TY - GEN
T1 - AISec'19
T2 - 26th ACM SIGSAC Conference on Computer and Communications Security, CCS 2019
AU - Afroz, Sadia
AU - Biggio, Battista
AU - Carlini, Nicholas
AU - Elovici, Yuval
AU - Shabtai, Asaf
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/6
Y1 - 2019/11/6
N2 - Recent years have seen a dramatic increase in applications of Artificial Intelligence (AI) and Machine Learning (ML) to security and privacy problems. The analytic tools and intelligent behavior provided by these techniques make AI and ML increasingly important for autonomous real-time analysis and decision making in domains with a wealth of data or that require quick reactions to constantly changing situations. The use of learning methods in security-sensitive domains, in which adversaries may attempt to mislead or evade intelligent machines, creates new frontiers for security research. The recent widespread adoption of deep-learning techniques, whose security properties are difficult to reason about directly, has only added to the importance of this research. In addition, data mining and machine learning techniques create a wealth of privacy issues, due to the abundance and accessibility of data. The 12th ACM Workshop on Artificial Intelligence and Security (AISec) is one of the historical, leading venues for presenting and discussing new developments in the intersection of security and privacy with AI and ML.
AB - Recent years have seen a dramatic increase in applications of Artificial Intelligence (AI) and Machine Learning (ML) to security and privacy problems. The analytic tools and intelligent behavior provided by these techniques make AI and ML increasingly important for autonomous real-time analysis and decision making in domains with a wealth of data or that require quick reactions to constantly changing situations. The use of learning methods in security-sensitive domains, in which adversaries may attempt to mislead or evade intelligent machines, creates new frontiers for security research. The recent widespread adoption of deep-learning techniques, whose security properties are difficult to reason about directly, has only added to the importance of this research. In addition, data mining and machine learning techniques create a wealth of privacy issues, due to the abundance and accessibility of data. The 12th ACM Workshop on Artificial Intelligence and Security (AISec) is one of the historical, leading venues for presenting and discussing new developments in the intersection of security and privacy with AI and ML.
KW - Artificial Intelligence
KW - Computer Security
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85075927370&partnerID=8YFLogxK
U2 - 10.1145/3319535.3353556
DO - 10.1145/3319535.3353556
M3 - Conference contribution
AN - SCOPUS:85075927370
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 2707
EP - 2708
BT - CCS 2019 - Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
PB - Association for Computing Machinery
Y2 - 11 November 2019 through 15 November 2019
ER -