Crowdroid: behavior-based malware detection system for Android

By Iker Burguera, Urko Zurutuza, S. Nadjm-Tehrani. 2011

1,164 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

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Citations in the corpus

Is data clustering in adversarial settings secure? (2013, background): “, spotting fast-flux domains in DNS traffic [24], gaining useful insights on tools and sources of attacks against Internet websites [25], detecting repackaged Android applications [16] and (Android) mobile malware [9], and even automatically generating signatures for anti-virus software to enable de”
A Comprehensive Review on Malware Detection Approaches (2020, methodology): “A new framework to obtain and analyze smartphone application activity is presented in [100].”
Android Security: A Survey of Issues, Malware Penetration, and Defenses (2015, methodology): “The results can be finally sent back to the device [81], [110].”
Machine Learning for Android Malware Detection Using Permission and API Calls (2013, background): “So AndroidManifest.xml provides firsthand information to understand the characteristics and security settings of the Apps.”
Evaluation of machine learning classifiers for mobile malware detection (2014, neutral): “Burguera et al. (2011) proposed Crowdroid, a dynamic analysissystemthatexaminesapplicationbehaviourtodetect malware in Android by monitoring system calls.”
Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing (2012, methodology): “Researchers have also developed many useful techniques and tools to detect the sensitive information leakage in mobile apps [3, 10, 12-16, 19, 35, 36], by using permission analysis (e.g. [3, 16]), static code analysis (e.g. [12]), network analysis (e.g. [35]), or dynamic flow analysis (e.g. [14]).”
PScout: analyzing the Android permission specification (2012, background): “There is a large body of security research on permissionbased systems [15,16] and in Android security [6,7,8,11,12, 13, 18].”
A Survey on Security for Mobile Devices (2013, background): “[116] presents a framework to dynamically analyze applications behavior to detect malware on Android by collecting system traces from several real users through crowdsourcing and a central server.”
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets (2012, background): “Malware detection onmobile devices The second area of related work includes systems [29, 30, 33, 43, 45, 49, 52] to detect malware on mobile devices.”
You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020, methodology): “In traditional approaches, static analysis [1], [2], [68] and dynamic analysis [69], [70], [71] have been used to analyze and detect malware.”
Machine Learning Based Automotive Forensic Analysis for Mobile Applications Using Data Mining (2015, background): “[13] describe the design and usage of a structure to tell apart conceivably vindictive applications in light-weight of authorizations asked for by Android applications.”
Mobile-sandbox: having a deeper look into android applications (2013, methodology): “[3] I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani.”
A Machine Learning Approach to Android Malware Detection (2012, background): “Others [6], [36] monitor system calls and attempt to detect unusual system call patterns.”
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, positive): “Crowdroid [42] made a first step towards the use of dynamic analysis results for Android malware detection by performing k-means clustering based on system call invocation counts.”
Detection of Intrusions and Malware, and Vulnerability Assessment (2012, background): “Malware can be placed on dedicated hardware such as video cards and network interface cards to attack the host platform [30,31,11].”
Poisoning Complete-Linkage Hierarchical Clustering (2014, background): “, malware) such as computer viruses or worms [3, 4], and even identifying repackaged Android applications and Android mobile malware [5, 6].”
The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “As apps can only interact with the hardware via system calls, system call-centric analysis has been implemented for Windows devices [Becher and Freiling 2008; Hwang et al. 2008] and Android devices [Burguera et al. 2011; Grace et al. 2012; Tam et al. 2015b].”
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “Similarly, the methods Crowdroid [4], DroidMat [36], Adagio [20], MAST [5], and DroidAPIMiner [1] analyze features statically extracted from Android applications using machine learning techniques.”
A survey of the use of crowdsourcing in software engineering (2017, methodology): “Many previous studies have demonstrated that crowdsourc- 1415 ing is an e↵ective way to augment software security (Arellano et al., 2010; Burguera et al., 2011; Sharifi et al., 2011; Agarwal and Hall, 2013; Papamartzivanos et al., 2014; Saxe et al., 2014): Arellano (2010) proposed crowdsourced web a”
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.”