Mobile Application Impersonation Detection Using Dynamic User Interface Extraction

Malisa, Luka; Kostiainen, Kari; Och, Michael; Čapkun, Srđjan

doi:10.1007/978-3-319-45744-4_11

Cited by 21 publications

(29 citation statements)

References 28 publications

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“…An impersonation attack might cause a serious breach of network security as it allows unauthorized or malicious users to access the network [41]. Some researchers proposed new detectors particularly for an impersonation attack [42], [43], [41], [44], [45] and [46]. Malisa et al [42] proposed a mobile application, which can detect both whole and partial user interface impersonations.…”

Section: Impersonation Attackmentioning

confidence: 99%

“…Some researchers proposed new detectors particularly for an impersonation attack [42], [43], [41], [44], [45] and [46]. Malisa et al [42] proposed a mobile application, which can detect both whole and partial user interface impersonations. We agree that the concept of extracting features captures important information to detect an attack.…”

Section: Impersonation Attackmentioning

confidence: 99%

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Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Aminanto

Choi

Tanuwidjaja

et al. 2018

IEEE Trans.Inform.Forensic Secur.

175

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The recent advances in mobile technologies have resulted in IoT-enabled devices becoming more pervasive and integrated into our daily lives. The security challenges that need to be overcome mainly stem from the open nature of a wireless medium such as a Wi-Fi network. An impersonation attack is an attack in which an adversary is disguised as a legitimate party in a system or communications protocol. The connected devices are pervasive, generating high-dimensional data on a large scale, which complicates simultaneous detections. Feature learning, however, can circumvent the potential problems that could be caused by the large-volume nature of network data. This study thus proposes a novel Deep-Feature Extraction and Selection (D-FES), which combines stacked feature extraction and weighted feature selection. The stacked autoencoding is capable of providing representations that are more meaningful by reconstructing the relevant information from its raw inputs. We then combine this with modified weighted feature selection inspired by an existing shallow-structured machine learner. We finally demonstrate the ability of the condensed set of features to reduce the bias of a machine learner model as well as the computational complexity. Our experimental results on a well-referenced Wi-Fi network benchmark dataset, namely, the Aegean Wi-Fi Intrusion Dataset (AWID), prove the usefulness and the utility of the proposed D-FES by achieving a detection accuracy of 99.918% and a false alarm rate of 0.012%, which is the most accurate detection of impersonation attacks reported in the literature.

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Section: Impersonation Attackmentioning

confidence: 99%

Section: Impersonation Attackmentioning

confidence: 99%

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Aminanto

Choi

Tanuwidjaja

et al. 2018

IEEE Trans.Inform.Forensic Secur.

175

View full text Add to dashboard Cite

show abstract

“…An automated screenshot mechanism is proposed in both [16,15] to find similarities between apps and determine whether a UI attack is being made by an app.…”

Section: Discussionmentioning

confidence: 99%

Knock-Knock: The Unbearable Lightness of Android Notifications

Patsakis

Alepis

2018

Proceedings of the 4th International Conference on Information Systems Security and Privacy

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Android Notifications can be considered as essential parts in Human-Smartphone interaction and inextricable modules of modern mobile applications that can facilitate User Interaction and improve User Experience. This paper presents how this well-crafted and thoroughly documented mechanism, provided by the OS can be exploited by an adversary. More precisely, we present attacks that result either in forging smartphone application notifications to lure the user in disclosing sensitive information, or manipulate Android Notifications to launch a Denial of Service attack to the users' device, locally and remotely, rendering them unusable. This paper concludes by proposing generic countermeasures for the discussed security threats.

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“…It re-executes all the unit test cases every time when a piece of code is changed. As an example, Hu et al [84] have applied unit testing to [26] CRASHSCOPE [27] MAMBA [28] Pretect [29] DroidMate [93] SSDA [30] TrimDroid [8] ERVA [9] Clapp et al [10] SAPIENZ [11] RacerDroid [31] Baek et al [94] DiagDroid [32] MobiPlay [95] MOTIF [33] DRUN [34] DroidDEV [96] GAT [35] Zhang et al [36] Jabbarvand et al [37] Qian et al [38] Ermuth et al [39] Cadage [97] Zhang et al [40] Zhang et al [41] dLens [42] Sonny et al [98] Packeviius et al [43] SIG-Droid [99] Knorr et al [44] TAST [100] IntentDroid [45] Griebe et al [101] Farto et al [46] Bielik et al [47] MobiGUITAR [48] AGRippin [102] Aktouf et al [49] THOR [103] AppAudit [50] Morgado et al [104] Hassanshahi et al [51] iMPAcT [52] Deng et al [53] Espada et al…”

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

“…Table 5 summarises the aforementioned test phases, where the most recurrently applied testing phase is system testing (accounting for nearly 80% of the selected publications), followed by unit testing and integration testing, respectively. [26] CRASHSCOPE [27] MAMBA [28] Pretect [29] DroidMate [93] SSDA [30] TrimDroid [8] ERVA [9] Clapp et al [10] SAPIENZ [11] RacerDroid [31] Baek et al [94] DiagDroid [32] MobiPlay [95] MOTIF [33] DRUN [34] DroidDEV [96] GAT [35] Zhang et al [36] Jabbarvand et al [37] Qian et al [38] Ermuth et al [39] Cadage [97] Zhang et al [40] Zhang et al [41] dLens [42] Sonny et al [98] Packeviius et al…”

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

Automated Testing of Android Apps: A Systematic Literature Review

et al. 2019

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Automated testing of Android apps is essential for app users, app developers and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also non-functional requirements are satisfied. In this paper, we aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation. 1 INTRODUCTION Android smart devices have become pervasive after gaining tremendous popularity in recent years. As of July 2017, Google Play, the official app store, is distributing over 3 million Android applications (i.e., apps), covering over 30 categories ranging from entertainment and personalisation apps to education and financial apps. Such popularity among developer communities can be attributed to the accessible development environment based on familiar Java programming language as well as the availability of libraries implementing diverse functionalities [1]. The app distribution ecosystem around the official store and other alternative stores such as Anzhi and AppChina is further attractive for users to find apps and organisations to market their apps [2]. Unfortunately, the distribution ecosystem of Android is porous to poorly-tested apps [3]-[5]. Yet, as reported • ξ The corresponding author.

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Mobile Application Impersonation Detection Using Dynamic User Interface Extraction

Cited by 21 publications

References 28 publications

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Knock-Knock: The Unbearable Lightness of Android Notifications

Automated Testing of Android Apps: A Systematic Literature Review

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