The ubiquitous presence of Android smartphones exposes users to an ever-expanding arsenal of malware threats. Existing detection methods often struggle with false positives and limited adaptability to emerging threats...
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With Androids dominant position within the current smartphone OS, increasing number of malware applications pose a great threat to user privacy and security. Classification algorithms that use a single feature usually...
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With Androids dominant position within the current smartphone OS, increasing number of malware applications pose a great threat to user privacy and security. Classification algorithms that use a single feature usually have weak detection performance. Although the use of multiple features can improve the detection effect, increasing the number of features increases the requirements of the operating environment and consumes more time. We propose a fast Android malware detection framework based on the combination of multiple features: FAMD (Fast Android Malware Detector). First, we extracted permissions and dalvik opcode sequences from samples to construct the original feature set. Second, the dalvik opcodes are preprocessed with the N-Gram technique, and the FCBF (Fast Correlation-Based Filter) algorithm based on symmetrical uncertainty is employed to reduce feature dimensionality. Finally, the dimensionality-reduced features are input into the CatBoost classifier for malware detection and family classification. The dataset DS-1, which we collected, and the baseline dataset Drebin were used in the experiment. The results show that the combined features can effectively improve the detection accuracy of malware that can reach 97.40% on Drebin dataset, and the malware family classification accuracy can achieve 97.38% Compared with other state-of-the-art works, our framework achieves higher accuracy and lower time consumption.
Today, mobile devices are beginning to be used in every aspect of life. In addition to being able to perform financial transactions such as banking and shopping, mobile devices can also store personal information such...
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ISBN:
(纸本)9781538615010
Today, mobile devices are beginning to be used in every aspect of life. In addition to being able to perform financial transactions such as banking and shopping, mobile devices can also store personal information such as pictures / videos on these platforms, and important information about the current surroundings of the phone, such as location / sound, can be obtained. Among the mobile platforms, the popularity of the Android operating system and its open source code make it the main target for malware developers. Today's antivirus software is not effective against malicious software that has been tampered with or encountered for the first time while it is effective against pre-existing threats because they are mostly signature-based. New threats need to be detected as quickly as possible, considering new-signatured versions of the same malware can be created quickly and easily with automatic tools. For this reason, researches based on machine learning and deep learning have been conducted in the last years. In this study deep learning methods, which have been tried to be used successfully in all areas of life in recent years, are tested in mobile malware detection. The opcodes of Android applications were grouped in groups of 2 and 3, their features were extracted, weights were optimized using stacked denoising auto encoder and classified by a multi-layered artifical neural network. As a result of the classification, harmful software was detected with an accuracy of 92.04%
Android mobile devices are widely used in recent years. Due to the openness of Android, applications with malicious behavior have more opportunities to get confidential information, which can cause property damage. Mo...
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ISBN:
(纸本)9783030000189;9783030000172
Android mobile devices are widely used in recent years. Due to the openness of Android, applications with malicious behavior have more opportunities to get confidential information, which can cause property damage. Most of current solutions are hard to detect these rapidly developing malicious applications with high accuracy. In this paper, a static malicious application detection method based on Sparse Bayesian Learning Algorithm and n-gram analysis is proposed to solve this problem.
In recent years, mobile technology and mobile-device have been rapidly developed. Since mobile devices collect and transmit large amounts of private information about users, malicious applications will pose a signific...
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In recent years, mobile technology and mobile-device have been rapidly developed. Since mobile devices collect and transmit large amounts of private information about users, malicious applications will pose a significant threat to the privacy and property security of the individual. Openness is a crucial factor why Android becomes the most popular mobile operate system, but it also results the Android system vulnerable to malware. In this paper, the n-gram opcode is employed to describe the applications, and then a static analysis method based on genetic algorithm and support vector machine is used to detect applications with malicious behaviors.
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