Summary Effectiveness of Static Analysis Against Android Malware arxiv.org
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This study explores the effect of obfuscation on Android malware detection using machine learning and suggests a strong detector.
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Key Points
- Obfuscation techniques are commonly used by malware authors to bypass static analysis-based malware detectors in Android.
- Some machine learning (ML) detection proposals have been developed that are resilient to obfuscation.
- The impact of specific obfuscation techniques on static analysis features used for ML malware detection in Android is assessed.
- Certain features remain valid for ML malware detection even in the presence of obfuscation.
- A robust ML malware detector for Android is proposed that outperforms current state-of-the-art detectors.
- Static analysis and dynamic analysis are two techniques used for data extraction in Android malware detection.
- Obfuscation is a security technique used to prevent code analysis by transforming the code of apps without altering their functionality.
- This study provides a comprehensive assessment of the impact of obfuscation techniques on static analysis features for ML malware detection in Android.
Summaries
19 word summary
This study examines the impact of obfuscation on machine learning malware detection in Android and proposes a robust detector.
60 word summary
This study explores the impact of obfuscation on static analysis features for machine learning (ML) malware detection in Android. Despite obfuscation, certain features remain valid for ML malware detection. The study proposes a robust ML malware detector for Android that outperforms current detectors. Obfuscation techniques can counteract the effectiveness of static analysis, but the proposed detector is robust against obfuscation.
137 word summary
This study examines the impact of obfuscation techniques on static analysis features for machine learning (ML) malware detection in Android. Despite the influence of obfuscation, certain features remain valid for ML malware detection. Based on these findings, a robust ML malware detector for Android is proposed that outperforms current state-of-the-art detectors. The rise of Android devices has led to an increase in malware targeting this operating system. To combat this, researchers have developed ML-based anti-malware solutions. Both dynamic and static analysis methods are valid for Android malware detection, but static analysis is more computationally efficient. However, obfuscation techniques can counteract the effectiveness of static analysis. This study comprehensively assesses the impact of obfuscation techniques on static analysis features for ML-based Android malware detection. The proposed ML-based Android malware detector is robust against obfuscation and outperforms current detectors.
510 word summary
This study examines the impact of obfuscation techniques on static analysis features used for machine learning (ML) malware detection in Android. The experiment evaluates how obfuscation affects different static analysis features across various tools. Despite the influence of obfuscation, certain features remain valid for ML malware detection. Based on these findings, a robust ML malware detector for Android is proposed that outperforms current state-of-the-art detectors.
The rise of Android devices has led to an increase in malware targeting this operating system. To combat this, researchers have developed ML-based anti-malware solutions. These algorithms analyze app data to classify apps as either goodware or malware. Data extraction for Android malware detection can be done through dynamic or static analysis. Dynamic analysis involves executing the app and monitoring its behavior, while static analysis involves inspecting the APK file. Both methods are valid, but static analysis is more computationally efficient. However, obfuscation techniques can counteract the effectiveness of static analysis.
Obfuscation is a technique that transforms app code to prevent code analysis. Both legitimate developers and malware authors use obfuscation. Some studies have shown that obfuscation hampers static analysis-based malware detection, while others propose feature extraction techniques that can identify obfuscated malware.
To address these limitations, this study comprehensively assesses the impact of obfuscation techniques on static analysis features for ML-based Android malware detection. The strength, validity, and detection potential of various static analysis features are analyzed when obfuscation is present. The study evaluates the impact of different obfuscation strategies and tools on these features, providing insights into their use for detecting obfuscated malware.
In conclusion, this study offers valuable insights into the impact of obfuscation techniques on static analysis features for Android malware detection. Certain features remain effective for ML-based malware detection despite obfuscation. The proposed ML-based Android malware detector outperforms current state-of-the-art detectors and is robust against obfuscation. The availability of the dataset and code used in the study promotes open science and reproducibility.
The study evaluates the effectiveness of static analysis features for detecting Android malware in the presence of obfuscation. Different obfuscation strategies are applied to Android apps using state-of-the-art tools, and the impact on various static analysis features is analyzed. Seven feature families are considered, including Permissions, Components, API functions, Opcodes, Strings, File Related, and Ad-hoc. The stability of these features when obfuscation is applied varies, with manifest-based features being the most stable.
The study also examines the differences in features obtained using different obfuscation tools. These differences depend on the tool used and are due to implementation peculiarities. The largest differences are observed for API function and Ad-hoc features.
