Opcodes as predictor for malware

By Daniel Bilar. International Journal of Electronic Security and Digital Forensics, 2007

301 citations recorded by the source corpus.

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A Comprehensive Review on Malware Detection Approaches (2020, background): “A statistical analysis of opcode frequency distributions to identify and differentiate modern (polymorphic and metamorphic) malware is explained in [86].”
On the Detection of Kernel-Level Rootkits Using Hardware Performance Counters (2017, background): “A variety of works [4, 23, 32] have investigated the use of opcodes for the detection of malware.”
Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey (2009, background): “Bilar (2007) examined the OpCode frequency distribution difference between malicious and benign code.”
Opcode sequences as representation of executables for data-mining-based unknown malware detection (2013, background): “A recent study [4] statistically analysed the ability of single opcodes to serve as the basis for malware detection and conﬁrmed their high reliability for determining the maliciousness of executables.”
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning (2019, methodology): “Bilar [12] introduced Operational Codes (OpCodes) as a suitable and reliable feature for malware identification using machine learning techniques.”