NDLI: Proceedings of the 5th ACM workshop on Security and artificial intelligence (AISec '12)

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Machine learning meets social networking security: detecting and analyzing malicious social networks for fun and profit

Improving malware classification: bridging the static/dynamic gap

Learning stateful models for network honeypots

Robust detection of comment spam using entropy rate

Tracking concept drift in malware families

Early detection of malicious behavior in JavaScript code

Nonparametric semi-supervised learning for network intrusion detection: combining performance improvements with realistic in-situ training

Understanding the time-series behavioral characteristics of evolutionally advanced email spammers

Autonomous learning for detection of JavaScript attacks: vision or reality?

An information theoretic framework for web inference detection

On the effectiveness of using state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks

Proceedings of the 5th ACM workshop on Security and artificial intelligence (AISec '12)

Content Provider	ACM Digital Library
Editor	Kapadia, Apu Rubinstein, Benjamin I.P. Gu, Guofei Nelson, Blaine Yu, Ting Venkatakrishan, V. N.
Copyright Year	2012
Abstract	It is our great pleasure to welcome you to the 5th ACM Workshop on Artificial Intelligence and Security -- AISec 2012. This year's workshop serves as the premier forum for the presentation of research on the leading edge issues of security, privacy, AI and machine learning, and as a venue for developing the fundamental theory and practical applications supporting the use of machine learning for security and privacy. The needs of this burgeoning community, who are especially focused on (among other topics) learning in game-theoretic adversarial environments, privacypreserving learning, or the use of sophisticated new learning algorithms in security is not met elsewhere. The mission of this workshop is thus to share novel AI solutions that fulfill the needs of security applications, to identify the unique constraints of learning in security-sensitive environments, and to explore new directions for future research and development. AISec gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the intersection of AI and security applications. We are delighted to once again be co-located with the premier ACM Computer and Communication Security (CCS 2012) conference. The call for papers attracted 24 submissions from Asia, Europe, Africa, and North America. The program committee accepted 10 papers that cover a variety of topics including approaches to malware, spam, and intrusion detection as well as automatic redaction. In addition, we are delighted that the program includes a keynote speech entitled "Machine Learning Meets Social Networking Security -- Detecting and Analyzing Malicious Social Networks for Fun and Profit" to be presented by Guofei Gu from Texas A&M. Together, we hope these contributions comprise a wonderful program that will bring together leading researchers with backgrounds in both Security & Privacy and AI.
ISBN	9781450316644
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-19
Access Restriction	Subscribed
Content Type	Text
Resource Type	Conference Proceedings

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