Graph-based malware detection using dynamic analysis

By Blake Anderson, Daniel Quist, Joshua Neil, C. Storlie, Terran Lane. Journal in Computer Virology, 2011

311 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/49589aec41cbc2cb827464682d4f9148dc2bae1b

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Citations in the corpus

A Comprehensive Review on Malware Detection Approaches (2020, methodology): “Graph-based malware detection using dynamic analysis is proposed in [42].”
A comparison of static, dynamic, and hybrid analysis for malware detection (2015, methodology): “The authors of [2] propose a malware detection technique that uses instruction trace logs of executables, where this information is collected dynamically.”
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models (2021, background): “generated by knowledge graphs are useful to other systems like AI-based intrusion detection systems [37], [38], [58], or alert-generators [3], [35], reaching a larger breadth of linked systems and cybersecurity professionals.”
Detection of Intrusions and Malware, and Vulnerability Assessment (2013, methodology): “Previously, many types of malware features have been used to classify or cluster malware instances, such as byte sequence n-gram [31,12,25], instructions in execution traces [1], PE headers [34,26], function call graphs [8], control flow graphs [15], and system calls [11,4,6].”
Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection (2016, background): “ernative approaches based on dynamically analysing behaviours. In particular, machine learning has been widely applied in research to classify malware, as an alternative to signature-based approaches [1], [20], [28]. 1A class of malware that use tricks to scare users, e.g. by displaying a warning pu”
Malware Analysis and Classification: A Survey (2014, methodology): “[40] presented a malware detection algorithm based on the analysis of graphs constructed from dynamically collected instruction traces.”
Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, methodology): “Chunks either of 50, 100 KB, or equal to le size [72] 1-grams [1, 50, 72] 2-grams [3, 4, 50, 69, 72] 3-grams [19, 36, 50, 72] 4-grams [50, 76] 5-grams, 6-grams [50]”
A Survey on Malware Detection Using Data Mining Techniques (2017, methodology): “A detection method based on instruction traces was proposed by Anderson et al. [2011].”
Contextual Weisfeiler-Lehman graph kernel for malware detection (2016, background): “Recently, three approaches [3], [6] and [9], have successfully demonstrated using these general purpose graph kernels for malware detection.”
You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020, methodology): “In traditional approaches, static analysis [95], [92], [46] and dynamic analysis [24], [41], [34] have been used to analyze and detect malware.”
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning (2019, background): “Previous research has shown the usefulness of this representation in malware detection [14], [15], [30].”