FACE-EO paper record
The FACE-EO paper was presented at FPS 2026. Publication of the proceedings is pending; the full citation will be listed here once it is available.
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Papers, technical references and research protocols.
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Publication register
Available work and what is coming.
The FACE-EO paper was presented at FPS 2026; its DOI is listed once the proceedings are published. Technical references and protocols are listed separately so documentation is not mistaken for peer review.
Behavior schema
A public description of the representation dimensions and the current record specimen.
Open referenceEvidence states
The semantics of observed, inferred, partially observed, not-observed and telemetry-gap states.
Open referenceBenchmark methodology
Five proposed evaluation protocols, including their measures and negative-result conditions.
Review entryPeer-reviewed work
Papers by our founder.
Each entry lists the venue and identifier shown by its DOI record. The FACE-EO paper has no DOI yet.
FACE-EO: A Behavior-Centric Intermediate Representation for Explainable Cybersecurity Analysis
M. Nar. FPS 2026 (Accepted, camera-ready; proceedings pending).
A typed, evidence-aware representation that splits software behavior into six components and marks each fact as observed, partial, inferred, missing or conflicting.
M. Nar, “FACE-EO: A Behavior-Centric Intermediate Representation for Explainable Cybersecurity Analysis,” FPS 2026, to appear.
Metamorphic malware identification using engine-specific patterns based on co-opcode graphs
A. G. Kakisim, M. Nar, I. Sogukpinar. Computer Standards & Interfaces, vol. 71, 103443 (Elsevier).
Identifies metamorphic malware families by building a co-opcode graph per family and extracting engine-specific signatures that survive code mutation.
doi:10.1016/j.csi.2020.103443Analysis and Comparison of Disassemblers for OpCode Based Malware Analysis
M. Nar, A. G. Kakisim, M. N. Yavuz, I. Sogukpinar. 2019 4th International Conference on Computer Science and Engineering (UBMK), IEEE.
Measures how the choice of disassembler changes the accuracy of opcode-based malware detection.
doi:10.1109/UBMK.2019.8907153Analysis and Evaluation of Dynamic Feature-Based Malware Detection Methods
A. G. Kakisim, M. Nar, N. Carkaci, I. Sogukpinar. SecITC 2018, Lecture Notes in Computer Science (Springer).
Compares which runtime features (API calls, system library use, operations) best separate malware from benign software.
doi:10.1007/978-3-030-12942-2_19Analysis and Comparison of Opcode-based Malware Detection Approaches
M. Nar, A. G. Kakisim, N. Carkaci, M. N. Yavuz, I. Sogukpinar. 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE.
Tests five opcode-based detectors on a new dataset of 6,857 benign and 8,701 malicious samples; real malware lowers their accuracy versus synthetic samples.
doi:10.1109/UBMK.2018.8566292Citation gate
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