BigBing: Privacy-Preserving Cloud-Based Malware Classification Service

Kucuk, Yunus; Patil, Nikhil; Zhan, Siyan; Yan, Guanhua

doi:10.1109/pac.2018.00011

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Kucuk et al [ 35 ] designed BigBing, which is a cloud-based malware architecture that respects individuals’ privacy while simultaneously employing a scalable big data approach that can scale efficiently with a voluminous number of users’ data. At the core of BigBing is a frontend gateway web interface that users can access to share a list of binary samples with the system.…”

Section: Resultsmentioning

confidence: 99%

Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review

Ehsan

Çatal

Mishra

2022

Sensors

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Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles.

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Section: Resultsmentioning

confidence: 99%

Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review

Ehsan

Çatal

Mishra

2022

Sensors

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“…Matching via general crypto or MPC [80,81,169,205,296,303,333,349] (total: 9) Client-side or metadata-based [177,211,366,374] (total: 4) AI via MPC or federated learning [64,122,333,334] (total: 4) Matching in Trusted Execution Environment [95,350,372] (total: 3)…”

Section: Spam Filteringmentioning

confidence: 99%

SoK: Content Moderation for End-to-End Encryption

Scheffler

Mayer

2023

PoPETs

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Popular messaging applications now enable end-to-end-encryption (E2EE) by default, and E2EE data storage is becoming common. These important advances for security and privacy create new content moderation challenges for online services, because services can no longer directly access plaintext content. While ongoing public policy debates about E2EE and content moderation in the United States and European Union emphasize child sexual abuse material and misinformation in messaging and storage, we identify and synthesize a wealth of scholarship that goes far beyond those topics. We bridge literature that is diverse in both content moderation subject matter, such as malware, spam, hate speech, terrorist content, and enterprise policy compliance, as well as intended deployments, including not only privacy-preserving content moderation for messaging, email, and cloud storage, but also private introspection of encrypted web traffic by middleboxes. In this work, we systematize the study of content moderation in E2EE settings. We set out a process pipeline for content moderation, drawing on a broad interdisciplinary literature that is not specific to E2EE. We examine cryptography and policy design choices at all stages of this pipeline, and we suggest areas of future research to fill gaps in literature and better understand possible paths forward.

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“…However, this method can't keep track of all files in the cloud in real-time. The authors of [182], [186] have proposed monitoring system parameters, such as API calls, file contents and permissions as features for detecting malware. However, these approaches might fail in detecting malware in the cloud if the malware can disguise its harmful activities.…”

Section: Cloud-based Malware Detectionmentioning

confidence: 99%