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Matthew G. Schultz

dblp:94/5058 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Malware analysis › malware detection
malicious executable detection
0.012001
Data Mining Methods for Detection of New Malicious Executables · S&P 2001
Network security › intrusion detection and prevention
intrusion detection
0.012001
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
YearPublicationVenuePosition
2001 Data Mining Methods for Detection of New Malicious Executables
abstract
A 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&P1