DL-Droid: Deep learning based android malware detection using real devices

Alzaylaee, Mohammed K.; Yerima, Suleiman Y.; Sezer, Sakir

doi:10.1016/j.cose.2019.101663

Cited by 332 publications

(182 citation statements)

References 30 publications

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“…This paper proposes a dynamic analysis system DL-Droid based on deep learning for Android malware detection, which uses a stateful input generation method to enhance code coverage to achieve high detection performance. In the real Android device, 31125 sample data are used for experiment instead of Android simulator, and the first 420 API calls, intents, and permission features are selected by information gain algorithm to achieve a 97.8% detection rate, which is better than the seven popular traditional machine learning classifiers [35].…”

Section: B Malware Detection Using Deep Learning Based On Dynamic Anmentioning

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: B Malware Detection Using Deep Learning Based On Dynamic Anmentioning

confidence: 99%

Review of Android Malware Detection Based on Deep Learning

2020

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“…ReLU helps to mitigate vanishing and exploding gradient issues [23]. It has been found to be more efficient in terms of time and cost for training huge data in comparison to classical non-linear activa- tion functions such as Sigmoid or Tangent functions [24]. A simplified view of our architecture is shown in Figure 2.…”

Section: + −mentioning

confidence: 99%

Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks

Yerima

Alzaylaee

2020

2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)

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Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.

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“…On Android, most of the approaches static analysis relying static features related to applications [4][5][6][7][8], dynamic analysis relying on features observed during their execution [9][10][11] and hybrid analysis combining the both [12][13][14]. Despite Google Play Protect provided by Google, to filter threats, there are still malicious applications carefully designed by bad people to have an impact on the security and privacy of users [5,15]. Aforementioned solutions therefore need additional knowledge.…”

Section: Introductionmentioning

confidence: 99%

CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme of Android Apps Based on Sentiment Analysis of Reviews

Tchakounté¹,

Pagore²,

Kamgang³

et al. 2020

Preprint

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To keep its business reliable, Google is concerned to ensure quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7.835.322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication and availability, while the remaining 77% has a polarity under 0.5. Developers should make lot of efforts in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications works without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated to existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.

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DL-Droid: Deep learning based android malware detection using real devices

Cited by 332 publications

References 30 publications

Review of Android Malware Detection Based on Deep Learning

Review of Android Malware Detection Based on Deep Learning

Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks

CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme of Android Apps Based on Sentiment Analysis of Reviews

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