2019 IEEE Security and Privacy Workshops (SPW)最新文献_第5页

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2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-05-01 DOI: 10.1109/spw.2019.00002

引用次数: 0

[Copyright notice] (版权)

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-05-01 DOI: 10.1109/spw.2019.00003

引用次数: 0

DLS 2019 Organization DLS 2019组织

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-05-01 DOI: 10.1109/spw.2019.00007

引用次数: 0

Message from the Workshop General Chair 讲习班总主席致辞

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-05-01 DOI: 10.1109/spw.2019.00005

引用次数: 0

Victim Routine Influences the Number of DDoS Attacks: Evidence from Dutch Educational Network 受害者的日常行为影响DDoS攻击的数量:来自荷兰教育网的证据

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-05-01 DOI: 10.1109/SPW.2019.00052

Abhishta Abhishta, M. Junger, R. Joosten, L. Nieuwenhuis

引用次数: 2

Are Self-Driving Cars Secure? Evasion Attacks Against Deep Neural Networks for Steering Angle Prediction 自动驾驶汽车安全吗?面向转向角预测的深度神经网络规避攻击

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-04-15 DOI: 10.1109/SPW.2019.00033

Alesia Chernikova, Alina Oprea, C. Nita-Rotaru, Baekgyu Kim

引用次数: 60

On the Robustness of Deep K-Nearest Neighbors 关于深度k近邻的鲁棒性

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-03-20 DOI: 10.1109/SPW.2019.00014

Chawin Sitawarin, David A. Wagner

引用次数: 53

Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification 基于字节的深度神经网络恶意软件分类激活分析

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2019-03-12 DOI: 10.1109/SPW.2019.00017

Scott E. Coull, Christopher Gardner

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引用次数: 37

Exploring Adversarial Examples in Malware Detection 探索恶意软件检测中的对抗性示例

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2018-10-18 DOI: 10.1109/SPW.2019.00015

Octavian Suciu, Scott E. Coull, Jeffrey Johns

{"title":"Exploring Adversarial Examples in Malware Detection","authors":"Octavian Suciu, Scott E. Coull, Jeffrey Johns","doi":"10.1109/SPW.2019.00015","DOIUrl":"https://doi.org/10.1109/SPW.2019.00015","url":null,"abstract":"The convolutional neural network (CNN) architecture is increasingly being applied to new domains, such as malware detection, where it is able to learn malicious behavior from raw bytes extracted from executables. These architectures reach impressive performance with no feature engineering effort involved, but their robustness against active attackers is yet to be understood. Such malware detectors could face a new attack vector in the form of adversarial interference with the classification model. Existing evasion attacks intended to cause misclassification on test-time instances, which have been extensively studied for image classifiers, are not applicable because of the input semantics that prevents arbitrary changes to the binaries. This paper explores the area of adversarial examples for malware detection. By training an existing model on a production-scale dataset, we show that some previous attacks are less effective than initially reported, while simultaneously highlighting architectural weaknesses that facilitate new attack strategies for malware classification. Finally, we explore how generalizable different attack strategies are, the trade-offs when aiming to increase their effectiveness, and the transferability of single-step attacks.","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125807024","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: 151

Targeted Adversarial Examples for Black Box Audio Systems 黑箱音频系统的目标对抗示例

2019 IEEE Security and Privacy Workshops (SPW) Pub Date : 2018-05-20 DOI: 10.1109/SPW.2019.00016

Rohan Taori, Amog Kamsetty, Brenton Chu, N. Vemuri

引用次数: 153