A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

Kim, TaeGuen; Kang, BooJoong; Rho, Mina; Sezer, Sakir; Im, Eul Gyu

doi:10.1109/tifs.2018.2866319

Cited by 392 publications

(169 citation statements)

References 38 publications

(41 reference statements)

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“…TaeGuen Kim et al [16] proposed a multi-modal deep learning malware detection model, which extracts multiple feature types to reflect the attributes of the android application from various aspects and uses feature extraction methods based on presence or similarity to refine these features and to achieve effective feature representation in malware detection. It extracts seven types of static features: permissions, components, environment, strings, Dalvik opcode sequences, API call sequences, and shared library function opcode features.…”

Section: A Malware Detection Using Deep Learning Based On Static Anamentioning

confidence: 99%

Review of Android Malware Detection Based on Deep Learning

2020

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At present, smartphones running the Android operating system have occupied the leading market share. However, due to the Android operating system's open-source nature, Android malware has increased dramatically. Malware can steal user privacy and even maliciously charge fees and steal funds. It has posed a severe threat to cyberspace security because traditional detection methods have many limitations. With the widespread application of deep learning in recent years, the method of detecting Android malware using deep learning has gradually attracted widespread attention from scholars at home and abroad. Although scholars have researched Android malware detection using deep learning, there is currently a lack of a detailed and comprehensive introduction to malware detection's latest research results based on deep learning. In order to solve this problem, this study analyzes and summarizes the latest research results by investigating a large number of the latest domestic and international academic papers, summarizing malware detection architecture and detection schemes, and analyzing existing problems and challenges. This review will help researchers better understand the research status and future research directions in this field. INDEX TERMS Android, malware, deep learning, review.

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Section: A Malware Detection Using Deep Learning Based On Static Anamentioning

confidence: 99%

Review of Android Malware Detection Based on Deep Learning

2020

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“…The framework extracts sequence of API calls and protection levels as featured and builds the CNN based detection model. In [374], the authors have proposed a multi-model DL based malware detector for android systems. The proposed model is trained using 7 different features obtained by analyzing different files from the APK file.…”

Section: ) Deep Neural Network (Dnn)mentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

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“…Kim et al proposed a framework that uses similarity-based feature (e.g., permission, component, environmental etc.) extraction and employs multimodal deep learning for malware detection [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Adapting Text Categorization for Manifest based Android Malware Detection

Çoban¹,

Özel²

2019

csci

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Malware is a shorthand of malicious software that are created with the intent of damaging hardware systems, stealing data, and causing a mess to make money, protest something, or even make war between governments. Malware is often spread by downloading some applications for your hardware from some download platforms. It is highly probable to face with a malware while you try to load some applications for your smart phones nowadays. Therefore it is very important that some tools are needed to detect malware before loading them to the hardware systems. There are mainly three different approaches to detect malware: i) static, ii) dynamic, and iii) hybrid. Static approach analyzes the suspicious program without executing it. Dynamic approach, on the other hand, executes the program in a controlled environment and obtains information from operating system during runtime. Hybrid approach, as its name implies, is the combination of these two approaches. Although static approach may seem to have some disadvantages, it is highly preferred because of its lower cost. In this paper, our aim is to develop a static malware detection system by using text categorization techniques. To reach our goal, we apply text mining techniques like feature extraction by using bag-of-words, n-grams, etc. from manifest content of suspicious programs, then apply text classification methods to detect malware. Our experimental results revealed that our approach is capable of detecting malicious applications with an accuracy between 94.0% and 99.3%.

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A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

Cited by 392 publications

References 38 publications

Review of Android Malware Detection Based on Deep Learning

Review of Android Malware Detection Based on Deep Learning

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Adapting Text Categorization for Manifest based Android Malware Detection

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