2021 IEEE Conference on Dependable and Secure Computing (DSC)最新文献_第4页

A Black-Box Adversarial Attack via Deep Reinforcement Learning on the Feature Space 通过特征空间深度强化学习实现黑箱对抗攻击

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346264

Lyue Li, Amir Rezapour, Wen-Guey Tzeng

{"title":"A Black-Box Adversarial Attack via Deep Reinforcement Learning on the Feature Space","authors":"Lyue Li, Amir Rezapour, Wen-Guey Tzeng","doi":"10.1109/DSC49826.2021.9346264","DOIUrl":"https://doi.org/10.1109/DSC49826.2021.9346264","url":null,"abstract":"In this paper we propose a novel black-box adversarial attack by using the reinforcement learning to learn the characteristics of the target classifier C. Our method does not need to find a substitute classifier that resembles $C$ with respect to its structure and parameters. Instead, our method learns an optimal attacking policy of guiding the attacker to build an adversarial image from the original image. We work on the feature space of images, instead of the pixels of images directly. Our method achieves better results on many measures. Our method achieves 94.5 % attack success rate on a well-trained digit classifier. Our adversarial images have better imperceptibility even though the norm distances to original images are larger than other methods. Since our method works on the characteristics of a classifier, it has better transferability. The transfer rate of our method could reach 52.1 % for a targeted class and 65.9% for a non-targeted class. This improves over previous results of single-digit transfer rates. Also, we show that it is harder to defend our attack by incorporating defense mechanisms, such as MagNet, which uses a denoising technique. We show that our method achieves 65% attack success rate even though the target classifier employs MagNet to defend.","PeriodicalId":184504,"journal":{"name":"2021 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674101","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

A Profile Matching Scheme based on Private Set Intersection for Cyber-Physical-Social Systems 一种基于私有集交集的网络-物理-社会系统轮廓匹配方案

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346252

Yalian Qian, Xueya Xia, Jian Shen

引用次数: 2

Mixed-mode Information Flow Tracking with Compile-time Taint Semantics Extraction and Offline Replay 基于编译时污点语义提取和离线重放的混合模式信息流跟踪

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346239

Yu-Hsin Hung, Bing-Jhong Jheng, Hong-Wei Li, Wen-Yang Lai, S. Mallissery, Yu-Sung Wu

{"title":"Mixed-mode Information Flow Tracking with Compile-time Taint Semantics Extraction and Offline Replay","authors":"Yu-Hsin Hung, Bing-Jhong Jheng, Hong-Wei Li, Wen-Yang Lai, S. Mallissery, Yu-Sung Wu","doi":"10.1109/DSC49826.2021.9346239","DOIUrl":"https://doi.org/10.1109/DSC49826.2021.9346239","url":null,"abstract":"Static information flow analysis (IFA) and dynamic information flow tracking (DIFT) have been widely employed in offline security analysis of computer programs. As security attacks become more sophisticated, there is a rising need for IFA and DIFT in production environment. However, existing systems usually deal with IFA and DIFT separately, and most DIFT systems incur significant performance overhead. We propose MIT to facilitate IFA and DIFT in online production environment. MIT offers mixed-mode information flow tracking at byte-granularity and incurs moderate runtime performance overhead. The core techniques consist of the extraction of taint semantics intermediate representation (TSIR) at compile-time and the decoupled execution of TSIR for information flow analysis. We conducted an extensive performance overhead evaluation on MIT to confirm its applicability in production environment. We also outline potential applications of MIT, including the implementation of data provenance checking and information flow based anomaly detection in real-world applications.","PeriodicalId":184504,"journal":{"name":"2021 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116819520","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: 1

An Efficient Anonymous Authentication Scheme for Privacy-preserving in Smart Grid 智能电网中一种高效的匿名身份认证方案

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346257

Xueya Xia, S. Ji

引用次数: 2

Efficient Multi-Authority Attribute-based Signcryption with Constant-Size Ciphertext 具有恒定长度密文的高效多权威属性签名加密

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346249

Yang Zhao, Ankang Ruan, Guohang Dan, Jicheng Huang, Yi Ding

{"title":"Efficient Multi-Authority Attribute-based Signcryption with Constant-Size Ciphertext","authors":"Yang Zhao, Ankang Ruan, Guohang Dan, Jicheng Huang, Yi Ding","doi":"10.1109/DSC49826.2021.9346249","DOIUrl":"https://doi.org/10.1109/DSC49826.2021.9346249","url":null,"abstract":"Recently, efficient fine-grained access mechanism has been studied as a main concern in cloud storage area for several years. Attribute-based signcryption (ABSC) which is logical combination of attribute-based encryption(ABE) and attribute-based signature(ABS), can provide confidentiality, authenticity for sensitive data and anonymous authentication. At the same time it is more efficient than previous “encrypt-then-sign” and “sign-then-encrypt” patterns. However, most of the existing ABSC schemes fail to serve for real scenario of multiple authorities and have heavy communication overhead and computing overhead. Hence, we construct a novel ABSC scheme realizing multi-authority access control and constant-size ciphertext that does not depend on the number of attributes or authorities. Furthermore, our scheme provides public verifiability of the ciphertext and privacy protection for the signcryptor. Specially, it is proven to be secure in the standard model, including ciphertext indistinguishability under adaptive chosen ciphertext attacks and existential unforgeability under adaptive chosen message attack.","PeriodicalId":184504,"journal":{"name":"2021 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130201040","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: 2

