Cloud-based malware detection for evolving data streams
Abstract
Data stream classification for intrusion detection poses at least three major challenges. First, these data streams are typically infinite-length, making traditional multipass learning algorithms inapplicable. Second, they exhibit significant concept-drift as attackers react and adapt to defenses. Third, for data streams that do not have any fixed feature set, such as text streams, an additional feature extraction and selection task must be performed. If the number of candidate features is too large, then traditional feature extraction techniques fail.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 18, 2008
- Source ID
- 10.1145/2019618.2019622
Entities
People
- Bhavani Thuraisingham
- Jiawei Han
- Jing Gao
- Kevin W. Hamlen
- Latifur Khan
- Mohammad M. Masud
- Tahseen M. Al-khateeb
Organizations
- Air Force Office of Scientific Research
- National Aeronautics and Space Administration
- University of Illinois Urbana–Champaign
- University of Texas at Dallas