The study then uses ML algorithms to assess the ability of static features to detect malware and their stability in the presence of obfuscation. In a clean environment, most feature families provide sufficient information for effective malware detection using ML algorithms, particularly API functions and Strings. However, File-Related features are found to be unsuitable.
The study investigates the sensitivity of ML algorithms to changes induced by feature vector obfuscation. Even small changes in feature vectors can significantly impact prediction.
628 word summary
This study examines the impact of obfuscation techniques on static analysis features used for machine learning (ML) malware detection in Android. The experiment evaluates how obfuscation affects different static analysis features across various tools. Despite the influence of obfuscation, certain features remain valid for ML malware detection. Based on these findings, a robust ML malware detector for Android is proposed that outperforms current state-of-the-art detectors.
The rise of Android devices has led to an increase in malware targeting this operating system. To combat this, researchers have developed ML-based anti-malware solutions. These algorithms analyze app data to classify apps as either goodware or malware. Data extraction for Android malware detection can be done through dynamic or static analysis. Dynamic analysis involves executing the app and monitoring its behavior, while static analysis involves inspecting the APK file. Both methods are valid, but static analysis is more computationally efficient. However, obfuscation techniques can counteract the effectiveness of static analysis.
Obfuscation is a technique that transforms app code to prevent code analysis. Both legitimate developers and malware authors use obfuscation. Legitimate developers use it to protect their code, while malware authors use it to hinder static analysis. Some studies have shown that obfuscation hampers static analysis-based malware detection, while others propose feature extraction techniques that can identify obfuscated malware. However, these studies have limitations in terms of reproducibility and experimental details.
To address these limitations, this study comprehensively assesses the impact of obfuscation techniques on static analysis features for ML-based Android malware detection. The strength, validity, and detection potential of various static analysis features are analyzed when obfuscation is present. The study evaluates the impact of different obfuscation strategies and tools on these features, providing insights into their use for detecting obfuscated malware. Based on the experimental results, a high-performing ML-based Android malware detector that is resilient against obfuscation is proposed. This detector surpasses current state-of-the-art detectors and can identify goodware and malware even in the presence of obfuscation. Additionally, the study provides a dataset of over 95K obfuscated Android apps, enabling researchers to test their malware detection proposals.
In conclusion, this study offers valuable insights into the impact of obfuscation techniques on static analysis features for Android malware detection. Certain features remain effective for ML-based malware detection despite obfuscation. The proposed ML-based Android malware detector outperforms current state-of-the-art detectors and is robust against obfuscation. The availability of the dataset and code used in the study promotes open science and reproducibility.
The study evaluates the effectiveness of static analysis features for detecting Android malware in the presence of obfuscation. Different obfuscation strategies are applied to Android apps using state-of-the-art tools, and the impact on various static analysis features is analyzed. Seven feature families are considered, including Permissions, Components, API functions, Opcodes, Strings, File Related, and Ad-hoc. The stability of these features when obfuscation is applied varies, with manifest-based features being the most stable.
The study also examines the differences in features obtained using different obfuscation tools. These differences depend on the tool used and are due to implementation peculiarities. The largest differences are observed for API function and Ad-hoc features. The selection of files to be transformed also affects the differences between tools, as some tools perform additional checks to avoid modifying certain content.
The study then uses ML algorithms to assess the ability of static features to detect malware and their stability in the presence of obfuscation. The RandomForests classification algorithm is used without parameter optimization. In a clean environment, most feature families provide sufficient information for effective malware detection using ML algorithms, particularly API functions and Strings. However, File-Related features are found to be unsuitable.
The study investigates the sensitivity of ML algorithms to changes induced by feature vector obfuscation. Even small changes in feature vectors can significantly impact prediction
1044 word summary
Malware authors often use obfuscation techniques to bypass static analysis-based malware detectors in Android. However, some machine learning (ML) detection proposals have been developed that are resilient to obfuscation. In this study, the impact of specific obfuscation techniques on static analysis features used for ML malware detection in Android is assessed. The experimental results show that obfuscation techniques affect different static analysis features to varying degrees across different tools. However, certain features remain valid for ML malware detection even in the presence of obfuscation. Based on these findings, a robust ML malware detector for Android is proposed that outperforms current state-of-the-art detectors.
