1st Deep Learning and Security (DLS)


City San Francisco
Province California
Country US
Twitter @IEEESSP
Website https://www.ieee-security.org/TC/SPW2018/DLS/
CFP Deadline Dec. 22, 2017, 11:55 p.m.

Details

This workshop is aimed at academic and industrial researchers interested in the application of deep learning methods to computer security problems. Some of the key research questions of interest will include the following:

What are the strengths and shortcomings of current learning methods for representing and/or detecting security threats?
Can deep learning methods be successfully applied to security applications?
Can deep learning help to develop more efficient malware analysis by building a more accurate representation of program behaviors?
What are the challenges involved, and will the use of deep learning methods significantly improve over previous results?
Can deep learning methods better cope with problems related to learning in adversarial environments?
What are the big, open problems in threat representation, especially for the detection of malicious software?
How can generative models improve our understanding and detection of threats?

TOPICS
Topics of interest include (but are not limited to):
Deep Learning
- Deep learning architectures
- Deep NLP (natural language processing)
- Recurrent networks architectures
- Effective feature embedding
- Neural networks for graphs
- Generative adversarial networks
- Deep reinforcement learning
- Relational modeling and prediction
- Semantic knowledge-bases
- Neural abstract machines and program induction

Security Applications
- Program representation
- Malware identification, analysis and similarity
- Detecting malicious software downloads at scale
- Representation and detection of social engineering attacks
- Botnet detection
- Intrusion detection and response
- Spam and phishing detection
- Classification of sequences of system/network events
- Security in social networks
- Application of learning to computer forensics
- Learning in adversarial environments