Can Deep Learning Save Us and Itself from the Avalanche of Threats in Cyberspace?

Vassilev, Apostol

doi:https://doi.org/10.1109/MC.2019.2918391

Journal Article

Can Deep Learning Save Us and Itself from the Avalanche of Threats in Cyberspace?

Documentation Topics

Published: September 24, 2019
Citation: Computer (IEEE Computer) vol. 52, no. 10, (October 2019) pp. 76-82

Author(s)

Apostol Vassilev (NIST)

Abstract

I present a computationally efficient and accurate feedforward neural network for sentiment prediction capable of maintaining high transfer accuracy when coupled with an effective semantics model of the text. Experimental results show the advantages of the new approach. Applications to security validation programs are discussed.

Keywords

deep learning; sentiment analysis; Natural Language Processing

Control Families

None selected

Documentation

Publication:
https://doi.org/10.1109/MC.2019.2918391

Supplemental Material:
Preprint (pdf)

Document History:
09/24/19: Journal Article (Final)

Topics

Security and Privacy

general security & privacy

Technologies

artificial intelligence