Behavioral detection of malware on mobile handsets

By A. Bose, Xin Hu, Kang G. Shin, Taejoon Park. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2008

333 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fae437495416846ffb3b66a6f8ab5c6db7d9ae5f

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Citations in the corpus

Detection of Intrusions and Malware, and Vulnerability Assessment (2012, methodology): “, exfiltration cannot be detected by the approach described in [10].”
Soundcomber: A Stealthy and Context-Aware Sound Trojan for Smartphones (2011, background): “Specifically, a behavior-based malware detector [3] can pick up anomalous behavior such as regular CPU-intensive operations and heavy use of bandwidth , which could be associated with activities like performing an in-depth speech recognition and transmitting a large amount of phone recordings…”
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].”
“Andromaly”: a behavioral malware detection framework for android devices (2012, background): “Bose et al. (2008) HIDS Signature-based detection.”
The Evolution of Android Malware and Android Analysis Techniques (2017, unknown): “…and Hund 2008] 19 [Bl¨asing et al. 2010] 30 [Chen et al. 2013] 9 [Miettinen et al. 2006] 20 [Burguera et al. 2011] 31 [Backes et al. 2014] 10 [Bose et al. 2008] 21 [Andrus et al. 2011] 32 [Li et al. 2014] known as in-guest, or in-the-box, analysis as it relies on the Dalvik runtime (or the…”
Apposcopy: semantics-based detection of Android malware through static analysis (2014, background): “Another related approach is the behavioral detection technique described in [9].”
DroidMat: Android Malware Detection through Manifest and API Calls Tracing (2012, background): “Scalability: It is scalable, that is, it is linear in the size of the problem (i.e., the number of non-zeros in the input matrix).”
Cloud-enabled privacy-preserving collaborative learning for mobile sensing (2012, unknown): “, [35, 8, 10, 43, 4]) have used machine-learning classifiers, often generated by using training samples from a singleuser.”
A Survey on Security for Mobile Devices (2013, background): “To slow down the spread of mobile malware, the authors introduce an extended model (which incorporates the works of [47] and [48]), which is a Java-based engine that is independent from the platform.”
A Machine Learning Approach to Android Malware Detection (2012, methodology): “Other approaches use more traditional comparison with known malware (e.g. [4]), or other heuristics (e.g. [37]).”
On lightweight mobile phone application certification (2009, background): “Behavior signatures [4] considering temporal patterns of malicious behavior also show promise and may defend against malware variants with different binary signatures.”
Review: machine learning techniques applied to cybersecurity (2019, methodology): “Later, clustering algorithms were applied to classify software as malware [114–116], and SVM were tested in computer systems [117] as well as in Symbian-OS mobile handsets [118].”
Vetting undesirable behaviors in android apps with permission use analysis (2013, methodology): “A rich literature exists (see, e.g., [14,16, 21, 22, 33, 40, 49]) that focuses on solutions to construct effective behavior representations.”
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.”
Privacy Protection for Preventing Data Over-Collection in Smart City (2016, background): “Researcher have been studying mobile phone security for several years [2], [3], [4], especially malware [5], [6].”