Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security最新文献

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security 第11届ACM人工智能与安全研讨会论文集

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-10-15 DOI: 10.1145/3270101

引用次数: 0

FeatNet: Large-scale Fraud Device Detection by Network Representation Learning with Rich Features 基于富特征网络表示学习的大规模欺诈设备检测

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3270101.3270109

Chao Xu, Zhentan Feng, Yizheng Chen, Minghua Wang, Tao Wei

{"title":"FeatNet: Large-scale Fraud Device Detection by Network Representation Learning with Rich Features","authors":"Chao Xu, Zhentan Feng, Yizheng Chen, Minghua Wang, Tao Wei","doi":"10.1145/3270101.3270109","DOIUrl":"https://doi.org/10.1145/3270101.3270109","url":null,"abstract":"Online fraud such as search engine poisoning, groups of fake accounts and opinion fraud is conducted by fraudsters controlling a large number of mobile devices. The key to detect such fraudulent activities is to identify devices controlled by fraudsters. Traditional approaches that fingerprint devices based on device metadata only consider single device information. However, these techniques do not utilize the relationship among different devices, which is crucial to detect fraudulent activities. In this paper, we propose an effective device fraud detection framework called FeatNet, which incorporates device features and device relationships in network representation learning. Specifically, we partition the device network into bipartite graphs and generate the neighborhoods of vertices by revised truncated random walk. Then, we generate the feature signature according to device features to learn the representation of devices. Finally, the embedding vectors of all bipartite graphs are used for fraud detection. We conduct experiments on a large-scale data set and the result shows that our approach can achieve better accuracy than existing algorithms and can be deployed in the real production environment with high performance.","PeriodicalId":132293,"journal":{"name":"Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130199828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Session details: AI for Detecting Software Vulnerabilities 会议详情:用于检测软件漏洞的人工智能

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3285950

A. Shabtai

引用次数: 0

Session details: AI Security / Adversarial Machine Learning 会议细节:AI安全/对抗性机器学习

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3285949

B. Biggio

引用次数: 0

Session details: Keynote Address 会议详情:主题演讲

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3285948

Sadia Afroz

引用次数: 0

Adaptive Grey-Box Fuzz-Testing with Thompson Sampling 自适应灰盒模糊测试与汤普森采样

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3270101.3270108

Siddharth Karamcheti, Gideon Mann, David S. Rosenberg

{"title":"Adaptive Grey-Box Fuzz-Testing with Thompson Sampling","authors":"Siddharth Karamcheti, Gideon Mann, David S. Rosenberg","doi":"10.1145/3270101.3270108","DOIUrl":"https://doi.org/10.1145/3270101.3270108","url":null,"abstract":"Fuzz testing, or \"fuzzing,\" refers to a widely deployed class of techniques for testing programs by generating a set of inputs for the express purpose of finding bugs and identifying security flaws. Grey-box fuzzing, the most popular fuzzing strategy, combines light program instrumentation with a data driven process to generate new program inputs. In this work, we present a machine learning approach that builds on AFL, the preeminent grey-box fuzzer, by adaptively learning a probability distribution over its mutation operators on a program-specific basis. These operators, which are selected uniformly at random in AFL and mutational fuzzers in general, dictate how new inputs are generated, a core part of the fuzzer's efficacy. Our main contributions are two-fold: First, we show that a sampling distribution over mutation operators estimated from training programs can significantly improve performance of AFL. Second, we introduce a Thompson Sampling, bandit-based optimization approach that fine-tunes the mutator distribution adaptively, during the course of fuzzing an individual program and outperforms offline training. A set of experiments across complex programs demonstrates that tuning the mutational operator distribution generates sets of inputs that yield significantly higher code coverage and finds more crashes faster and more reliably than both baseline versions of AFL as well as other AFL-based learning approaches.","PeriodicalId":132293,"journal":{"name":"Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128740956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Session details: AI for Forensics 会议细节:AI for Forensics

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3285952

Y. Elovici

引用次数: 0

All You Need is "Love": Evading Hate Speech Detection 所有你需要的是“爱”:逃避仇恨言论检测

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3270101.3270103

Tommi Gröndahl, Luca Pajola, Mika Juuti, M. Conti, N. Asokan

引用次数: 186

Toward Smarter Vulnerability Discovery Using Machine Learning 使用机器学习实现更智能的漏洞发现

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3270101.3270107

Gustavo Grieco, Artem Dinaburg

引用次数: 7

A Marauder's Map of Security and Privacy in Machine Learning: An overview of current and future research directions for making machine learning secure and private 机器学习中安全和隐私的掠夺者地图:使机器学习安全和隐私的当前和未来研究方向概述

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security Pub Date : 2018-01-15 DOI: 10.1145/3270101.3270102

Nicolas Papernot

{"title":"A Marauder's Map of Security and Privacy in Machine Learning: An overview of current and future research directions for making machine learning secure and private","authors":"Nicolas Papernot","doi":"10.1145/3270101.3270102","DOIUrl":"https://doi.org/10.1145/3270101.3270102","url":null,"abstract":"There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions. We structure our discussion around three of these directions, which we believe are likely to lead to significant progress. The first seeks to design mechanisms for assembling reliable records of compromise that would help understand the degree to which vulnerabilities are exploited by adversaries, as well as favor psychological acceptability of machine learning applications. The second encompasses a spectrum of approaches to input verification and mediation, which is a prerequisite to enable fail-safe defaults in machine learning systems. The third pursues formal frameworks for security and privacy in machine learning, which we argue should strive to align machine learning goals such as generalization with security and privacy desirata like robustness or privacy. Key insights resulting from these three directions pursued both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by systematizing best practices in our growing community.","PeriodicalId":132293,"journal":{"name":"Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125402978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37