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【InForSec@复旦大学】德克萨斯大学达拉斯分校助理教授杨威与您相约复旦大学!

摘要:7月22日,德克萨斯大学达拉斯分校助理教授杨威与您相约复旦大学!

While machine learning-based techniques have been widely applied in security domains, being able to explain the rationale behind their decision making process remains as a largely open problem. Recent techniques on interpreting decision making of neural networks either provide local explanation for each input instance or approximate the original model based on a set of input-output instances. 

虽然基于机器学习的技术已广泛应用于安全领域,但能够解释其决策过程背后的基本原理仍然是一个基本上是开放的问题。解释神经网络决策的最新技术或者为每个输入实例提供局部解释,或者基于一组输入 - 输出实例来近似原始模型

The quality of explanation provided by these techniques is limited by the scope of inputs used to generate approximated models or explanations. However, the inherent nature of security research requires us to understand the intrinsic characteristics of a neural network model instead of just parts of model behaviors. 

这些技术提供的解释质量受到用于生成近似模型或解释的输入范围的限制。然而,安全研究的固有性质要求我们理解神经网络模型的内在特征,而不仅仅是模型行为的一部分。

In this talk, I will first introduce REINAM as an example of applying machine learning technique for security research purpose. REINAM is a reinforcement-learning approach for synthesizing probabilistic context-free program input grammars without any seed inputs. 

在本次演讲中,我将首先介绍REINAM作为将机器学习技术应用于安全研究目的的一个例子。REINAM是一种强化学习方法,用于在没有任何种子输入的情况下合成概率无上下文程序输入语法。

Then, I will introduce DENAS, a novel input-independent neural-network explanation approach dedicated for security applications. DENAS is capable of efficiently generating decision rules which could interpret the decision making of a neural network without providing any input. Finally, I will briefly introduce iRuler, an IoT analysis framework that leverages Satisfiability Modulo Theories (SMT) solving and model checking to discover inter-rule vulnerabilities.

然后,我将介绍DENAS,一种专用于安全应用的新颖的独立于输入的神经网络解释方法。DENAS能够有效地生成决策规则,该规则可以解释神经网络的决策而无需提供任何输入。最后,我将简要介绍iRuler,这是一个利用可满足模数理论(SMT)求解和模型检查来发现规则间漏洞的物联网分析框架。

Wei Yang is an assistant professor in the Department of Computer Science at the University of Texas at Dallas. 

Wei Yang是德克萨斯大学达拉斯分校计算机科学系的助理教授。

He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2018, advised by Prof. Carl A. Gunter and Prof. Tao Xie, an M.S. in Computer Science from North Carolina State University in 2013, advised by Prof. Tao Xie, and a B.E. in Software Engineering from Shanghai Jiao Tong University in 2011, advised by Prof. Jianjun Zhao. He was a visiting researcher in University of California, Berkeley in 2017, invited by Prof. Dawn Song.

他获得了博士学位。他于2018年在伊利诺伊大学厄巴纳 - 香槟分校获得计算机科学专业,由Carl A. Gunter教授和谢教授教授。2013年获得北卡罗来纳州立大学计算机科学学士学位,陶燮教授和B.E. 2011年,上海交通大学软件工程专业,赵建军教授。他是2017年加州大学伯克利分校的访问研究员,受到Dawn Song教授的邀请。

文章转载自:网安国际

发布者:系统管理员   点击数:1382   发布时间: 2019-07-18 19:02:08   更新时间: 2019-07-18 19:09:55