On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection

Zhao, Yanjie; Li, Li; Wang, Haoyu; Cai, Haipeng; Bissyandé, Tegawendé F.; Klein, Jacques; Grundy, John

doi:10.1145/3446905

Cited by 51 publications

(31 citation statements)

References 74 publications

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“…rough this process, it classifies samples and regresses a binary division into classification, continuation, or numerical types. As a result of this study, the DT algorithm was used the fourth most [32,37,39,44,46,47,53,56,58,59,62,64,65,68,71,72,75,76,78,85,86,92,96,98,101,111,[120][121][122][123][124][125][126][127][128][129][130][131][132][133][134].…”

Section: 31mentioning

confidence: 99%

A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

Kim

Lee

et al. 2022

Security and Communication Networks

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With the deployment of the 5G cellular system, the upsurge of diverse mobile applications and devices has increased the potential challenges and threats posed to users. Industry and academia have attempted to address cyber security challenges by implementing automated malware detection and machine learning algorithms. This study expands on previous research on machine learning-based mobile malware detection. We critically evaluate 154 selected articles and highlight their strengths and weaknesses as well as potential improvements. We explore the mobile malware detection techniques used in recent studies based on attack intentions, such as server, network, client software, client hardware, and user. In contrast to other SLR studies, our study classified the means of attack as supervised and unsupervised learning. Therefore, this article aims at providing researchers with in-depth knowledge in the field and identifying potential future research and a framework for a thorough evaluation. Furthermore, we review and summarize security challenges related to cybersecurity that can lead to more effective and practical research.

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Section: 31mentioning

confidence: 99%

A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

Kim

Lee

et al. 2022

Security and Communication Networks

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“…What's more, attackers continue to update their fraud techniques to bypass protection software as well as well-trained machine learning models in order to victimize users and businesses. In front of the increasing diiculty of Android malware defenses, it is non-trivial to construct a robust and transparent defense model or system only by traditional machine learning techniques [191].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Android Malware Defenses: A Systematic Literature Review

Liu

Tantithamthavorn

et al. 2022

ACM Comput. Surv.

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Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing deep learning techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on deep learning approaches for Android malware defenses exists. In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014-2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.

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“…Many other researchers [7,30,44] also point out the misalignment between code comments and natural user queries, and report it as a threat to the validity of their approaches. As mentioned in [31,57], improving the quality of the training data is still a research opportunity for machine learning, including DL-based code search models. Considering that there are still plenty of comments close to actual user queries and naturally paired with high-quality code snippets, a promising solution is to filter out the noisy ones.…”

Section: Introductionmentioning

confidence: 99%

On the Importance of Building High-quality Training Datasets for Neural Code Search

Sun¹,

Liu²,

Du³

et al. 2022

Preprint

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The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived. A large corpus of high-quality query and code pairs is demanded to establish a precise mapping from the natural language to the programming language. Due to the limited availability, most widely-used code search datasets are established with compromise, such as using code comments as a replacement of queries. Our empirical study on a famous code search dataset reveals that over one-third of its queries contain noises that make them deviate from natural user queries. Models trained through noisy data are faced with severe performance degradation when applied in real-world scenarios. To improve the dataset quality and make the queries of its samples semantically identical to real user queries is critical for the practical usability of neural code search. In this paper, we propose a data cleaning framework consisting of two subsequent filters: a rule-based syntactic filter and a model-based semantic filter. This is the first framework that applies semantic query cleaning to code search datasets. Experimentally, we evaluated the effectiveness of our framework on two widely-used code search models and three manually-annotated code retrieval benchmarks. Training the popular DeepCS model with the filtered dataset from our framework improves its performance by 19.2% MRR and 21.3% Answer@1, on average with the three validation benchmarks. CCS CONCEPTS• Software and its engineering → Reusability.

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On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection

Cited by 51 publications

References 74 publications

A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

Deep Learning for Android Malware Defenses: A Systematic Literature Review

On the Importance of Building High-quality Training Datasets for Neural Code Search

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