SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

Arshad, Saba; Shah, Munam Ali; Wahid, Abdul; Mehmood, Amjad; Song, Houbing; Yu, Hongnian

doi:10.1109/access.2018.2792941

Cited by 151 publications

(88 citation statements)

References 25 publications

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“…This may be the main reason behind the large number of malicious apps still on the loose in official app stores. Therefore, future research efforts should concentrate on clarifying how to efficiently join detection techniques into hybrid solutions with the purpose of increasing the subset of malware which can be detected, as proposed in previous work [44], but also offer actual detection improvement [45].…”

Section: Discussionmentioning

confidence: 99%

A Survey on Mobile Malware Detection Techniques

Kouliaridis

Barmpatsalou

Kambourakis

et al. 2020

IEICE Trans. Inf. & Syst.

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Modern mobile devices are equipped with a variety of tools and services, and handle increasing amounts of sensitive information. In the same trend, the number of vulnerabilities exploiting mobile devices are also augmented on a daily basis and, undoubtedly, popular mobile platforms, such as Android and iOS, represent an alluring target for malware writers. While researchers strive to find alternative detection approaches to fight against mobile malware, recent reports exhibit an alarming increase in mobile malware exploiting victims to create revenues, climbing towards a billion-dollar industry. Current approaches to mobile malware analysis and detection cannot always keep up with future malware sophistication [2], [4]. The aim of this work is to provide a structured and comprehensive overview of the latest research on mobile malware detection techniques and pinpoint their benefits and limitations.

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Section: Discussionmentioning

confidence: 99%

A Survey on Mobile Malware Detection Techniques

Kouliaridis

Barmpatsalou

Kambourakis

et al. 2020

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Then APK Auditor classifies an application as malicious if the calculated score exceeds the malware threshold limit. SAMADroid [8] proposes Android malware detection model based on the three different levels such as (1) static and dynamic analysis, (2) local and the remote host, and (3) machine learning intelligence. The static analysis features that SAMADroid are based on both the AndroidManifest.xml file (e.g., requested hardware components, requested permissions, and application components) and the detected API calls.…”

Section: Static Analysismentioning

confidence: 99%

What Static Analysis Can Utmost Offer for Android Malware Detection

Kabakuş

2019

ITC

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Malicious applications are widespread for Android despite the taken serious actions by the operating system. Static and dynamic analysis techniques are utilized to detect malware by identifying the signatures of malicious applications by inspecting both the resources and behaviors of malware, respectively. In this study, what static analysis can utmost offer to detect malware in Android ecosystem is discussed and experimented on commonly used datasets in the literature by proposing a novel Android malware detection approach based on static analysis techniques. Some novel static analysis features which are proved to be effective in terms of detecting malware in Android ecosystem and are underestimated by the related work in the literature are introduced by proving their effectiveness in this study. The experimental result shows that the proposed Android malware detection approach is very effective in terms of detecting Android malware. Each feature used by the proposed approach is evaluated by using different types of machine learning techniques in order to highlight its impact on detecting malware and inform the digital investigators. The accuracy of the proposed static analysis approach is calculated as high as 0.987 for 10,865 applications.

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“…Notwithstanding the fact that, some recent advances [18,19] used DNNs for filtering tasks and shown encouraging consequences, auxiliary information of users were modelled, for instance, in audio and images. From literature [20], the massive data generated in recent times is as a result of generations from multi-modal, multi-dimensional datasets from current Recommender Systems (Rss) Hybrid algorithms [21] as proposed in this paper and being represented are used to optimize real-world implementations of algorithms and have received significant interest in recent years and are increasingly used to solve real-world problems as propounded by [22]. These hybrid models could include combination of two or more algorithms such as particle swarm optimization (PSO) [23], matrix factorization [24], genetic algorithms (GA) [25] and other computational strategies like artificial intelligence or deep neural networks [26] including but not limited to fuzzy logic systems [27], simulation, sigmoid functions or MLP [28], radial basis functions [29], just to mention a few.…”

Section: Review Of Related Workmentioning

confidence: 99%

Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

Aboagye

Kumar

2019

Future Internet

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We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

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SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

Cited by 151 publications

References 25 publications

A Survey on Mobile Malware Detection Techniques

A Survey on Mobile Malware Detection Techniques

What Static Analysis Can Utmost Offer for Android Malware Detection

Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

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