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Added by
Ruitao Feng
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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With the proliferation of Android malware, the demand for an effective and efficient malware detection system is on the rise. The existing device-end learning based solutions tend to extract limited syntax features (e.g., permissions and API calls) to meet a certain time constraint of mobile devices. However, syntax features lack the semantics which can represent the potential malicious behaviors and further result in more robust model with high accuracy for malware detection. In this paper, we propose an efficient Android malware detection system, named SeqMobile, which adopts behavior-based sequence features and leverages customized deep neural networks on mobile devices instead of the server. Different from the traditional sequence-based approaches on server, to meet the performance demand, SeqMobile accepts three effective performance optimization methods to reduce the time cost. To evaluate the effectiveness and efficiency of our system, we conduct experiments from the following aspects 1) the detection accuracy of different recurrent neural networks; 2) the feature extraction performance on different mobile devices, 3) the detection accuracy and prediction time cost of different sequence lengths. The results unveil that SeqMobile can effectively detect malware with high accuracy. Moreover, our performance optimization methods have proven to improve the performance of training and prediction by at least twofold. Additionally, to discover the potential performance optimization from the SOTA TensorFlow model optimization toolkit for our approach, we also provide an evaluation on the toolkit, which can serve as a guidance for other systems leveraging on sequence-based learning approach. Overall, we conclude that our sequence-based approach, together with our performance optimization methods, enable us to detect malware under the performance demands of mobile devices.
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Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications provided by the official market, apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the security threats of attackers. Consequently, a last line of defense on mobile devices is necessary and much-needed. In this paper, we propose an effective Android malware detection system, MobiTive, leveraging customized deep neural networks to provide a real-time and responsive detection environment on mobile devices. MobiTive is a preinstalled solution rather than an app scanning and monitoring engine using after installation, which is more practical and secure. Original deep learning models cannot be directly deployed and executed on mobile devices due to various performance limitations, such as computation power, memory size, and energy. Therefore, we evaluate and investigate the following key points:(1) the performance of different feature extraction methods based on source code or binary code;(2) the performance of different feature type selections for deep learning on mobile devices;(3) the detection accuracy of different deep neural networks on mobile devices;(4) the real-time detection performance and accuracy on different mobile devices;(5) the potential based on the evolution trend of mobile devices specifications; and finally we further propose a practical solution (MobiTive) to detect Android malware on mobile devices.
According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively ~99% on the Android platform. Malware writers are applying stealthy mutations (obfuscations) to create malware variants, thwarting detection by signature based detectors. In addition, the plethora of more sophisticated detectors making use of static analysis techniques to detect such variants operate only at the bytecode level, meaning that malware embedded in native code goes undetected. A recent study shows that 86% of the most popular Android applications contain native code, making this a plausible threat. This paper proposes DroidNative, an Android malware detector that uses specific control flow patterns to reduce the effect of obfuscations, provides automation and platform independence, and as far as we know is the first system that operates at the Android native code level, allowing it to detect malware embedded in both native code and bytecode. When tested with traditional malware variants it achieves a detection rate (DR) of 99.48%, compared to academic and commercial tools DRs that range from 8.33% -- 93.22%. When tested with a dataset of 2240 samples DroidNative achieves a DR of 99.16%, a false positive rate of 0.4% and an average detection time of 26.87 sec/sample.
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. Traditional methods such as signature-based ones cannot defend users from an increasing number of new types of malware or rapid malware behavior changes. In this paper, we propose a new Android malware detection approach based on deep learning and static analysis. Instead of using Application Programming Interfaces (APIs) only, we further analyze the source code of Android applications and create their higher-level graphical semantics, which makes it harder for attackers to evade detection. In particular, we use a call graph from method invocations in an Android application to represent the application, and further analyze method attributes to form a structured Program Representation Graph (PRG) with node attributes. Then, we use a graph convolutional network (GCN) to yield a graph representation of the application by embedding the entire graph into a dense vector, and classify whether it is a malware or not. To efficiently train such a graph convolutional network, we propose a batch training scheme that allows multiple heterogeneous graphs to be input as a batch. To the best of our knowledge, this is the first work to use graph representation learning for malware detection. We conduct extensive experiments from real-world sample collections and demonstrate that our developed system outperforms multiple other existing malware detection techniques.
Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware analysis can be achieved through contrastive learning. We take the Android malware classification as an example to demonstrate our analysis. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and benign apps (or other types of malware). Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps are obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two widely used datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining 98.2% true positive rate on classifying 8,112 obfuscated malware samples.
Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.
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