NDLI: Secure Learning and Mining in Adversarial Environments [Extended Abstract]

Content Provider	IEEE Xplore Digital Library
Author	Li, Bo
Copyright Year	2015
Abstract	Machine learning and data mining have become ubiquitous tools in modern computing applications and large enterprise systems benefit from its adaptability and intelligent ability to infer patterns that can be used for prediction or decision-making. Great success has been achieved by applying machine learning and data mining to the security settings for large dataset, such as in intrusion detection, virus detection, biometric identity recognition, and spam filtering. However, the strengths of the learning systems, such as the adaptability and ability to infer patterns, can also become their vulnerabilities when there are adversarial manipulations during the learning and predicting process. Considering the fact that the traditional learning strategies could potentially introduce security faults into the learning systems, robust machine learning techniques against the sophisticated adversaries need to be studied, which is referred to as secure learning and mining through this abstract. Based on the goal of secure learning and mining, I aim to analyze the behavior of learning systems in adversarial environments by studying different kinds of attacks against the learning systems. Then design robust learning algorithms to counter the corresponding malicious behaviors based on the evaluation and prediction of the adversaries' goal and capabilities. The interactions between the defender and attackers are modeled as different forms of games, therefore game theoretic analysis are applied to evaluate and predict the constraints for both participants to deal with the real world large dataset.
Starting Page	1538
Ending Page	1539
File Size	1323192
Page Count	2
File Format	PDF
ISSN	23759259
e-ISBN	9781467384933
DOI	10.1109/ICDMW.2015.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Analytical models adversarial learning data mining Games game theory Cost function Robustness Electronic mail Data mining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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