Vulnerability of Privacy Visor Used to Disrupt Unauthorized Face Recognition 隐私遮阳板用于破坏未经授权的人脸识别的漏洞

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346246

Hiroaki Kikuchi, Kazuki Eto, Kazushi Waki, Takafumi Mori

引用次数: 1

EC-Model: An Evolvable Malware Classification Model EC-Model:一种可进化的恶意软件分类模型

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346248

Shan-Hsin Lee, Shen-Chieh Lan, Hsiu-Chuan Huang, Chia-Wei Hsu, Yung-Shiu Chen, S. Shieh

{"title":"EC-Model: An Evolvable Malware Classification Model","authors":"Shan-Hsin Lee, Shen-Chieh Lan, Hsiu-Chuan Huang, Chia-Wei Hsu, Yung-Shiu Chen, S. Shieh","doi":"10.1109/DSC49826.2021.9346248","DOIUrl":"https://doi.org/10.1109/DSC49826.2021.9346248","url":null,"abstract":"Malware evolves quickly as new attack, evasion and mutation techniques are commonly used by hackers to build new malicious malware families. For malware detection and classification, multi-class learning model is one of the most popular machine learning models being used. To recognize malicious programs, multi-class model requires malware types to be predefined as output classes in advance which cannot be dynamically adjusted after the model is trained. When a new variant or type of malicious programs is discovered, the trained multi-class model will be no longer valid and have to be retrained completely. This consumes a significant amount of time and resources, and cannot adapt quickly to meet the timely requirement in dealing with dynamically evolving malware types. To cope with the problem, an evolvable malware classification deep learning model, namely EC-Model, is proposed in this paper which can dynamically adapt to new malware types without the need of fully retraining. Consequently, the reaction time can be significantly reduced to meet the timely requirement of malware classification. To our best knowledge, our work is the first attempt to adopt multi-task, deep learning for evolvable malware classification.","PeriodicalId":184504,"journal":{"name":"2021 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128074585","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: 1

Privacy-Preserving Smart Road Pricing System in Smart Cities 智慧城市中保护隐私的智能道路收费系统

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346274

Qingfeng Zhu, S. Ji, Qi Liu

引用次数: 0

Phishing Site Detection Using Similarity of Website Structure 基于网站结构相似性的钓鱼网站检测

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346256

Shoma Tanaka, T. Matsunaka, A. Yamada, A. Kubota

{"title":"Phishing Site Detection Using Similarity of Website Structure","authors":"Shoma Tanaka, T. Matsunaka, A. Yamada, A. Kubota","doi":"10.1109/DSC49826.2021.9346256","DOIUrl":"https://doi.org/10.1109/DSC49826.2021.9346256","url":null,"abstract":"The number of phishing sites is increasing and becoming a problem. General phishing sites often have very short lives. Phishers are thought to construct phishing sites using tools such as phishing kits. Phishing sites constructed using the same tools have similar website structures. We propose a new method based on the similarity of website structure information defined by the types and sizes of web resources that make up these websites. Our method can detect phishing sites that is not registered with blocklists or do not have similar URL strings with targeting legitimate sites. In addition, our method can identify phishing sites that differed in appearance but have similar website structures. Our method is particularly effective for detecting phishing sites constructed by the same phishers or using the same tools, as our method identifies structural similarity between websites. We conducted an evaluation to confirm the correctness of our assumption using phishing sites constructed using phishing kits and the PhishTank dataset. We found a large number of phishing sites that were structurally similar to phishing sites constructed using phishing kits. We applied our method to web access logs provided by ordinary Japanese citizens, and detected some unknown phishing sites. We have also examined the possibility of improving our method based on the importance of web resources, determined using the number of occurrences in web access logs.","PeriodicalId":184504,"journal":{"name":"2021 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131716365","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: 5

CCA-Secure Attribute-Based Encryption Supporting Dynamic Membership in the Standard Model 支持标准模型中动态成员关系的基于cca安全属性的加密

2021 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2021-01-30 DOI: 10.1109/DSC49826.2021.9346247

Chun-I Fan, Yi-Fan Tseng, Cheng-Chun Feng

引用次数: 1