Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Opcode sequences as representation of executables for data-mining-based unknown malware detection
2011310 citationsIgor Santos, Félix Brezo et al.Information Sciencesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Igor Santos's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Igor Santos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Igor Santos more than expected).
This network shows the impact of papers produced by Igor Santos. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Igor Santos. The network helps show where Igor Santos may publish in the future.
Co-authorship network of co-authors of Igor Santos
This figure shows the co-authorship network connecting the top 25 collaborators of Igor Santos.
A scholar is included among the top collaborators of Igor Santos based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Igor Santos. Igor Santos is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Santos, Igor, et al.. (2017). Extension Breakdown: Security Analysis of Browsers Extension Resources Control Policies. Graduate School and Research Center in Digital Science (EURECOM). 679–694.21 indexed citations
Santos, Igor, Xabier Ugarte-Pedrero, Félix Brezo, & Pablo G. Bringas. (2013). NOA: An Information Retrieval Based Malware Detection System. Computing and Informatics / Computers and Artificial Intelligence. 32(1). 145–174.4 indexed citations
7.
Ugarte-Pedrero, Xabier, Igor Santos, Carlos Laorden, Borja Sanz, & Pablo G. Bringas. (2013). Collective Classification for Packed Executable Identification.. Computer Systems: Science & Engineering. 28.1 indexed citations
Santos, Igor, Félix Brezo, Xabier Ugarte-Pedrero, & Pablo G. Bringas. (2011). Opcode sequences as representation of executables for data-mining-based unknown malware detection. Information Sciences. 231. 64–82.310 indexed citations breakdown →
Nieves, Javier, Igor Santos, & Pablo G. Bringas. (2010). Overcoming data gathering errors for the prediction of mechanical properties on high precision foundries. World Automation Congress. 1–6.2 indexed citations
17.
Santos, Igor, Javier Nieves, & Pablo G. Bringas. (2010). Enhancing fault prediction on automatic foundry processes. World Automation Congress. 1–6.5 indexed citations
Santos, Igor, Javier Nieves, Yoseba K. Penya, & Pablo G. Bringas. (2009). Machine-learning-based mechanical properties prediction in foundry production. 2009 ICCAS-SICE. 4536–4541.13 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.