A Deep Feature Fusion Method for Android Malware Detection

Ding, Yuxin; Hu, Jieke; Xu, Weipan; Zhang, Xiao

doi:10.1109/icmlc48188.2019.8949298

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…In the study [17], a CNN based model is proposed. Static analysis is performed by using API calls and Opcode sequences as features.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Malware Detection for Android Systems: A Comparative Analysis

2023

Teh. vjesn.

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Nowadays, cyber attackers focus on Android, which is the most popular open-source operating system, as main target by applying some malicious software (malware) to access users' private information, control the device, or harm end-users. To detect Android malware, security experts have offered some learning-based models. In this study, we developed an Android malware detection system that uses different machine\deep learning models by performing both dynamic analyses, in which suspected malware is executed in a safe environment for observing its behaviours, and static analysis, which examines a malware file without any execution on the Android device. The benefits and weaknesses of these models and analyses are described in detail in this comparative study, and directions for future studies are drawn. Experimental results showed that the proposed models gave better results than those in the literature, with 0.988 accuracy for LSTM on static analysis and 0.953 accuracy for CNN-LSTM on dynamic analysis.

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“…In the study [17], a CNN based model is proposed. Static analysis is performed by using API calls and Opcode sequences as features.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Malware Detection for Android Systems: A Comparative Analysis

2023

Teh. vjesn.

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“…The MobiDroid framework [104] is a deep learning-based real-time and fast detection system recommended for Android malware detection, is presented. The first part of MobiDroid, which consists of two parts, contains the feature extraction and the related learning model, while the second part transmits the feature vector of the applications downloaded from official and unofficial sources to the detection system.…”

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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“…DeepDroid is a comprehensive framework consisting of data gathering, feature selection, and machine learning stages. In [18], a CNN-based model leveraged static analysis with API calls and Opcode sequences as features, achieving an impressive 97.5% accuracy using the Drebin dataset.…”

Section: Journal Of Nanoscopementioning

confidence: 99%

Effectiveness Of Detecting Android Malware Using Deep Learning Techniques

Atif Raza Zaidi,

Tahir Abbas,

Hamza Zahid

et al. 2023

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The pervasive adoption of Android in the smartphone market has attracted the attention of malicious actors who continually exploit its open system architecture. A number of cybercriminals have been targeting Android in recent times due to its popularity. As a result of the increasing demand for smartphones, malicious users have recently been drawn to Android and taken advantage of its open system design to commit crimes. As Android has grown in popularity, attackers have been targeting it more. It is possible to gain access to data hidden from view using algorithms even though security measures have been implemented. An Android malware detection system based on machine-deep learning has been developed by utilizing dynamic analysis, in which suspected malware is executed in a secure environment in order to observe its behavior, as well as static analysis, in which malware files are examined without being executed on an Android device. As a result of our experimental results, our suggested models have a higher accuracy rate than industry standards, with a static accuracy rate of 99 and a dynamic accuracy rate of 98 for CNN-LSTM

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A Deep Feature Fusion Method for Android Malware Detection

Cited by 5 publications

References 8 publications

Deep Learning based Malware Detection for Android Systems: A Comparative Analysis

Deep Learning based Malware Detection for Android Systems: A Comparative Analysis

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

Effectiveness Of Detecting Android Malware Using Deep Learning Techniques

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