Demo Data from SIGIR 2026 / reviewers in the wild / expert
Matthew G. Schultz
dblp:94/5058
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Malware analysis · 77% Network security · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Malware analysis › malware detection
malicious executable detection |
0.0 | 1 | 2001 | Data Mining Methods for Detection of New Malicious Executables · S&P 2001 |
Network security › intrusion detection and prevention
intrusion detection |
0.0 | 1 | 2001 | Data Mining Methods for Detection of New Malicious Executables · S&P 2001 |
Methods — techniques the papers use, named apart from their topics
signature-based detection · 0.0pattern mining · 0.0data mining · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Data Mining Methods for Detection of New Malicious ExecutablesabstractA serious security threat today is malicious executables, especially new, unseen malicious executables often arriving as email attachments. These new malicious executables are created at the rate of thousands every year and pose a serious security threat. Current anti-virus systems attempt to detect these new malicious programs with heuristics generated by hand. This approach is costly and oftentimes ineffective. We present a data mining framework that detects new, previously unseen malicious executables accurately and automatically. The data mining framework automatically found patterns in our data set and used these patterns to detect a set of new malicious binaries. Comparing our detection methods with a traditional signature-based method, our method more than doubles the current detection rates for new malicious executables. Matthew G. Schultz, Eleazar Eskin, Erez Zadok, Salvatore J. Stolfo |
S&P | 1 |