Authentic Learning Approach for Artificial Intelligence Systems Security and Privacy

被引：0

作者：

Akter, Shapna ^{[1
]}

Shahriar, Hossain ^{[2
]}

Lo, Dan ^{[1
]}

Sakib, Nazmus ^{[1
]}

Qian, Kai ^{[1
]}

Whitman, Michael ^{[3
]}

Wu, Fan ^{[4
]}

机构：

[1] Kennesaw State Univ, Dept Comp Sci, Kennesaw, GA 30144 USA

[2] Kennesaw State Univ, Dept Informat Technol, Kennesaw, GA USA

[3] Kennesaw State Univ, Dept Informat Syst & Secur, Kennesaw, GA USA

[4] Tuskegee Univ, Dept Comp Sci, Tuskegee, AL USA

来源：

2023 IEEE 47TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC | 2023年

基金：

美国国家科学基金会;

关键词：

Authentic learning; ML/DL algorithm; Adversarial attack; Security; Privacy; Education;

D O I：

10.1109/COMPSAC57700.2023.00151

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities.

引用

页码：1010 / 1012

页数：3

共 50 条

[41] Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Rohan Gupta
Devesh Srivastava
Mehar Sahu
Swati Tiwari
Rashmi K. Ambasta
Pravir Kumar
Molecular Diversity, 2021, 25 : 1315 - 1360
[42] Big data analytics and artificial intelligence aspects for privacy and security concerns for demand response modelling in smart grid: A futuristic approach
Reka, S. Sofana
Dragicevic, Tomislav
Venugopal, Prakash
Ravi, V.
Rajagopal, Manoj Kumar
HELIYON, 2024, 10 (15)
[43] Security, Trust, and Privacy for the Internet of Vehicles: A Deep Learning Approach
Muhammad, Ghulam
Alhussein, Musaed
IEEE CONSUMER ELECTRONICS MAGAZINE, 2022, 11 (06) : 49 - 55
[44] Privacy versus artificial intelligence in medicine
Rohringer, Taryn J.
Budhkar, Akshay
Rudzicz, Frank
UNIVERSITY OF TORONTO MEDICAL JOURNAL, 2019, 96 (01) : 51 - 53
[45] Evaluation on security and privacy of web-based learning systems
Chan, YY
Leung, CH
Liu, JK
3RD IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 2003, : 308 - 309
[46] Artificial Emotional Intelligence: Conventional and deep learning approach
Kumar, Himanshu
Martin, A.
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 212
[47] Artificial intelligence safety and security
Sanclemente, Gaudys L.
INTERNATIONAL AFFAIRS, 2019, 95 (03) : 728 - 729
[48] Artificial intelligence in information security
Zadeh, J
Watson, N
6TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL VII, PROCEEDINGS: INFORMATION SYSTEMS DEVELOPMENT II, 2002, : 199 - 201
[49] Artificial intelligence in database security
Bai, Y
Zhang, Y
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2004, 18 (01) : 3 - 17
[50] Artificial intelligence in security technology
Rupprecht, Reinhard
Technische Sicherheit, 2024, 14 (7-8): : 13 - 16

← 1 2 3 4 5 →