2020 IEEE Security and Privacy Workshops (SPW)最新文献_第5页

Electromagnetic Sensor and Actuator Attacks on Power Converters for Electric Vehicles 电动汽车电源转换器的电磁传感器和执行器攻击

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/SPW50608.2020.00032

Gokcen Y. Dayanikli, Rees Hatch, Ryan M. Gerdes, Hongjie Wang, R. Zane

{"title":"Electromagnetic Sensor and Actuator Attacks on Power Converters for Electric Vehicles","authors":"Gokcen Y. Dayanikli, Rees Hatch, Ryan M. Gerdes, Hongjie Wang, R. Zane","doi":"10.1109/SPW50608.2020.00032","DOIUrl":"https://doi.org/10.1109/SPW50608.2020.00032","url":null,"abstract":"Alleviating range anxiety for electric vehicles (i.e., whether such vehicles can be relied upon to travel long distances in a timely manner) is critical for sustainable transportation. Extremely fast charging (XFC), whereby electric vehicles (EV) can be quickly recharged in the time frame it takes to refuel an internal combustion engine, has been proposed to alleviate this concern. A critical component of these chargers is the efficient and proper operation of power converters that convert AC to DC power and otherwise regulate power delivery to vehicles. These converters rely on the integrity of sensor and actuation signals. In this work the operation of state-of-the art XFC converters is assessed in adversarial conditions, specifically against Intentional Electromagnetic Interference Attacks (IEMI). The targeted system is analyzed with the goal of determining possible weak points for IEMI, viz. voltage and current sensor outputs and gate control signals. This work demonstrates that, with relatively low power levels, an adversary is able to manipulate the voltage and current sensor outputs necessary to ensure the proper operation of the converters. Furthermore, in the first attack of its kind, it is shown that the gate signal that controls the converter switches can be manipulated, to catastrophic effect; i.e., it is possible for an attacker to control the switching state of individual transistors to cause irreparable damage to the converter and associated systems. Finally, a discussion of countermeasures for hardware designers to mitigate IEMI-based attacks is provided.","PeriodicalId":413600,"journal":{"name":"2020 IEEE Security and Privacy Workshops (SPW)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125551140","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: 20

Language-agnostic Injection Detection 语言无关的注入检测

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/SPW50608.2020.00060

Lars Hermerschmidt, A. Straub, Goran Piskachev

{"title":"Language-agnostic Injection Detection","authors":"Lars Hermerschmidt, A. Straub, Goran Piskachev","doi":"10.1109/SPW50608.2020.00060","DOIUrl":"https://doi.org/10.1109/SPW50608.2020.00060","url":null,"abstract":"Formal languages are ubiquitous wherever software systems need to exchange or store data. Unparsing into and parsing from such languages is an error-prone process that has spawned an entire class of security vulnerabilities. There has been ample research into finding vulnerabilities on the parser side, but outside of language specific approaches, few techniques targeting unparser vulnerabilities exist. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software.","PeriodicalId":413600,"journal":{"name":"2020 IEEE Security and Privacy Workshops (SPW)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121166064","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: 0

Toward a Trustable, Self-Hosting Computer System 迈向可信赖的、自托管的计算机系统

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/SPW50608.2020.00039

Gabriel L. Somlo

引用次数: 0

Mission Assurance for Autonomous Undersea Vehicles 自主水下航行器的任务保证

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/SPW50608.2020.00056

K. Siil, A. Rubin, Matthew C. Elder, A. Dahbura, M. Green, Lanier A Watkins

引用次数: 1

Cyber Resilient Supply Chain Technologies Workshop (CReSCT 2020) 网络弹性供应链技术研讨会(CReSCT 2020)

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/spw50608.2020.00012

引用次数: 0

Never Ending Story: Authentication and Access Control Design Flaws in Shared IoT Devices 永无止境的故事:共享物联网设备中的身份验证和访问控制设计缺陷

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/SPW50608.2020.00033

Blake Janes, Heather Crawford, T. OConnor

引用次数: 13

WAAS 2020 Organization WAAS 2020组织

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/spw50608.2020.00015

Lanier A Watkins

引用次数: 0

Detecting Cyber Threats in Non-English Hacker Forums: An Adversarial Cross-Lingual Knowledge Transfer Approach 检测非英语黑客论坛中的网络威胁:一种对抗性跨语言知识转移方法

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-05-01 DOI: 10.1109/SPW50608.2020.00021

Mohammadreza Ebrahimi, S. Samtani, Yidong Chai, Hsinchun Chen

{"title":"Detecting Cyber Threats in Non-English Hacker Forums: An Adversarial Cross-Lingual Knowledge Transfer Approach","authors":"Mohammadreza Ebrahimi, S. Samtani, Yidong Chai, Hsinchun Chen","doi":"10.1109/SPW50608.2020.00021","DOIUrl":"https://doi.org/10.1109/SPW50608.2020.00021","url":null,"abstract":"The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. Many cybersecurity professionals are closely examining the international Dark Web to proactively pinpoint potential cyber threats. Despite its potential, the Dark Web contains hundreds of thousands of non-English posts. While machine translation is the prevailing approach to process non-English text, applying MT on hacker forum text results in mistranslations. In this study, we draw upon Long-Short Term Memory (LSTM), Cross-Lingual Knowledge Transfer (CLKT), and Generative Adversarial Networks (GANs) principles to design a novel Adversarial CLKT (A-CLKT) approach. A-CLKT operates on untranslated text to retain the original semantics of the language and leverages the collective knowledge about cyber threats across languages to create a language invariant representation without any manual feature engineering or external resources. Three experiments demonstrate how A-CLKT outperforms state-of-the-art machine learning, deep learning, and CLKT algorithms in identifying cyber-threats in French and Russian forums.","PeriodicalId":413600,"journal":{"name":"2020 IEEE Security and Privacy Workshops (SPW)","volume":"53 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126216999","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: 6

Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression 用变分自编码器进行回归检测学习网络物理系统中的对抗例子

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-03-21 DOI: 10.1109/SPW50608.2020.00050

Feiyang Cai, Jiani Li, X. Koutsoukos

引用次数: 8

Modeling and Assessment of IoT Supply Chain Security Risks: The Role of Structural and Parametric Uncertainties 物联网供应链安全风险建模与评估:结构和参数不确定性的作用

2020 IEEE Security and Privacy Workshops (SPW) Pub Date : 2020-03-20 DOI: 10.1109/SPW50608.2020.00043

T. Kieras, Muhammad Junaid Farooq, Quanyan Zhu

引用次数: 5