Deep Android Malware Detection

McLaughlin, Niall; Rincón, Jesús Martínez del; Kang, BooJoong; Yerima, Suleiman Y.; Miller, Paul; Sezer, Sakir; Safaei, Yeganeh; Trickel, Erik; Zhao, Ziming; Doupé, Adam; Ahn, Gail-Joon

doi:10.1145/3029806.3029823

Cited by 387 publications

(251 citation statements)

References 22 publications

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“…Convolutional Neural Networks (CNNs), a specific DL technique, have grown in popularity in recent times leading to major innovations in computer vision [6]- [8] and Natural Language Processing [9], as well as various niche areas such as protein binding prediction [10], [11], machine vibration analysis [12] and medical signal processing [13]. Whilst their use is still under-researched in cybersecurity generally, the application of CNNs has advanced the state-of-the-art in certain specific scenarios such as malware detection [14]- [17], code analysis [18], network traffic analysis [4], [19]- [21] and intrusion detection in industrial control systems [22]. These successes, combined with the benefits of CNN with respect to reduced feature engineering and high detection accuracy, motivate us to employ CNNs in our work.…”

Section: Introductionmentioning

confidence: 99%

Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

Doriguzzi-Corin

Millar

Scott-Hayward

et al. 2020

IEEE Trans. Netw. Serv. Manage.

225

105

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Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called LUCID, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain LUCID's DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, LUCID matches existing stateof-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments.

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Section: Introductionmentioning

confidence: 99%

Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

Doriguzzi-Corin

Millar

Scott-Hayward

et al. 2020

IEEE Trans. Netw. Serv. Manage.

225

105

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“…( Yuan et al, 2016 ) in all other metrics, while utilizing more samples for the experiments. DL-Droid also outperforms Maldozer ( Karbab et al, 2017 ), Deep4MalDroid ( Hou et al, 2016 ), AutoDroid ( Hou et al, 2017 ) and the CNN approach presented in ( McLaughlin et al, 2017 ). It is interesting to note that, just like in Deep4MalDroid and Auto-Droid, the number of the optimum hidden layers for DL-Droid is three.…”

Section: Comparison Of the Performance Of The Deep Learning Classifiementioning

confidence: 88%

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

Alzaylaee

Yerima

Sezer

2020

Computers & Security

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314

172

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a b s t r a c tThe Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,0 0 0 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.

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“…Analysis [17], [112], [235]- [266] [73], [97], [187], [267]- [291] Mobility Analysis [227], [292]- [310] User Localization [272], [273], [311]- [315] [111], [316]- [334] Wireless Sensor Networks [335]- [346], [346]- [356] Network Control [186], [293], [357]- [368] [234], [368]- [403] Network Security [185], [345], [404]- [419] [223], [420]- [429], [429]- [436] Signal Processing [378], [380], [437]- [444] [322], [445]- [458] Emerging Applications For each domain, we summarize work broadly in tabular form, providing readers with a general picture of individual topics. Most important works in each domain are discussed in more details in text.…”

Section: App-level Mobile Datamentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Deep Android Malware Detection

Cited by 387 publications

References 22 publications

Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

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

Deep Learning in Mobile and Wireless Networking: A Survey

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