Experiential Learning: Case Study-Based Portable Hands-on Regression Labware for Cyber Fraud Prediction
Experiential Learning: Case Study-Based Portable Hands-on Regression Labware for Cyber Fraud Prediction
复制标题
体验式学习：用于网络欺诈预测的基于案例研究的便携式动手回归实验室软件
DOI：
10.1109/bigdata47090.2019.9005713
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Cuzzocrea, Alfredo
中科院分区：
文献类型：
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作者：
Shahriar, Hossain;Whitman, Miahcel;Lo, Dan Chia-Tien;Wu, Fan;Thomas, Cassandra;Cuzzocrea, Alfredo
机器学习(ML)分析、处理数据和发现模式。在网络安全方面，它有效地分析现有网络安全攻击的大数据，并制定主动战略来检测当前和未来的网络安全攻击。ML和网络安全都是计算课程中的重要科目，但将ML用于网络安全并不常见。本文设计并展示了一个基于案例研究的便携实验室体验，该体验建立在谷歌的CoLab(CoLab)基础上，用于ML网络安全应用程序，为学生提供随时随地访问动手实验室的机会，减少或消除繁琐的安装和配置。这种方法使学生能够专注于学习基本概念，并通过动手解决问题的技能获得宝贵的经验。本文以信用卡诈骗为例，报道了基于案例的回归实验室软件在网络诈骗预测中的初步结果和学生评价。
Machine Learning (ML) analyzes, and processes data and discover patterns. In cybersecurity, it effectively analyzes big data from existing cybersecurity attacks and develop proactive strategies to detect current and future cybersecurity attacks. Both ML and cybersecurity are important subjects in computing curriculum, but using ML for cybersecurity is not commonly explored. This paper designs and presents a case study-based portable labware experience built on Google's CoLaboratory (CoLab) for a ML cybersecurity application to provide students with hands-on labs accessing from anywhere and anytime, reducing or eliminating tedious installations and configurations. This approach allows students to focus on learning essential concepts and gaining valuable experience through hands-on problem solving skills. Our preliminary results and student evaluations are reported for a case-based hands-on regression labware in cyber fraud prediction using credit card fraud as an example.
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影响因子：
1.8
作者：
A. Cohen;M. Porath;A. Clarke;H. Bai;C. Leggo;Karen Meyer
通讯作者：
Karen Meyer
用于恶意软件检测的卷积神经网络特征评估
DOI：
10.1109/isdfs.2018.8355390
发表时间：
2018
期刊：
2018 6th International Symposium on Digital Forensic and Security (ISDFS)
影响因子：
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作者：
Kemal Özkan;Ş. Işık;Yusuf Kartal
通讯作者：
Yusuf Kartal