Pramadi, Yogha Restu

doi:10.1109/icoiact50329.2020.9332066

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…In [95] static and dynamic analysis techniques were used to compile API calls, system commands, manifest permissions, and intent attributes of benign and malicious applications from APK files. To obtain the highest performance value with different configuration values of deep learning hyperparameters, the highest accuracy rate of 99% was obtained because of experimental studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Protecting Android Devices From Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

Bayazit,

Sahingoz,

Dogan

2023

IEEE Access

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Advancements in microelectronics have increased the popularity of mobile devices like cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, broad device support, customizability, and integration with the Google ecosystem, has become the leading operating system for mobile devices. While Android's openness brings benefits, it has downsides like a lack of official support, fragmentation, complexity, and security risks if not maintained. Malware exploits these vulnerabilities for unauthorized actions and data theft. To enhance device security, static and dynamic analysis techniques can be employed. However, current attackers are becoming increasingly sophisticated, and they are employing packaging, code obfuscation, and encryption techniques to evade detection models. Researchers prefer flexible artificial intelligence methods, particularly deep learning models, for detecting and classifying malware on Android systems. In this survey study, a detailed literature review was conducted to investigate and analyze how deep learning approaches have been applied to malware detection on Android systems. The study also provides an overview of the Android architecture, datasets used for deep learning-based detection, and open issues that will be studied in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Protecting Android Devices From Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

Bayazit,

Sahingoz,

Dogan

2023

IEEE Access

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“…In [29], the authors proposed a hybrid detection method that performs dynamic detection on these suspicious applications which are detected by static detection. In [30], the authors compiled static and dynamic features from benign and malicious applications such as API call sequence, system command, manifest permission, and intent. en, they used a deep neural network to classify applications.…”

Section: Related Workmentioning

confidence: 99%

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

Shao

Xiong

Cai

2021

Security and Communication Networks

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As the number of Android malware applications continues to grow at a high rate, detecting malware to protect the system security and user privacy is becoming increasingly urgent. Each malware application belongs to a specific family, and there is a gap in the number of malware families. The accuracy of detection can be improved if malware family information is well utilized and certain strategies are adopted to balance the variability among samples. In addition, the performance of a base classifier is limited. If an ensemble classifier or an ensemble method can be adopted, the detection effect can be further improved. Therefore, this paper proposes a novel malware family-based bagging algorithm for Android malware detection, called FB2Droid, to perform malware detection. First, five features are extracted from the Android application package. Then, the relief feature selection algorithm is used for feature selection. Next, we designed two different sampling strategies based on different families of malware to alleviate the sample imbalance in the dataset. Combined with the two sampling strategies, the traditional bagging algorithm is improved to integrate the classifier. In the experiment, several classifiers were used to evaluate the proposed scheme. The experimental results show that the proposed sampling strategy and the improved bagging algorithm can effectively improve the detection accuracy of these classifiers.

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Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

Yunmar,

Kusumawardani,

Widyawan

et al. 2024

IEEE Access

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Over the last decade, numerous research efforts have been dedicated to countering malicious mobile applications. Given its market share, Android OS has been the primary target for most of these apps. Researchers have devised numerous solutions to protect Android devices and their users, categorizing them into static and dynamic approaches. Each of these approaches has its own advantages and disadvantages. The hybrid approach aims to combine the benefits of both. This study closely examines the hybrid solutions proposed between 2012 and 2023, highlighting their strengths and limitations. The objective of this study is to provide a comprehensive review of existing research on Android malware detection using a hybrid approach. Our review identifies several issues related to hybrid detection approaches, including datasets, feature utilization and selection, working environments, detection order mechanisms, integrity of the detection step, detection algorithms, and the use of automated input generation. Key findings of this study include: (i) the majority of studies have not adequately addressed on-device detection and have overlooked the importance of system usability, (ii) many studies rely on outdated datasets that do not accurately represent the current threat landscape, (iii) there is a need for a methodology to detect zero-day attacks, and (iv) most research has not paid attention to the impact of automated input generation on malware behavior and code coverage. We also discuss some open issues and future directions on the aspect that will help to substantiate the hybrid approach study.INDEX TERMS Android malware, heuristic-based detection, hybrid approach.

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Android Malware Detection Using Hybrid-Based Analysis & Deep Neural Network

Cited by 5 publications

References 18 publications

Protecting Android Devices From Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

Protecting Android Devices From Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

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