A survey on automated dynamic malware-analysis techniques and tools

By Manuel Egele, Theodoor Scholte, Engin Kirda, Christopher Krügel. CSUR, 2012

987 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d2ad315eb328dbe97f2a33ae297a30c8570136f4

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Citations in the corpus

A comparison of static, dynamic, and hybrid analysis for malware detection (2015, methodology): “Dynamic analysis requires that we execute the program, often in a virtual environment [18].”
TIP: Typifying the Interpretability of Procedures (2017, methodology): “A great example of this is a malware detection neural network [16] which was trained to distinguish regular code from malware.”
Deep neural network based malware detection using two dimensional binary program features (2015, methodology): “Traditionally, the two major approaches for malware detection can be roughly split based on the approach that is used to analyze the malware, either static and dynamic analysis (see review [12]).”
Efficient Detection of Zero-day Android Malware Using Normalized Bernoulli Naive Bayes (2015, background): “An elaborated survey on dynamic malwareanalysis techniques is presented in [5].”
Evaluation of machine learning classifiers for mobile malware detection (2014, neutral): “There are two means of analyzing malware: static and dynamic analysis (Egele et al. 2008).”
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey (2019, methodology): “(4) In 2011, Vomel et al. [17] published a survey about volatile memory forensic, however this technique was not covered in the 2012 survey [6].”
PeerRush: Mining for unwanted P2P traffic (2013, background): “It is common for security researchers to run botnet samples in a controlled environment to study their system and network activities [3].”
AVATAR: A Framework to Support Dynamic Security Analysis of Embedded Systems' Firmwares (2014, background): “, packed or obfuscated code) and to perform a wide range of more sophisticated examinations [24] - including taint propagation [33], [55], symbolic and concolic execution [10], [15], [22], unpacking [34], malware sandboxing [1], [5], and whitebox fuzzing [28], [29].”
A Survey on Malware Detection Using Data Mining Techniques (2017, methodology): “Typical examples include asking the users to install a provided “codec” to watch the movies which are hosted on the website, or clicking/opening images attached to spam emails [Egele et al. 2012].”
Malware Analysis and Classification: A Survey (2014, methodology): “Various techniques that can be applied to perform dynamic analysis include function call monitoring, function parameter analysis, information flow tracking, instruction traces and autostart extensibility points etc. [11].”
Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, background): “For an extensive dissertation on dynamic analyses, refer to [22].”