Embedded Malware Detection Using Markov n-Grams

By M. Shafiq, Syed Ali Khayam, Muddassar Farooq. 2008

123 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

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In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

Practical Evasion of a Learning-Based Classifier: A Case Study (2014, background): “, [37], [38], [39], [40], which uses n-grams, or short byte sequences, as basic features.”
Malicious PDF detection using metadata and structural features (2012, background): “Signature analysis and pattern matching has been studied extensively [26, 24] including specifically for PDF vulnerabilities [20, 16].”
Detection of Malicious PDF Files Based on Hierarchical Document Structure (2013, methodology): “The technical realization of these two fundamental tasks is presented below.”
Graph-based malware detection using dynamic analysis (2011, methodology): “The data transformation that we use, program trace to Markov chain, is similar to the Markov n-gram approach [32].”
Opcode sequences as representation of executables for data-mining-based unknown malware detection (2013, background): “In a similar vein, substantial research has focussed on n-gram distributions of byte sequences and data mining [50, 71, 85, 67].”
When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors (2016, background): “However, most classifier evasion attempts, including nine targeted mimicry scenarios from two recent studies, are given an outcome of uncertain indicating that these observations cannot be given a reliable prediction by the classifier.”
Towards Adversarial Malware Detection (2018, methodology): “Hence, we could only refer to what has been stated in the released papers [80, 82, 83, 89].”
Comprehensive Analysis and Detection of Flash-Based Malware (2016, background): “The detection of embedded malware, however, is a research field of its own and ranges from statically matching shellcode signatures to finding suspicious code in different file containers [4, 39, 40, 50].”
Static detection of malicious JavaScript-bearing PDF documents (2011, methodology): “For the end-user systems, some early work on the detection of potential exploits in PDF documents [13, 21] has gone largely unnoticed, and in practice the detection of malicious PDF documents still hinges upon signatures provided by security vendors.”
Looking at the bag is not enough to find the bomb: an evasion of structural methods for malicious PDF files detection (2013, methodology): “The first approaches based on machine learning for malicious PDF detection proposed static analysis on the raw (byte-level) document, by means of n-gram analysis [25, 32] and decision trees [38].”