RiskRanker: scalable and accurate zero-day android malware detection

By Michael C. Grace, Yajin Zhou, Qiang Zhang, S. Zou, Xuxian Jiang. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2012

730 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/76f96ffa541f52f67469ac8fbd34b9dee9bf8ee8

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Citations in the corpus

MalDozer: Automatic framework for android malware detection using deep learning (2018, methodology): “The hybrid analysis methods (Yuan et al., 2014) (Grace et al., 2012) (Bhandari et al., 2015) (Vidas et al., 2014), (Lindorfer et al., 2014) combine between both analyses to improve detection accuracy, which costs additional computational cost.”
DroidDetector: Android Malware Characterization and Detection Using Deep Learning (2016, methodology): “DroidRanger [11] and RiskRanker [12] , two typical signature-based methods, try to characterize malware using speciﬁc patterns in the bytecode and Application Program Interfaces (API) calls.”
Execute This! Analyzing Unsafe and Malicious Dynamic Code Loading in Android Applications (2014, methodology): “used static analysis to detect some of the code-loading techniques discussed in this paper [18].”
The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “Multiple ﬂow analysis can also be used together to search for malicious background actions [Felt et al. 2011; Grace et al. 2012].”
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (2016, background): “RiskRanker [28] aims to identify zero-day Android malware by assessing potential security risks caused by untrusted apps.”
Apposcopy: semantics-based detection of Android malware through static analysis (2014, background): “For example, RiskRanker [24] performs several risk analyses to rank Android applications as high, medium, or low-risk.”
Structural detection of android malware using embedded call graphs (2013, background): “Several researchers have proposed to detect malware based on manually specified expert features, such as the permissions requested by applications [10, 14, 31] or the usage of specific API functions [16, 44].”
A measurement study of google play (2014, background): “Much work has focused on the security of Android applications and the presence of malware in Google Play, but this work has focused on possible compromises of user data and privacy on the Android devices themselves [20, 22, 25, 44].”
DroidChameleon: evaluating Android anti-malware against transformation attacks (2013, background): “Many works have been done towards discovery and characterization of smartphone malware [7, 15, 20, 23, 24, 29, 30].”
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, background): “The classification that dynamic code loading is as an indicator for maliciousness is also in line with observations in related work [33,57].”
WHYPER: Towards Automating Risk Assessment of Mobile Applications (2013, background): “Previous work has looked at permissions [2–5], code [6–10], and run-time behavior [11–13].”
Android Security: A Survey of Issues, Malware Penetration, and Defenses (2015, background): “Due to this, they are usually deployed off-device [30], [80], [90], [113].”
A Survey of Android Malware Detection with Deep Neural Models (2020, background): “RiskRanker was proposed in Reference [143] to efficiently search zero-day malware through triage based on an exhibition of any dangerous behavior, including privilege escalation and unauthorized transmission of messages.”
A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code (2015, background): “RiskRanker [7] and DroidRanger [15] focus on the task of finding malicious applications in various Android markets.”
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “Similarly, Stowaway [15] analyzes API calls to detect overprivileged applications and RiskRanker [21] statically identifies applications with different security risks.”
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, unknown): “D.2.4 [Software Engineering]: Software/Program Verification— Validation; D.4.6 [Operating Systems]: Security and Protection— Invasive software”
Significant Permission Identification for Machine-Learning-Based Android Malware Detection (2018, methodology): “For example, RISKRANKER [6] utilizes static analysis to discover malicious behaviors in Android apps.”
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, background): “RiskRanker [6] analyses dangerous behaviors of the applications.”
AppSpear: Bytecode Decrypting and DEX Reassembling for Packed Android Malware (2015, methodology): “Android malware detection is an active research area and many methods [7, 10,12,19,30] have been proposed to address the large-scale malware analysis issue.”
Vetting undesirable behaviors in android apps with permission use analysis (2013, background): “DroidRanger [60] and RiskRanker [36] were two Android malware detectors that relied on existing knowledge about malicious symptoms.”