The spread of Android devices has led to an increase in the amount of malware crafted for this operating system. As a result, researchers have developed anti-malware solutions based on ML algorithms. These algorithms are able to find patterns in app data that can be used to classify apps as either goodware or malware. The performance of ML algorithms depends on the quality and soundness of the data used to build the classifier. In the case of Android malware detection, data extraction can be performed using either dynamic or static analysis. Dynamic analysis involves executing the app in a controlled environment and logging traces that describe its behavior. Static analysis, on the other hand, involves inspecting the content of the app package file (APK). Both techniques are valid for extracting valuable data from apps, but static analysis is computationally cheaper. However, it can be counteracted by applying obfuscation techniques.
Obfuscation is a security through obscurity technique that aims to prevent code analysis by transforming the code of apps without altering their functionality. Obfuscation can be used by both legitimate software developers and malware authors. Legitimate developers use obfuscation to protect their code from being analyzed by third parties, while malware authors use it to prevent static analysis from obtaining meaningful information about the behavior of apps. Some studies have shown that obfuscation harms detectors that rely on static analysis features for malware detection, while others have proposed feature extraction techniques that enable successful identification of malware even when apps are obfuscated. However, these studies have limitations in terms of reproducibility, biased datasets, and lack of details about the experimental setups.
To address these limitations, this study presents a comprehensive assessment of the impact of obfuscation techniques on static analysis features for ML malware detection in Android. The study analyzes the strength, validity, and detection potential of a complete set of features obtained through static analysis when obfuscation is used. The impact of different obfuscation strategies and tools on static analysis features is evaluated, providing insights about the use of these features for malware detection in obfuscated scenarios. Based on the experimental results, a high-performing ML-based Android malware detector that is robust against obfuscation is proposed. The detector outperforms current state-of-the-art detectors and can identify goodware and malware despite the presence of obfuscation. The study also presents a novel dataset with more than 95K obfuscated Android apps, allowing researchers to test the robustness of their malware detection proposals.
In conclusion, this study provides valuable insights into the impact of obfuscation techniques on static analysis features for Android malware detection. The findings show that certain features remain valid for ML malware detection even in the presence of obfuscation. The proposed ML-based Android malware detector is robust against obfuscation and outperforms current state-of-the-art detectors. The availability of the dataset and code used in the study promotes open science and reproducibility.
The effectiveness of static analysis features for detecting Android malware in the presence of obfuscation was evaluated in this study. Different obfuscation strategies were applied to Android apps using state-of-the-art obfuscation tools, and the impact on various static analysis features was analyzed. Seven families of features were considered, including Permissions, Components, API functions, Opcodes, Strings, File Related, and Ad-hoc. The persistence of these features when obfuscation was applied varied, with the features obtained from the manifest of applications being the most stable.
The differences in features obtained using different obfuscation tools were also examined. It was found that the differences in features depended on the tool used and were due to implementation peculiarities. The largest differences were observed for API function and Ad-hoc features. The way in which files to be transformed were selected also affected the differences observed between tools. Some tools performed additional checks to avoid modifying certain content, while others did not, resulting in differences in the features obtained.
Machine learning (ML) algorithms were then used to evaluate the ability of static features to detect malware and their stability in the presence of obfuscation. The RandomForests classification algorithm was used without any parameter optimization. In a clean environment, most feature families provided enough information for effective malware detection using ML algorithms, particularly API functions and Strings. However, File-Related features were found to be unsuitable for this purpose.
The sensitivity of ML algorithms to changes induced by feature vector obfuscation was also investigated. It was found that even small changes in feature vectors could have a significant impact on prediction performance. Some feature families exhibited high fluctuations in the decisions made by the models, indicating greater sensitivity to changes introduced by obfuscation.
To address this issue, a robust malware detection model was proposed based on features that exhibited high insensitivity to changes and high accuracy with non-obfuscated apps. The selected features included Permissions, API functions, and Strings. A RandomForest classifier trained with these robust features outperformed state-of-the-art obfuscation-resilient detectors in both non-obfuscated and obfuscated scenarios.
The study also highlighted the limitations of static analysis features for detecting Android malware. It was found that some feature types, such as file-related features, were not effective in differentiating malware. Feature persistence was not the sole factor influencing the robustness of the detection model, and high insensitivity values were a more adequate indicator of robustness.
In conclusion, static analysis features can be effective for ML-based Android malware detection in the presence of obfuscation. Features that are both relevant and insensitive to changes can be used to build robust detection models. The proposed robust detection approach using Permissions, API functions, and Strings outperformed state-of-the-art obfuscation-resilient detectors. Future work could involve extending the analysis to additional obfuscation techniques and exploring richer app representations that integrate different static analysis data.