2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)最新文献_第7页

A Fast Dent Detection Method for Curved Glass Using Deep Convolutional Neural Network 基于深度卷积神经网络的曲面玻璃凹痕快速检测方法

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925124

Lei Wang, Lilan Luo, Peng Zheng, Tianyu Zheng, Shan He

引用次数: 3

White Matter Segmentation from Cranial Ultrasound Images based on Convolutional Neural Network 基于卷积神经网络的颅脑超声图像白质分割

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925220

Jiaqi Tan, D. Que, Yijun Zhao, Yanyan Yu

引用次数: 0

Power Leakage Detection for a Masked SM3-MAC Hardware Implementation 屏蔽SM3-MAC硬件实现的漏电检测

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925299

Hang Yu, Zhenhao He, Liji Wu, Xiangmin Zhang

{"title":"Power Leakage Detection for a Masked SM3-MAC Hardware Implementation","authors":"Hang Yu, Zhenhao He, Liji Wu, Xiangmin Zhang","doi":"10.1109/ICASID.2019.8925299","DOIUrl":"https://doi.org/10.1109/ICASID.2019.8925299","url":null,"abstract":"The SM3-MAC algorithm is a Message Authentication Code (MAC) algorithm based on the SM3 hash algorithm proposed by Office of Security Commercial Code Administration in 2010. In this paper, a masking scheme for SM3-MAC algorithm using key mask is proposed. Then, 5,000 power traces are collected by software simulation with Hamming distance model. We can prove that the unmasked SM3-MAC hardware is vulnerable to first-order power analysis, while the masked SM3-MAC hardware does not have register leakage under the Hamming distance model. After that, the SAKURA-G FPGA board is used to collect two sets of power traces of the masked SM3-MAC hardware, each of which contains 3,000 traces. The Test Vector Leakage Assessment (TVLA) methodology proves that there is a 99.999% chance that no first-order power leakage is detected in the masked SM3-MAC hardware. Finally, the feasibility of second-order power analysis is discussed, and the effects of different pre-processing functions on correlation are investigated. A second-order test has been carried out to analyze the second-order security of the masked SM3-MAC hardware, which proves that there exists second-order leakage.","PeriodicalId":422125,"journal":{"name":"2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126507907","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

Security Analysis of JPEG Image Encryption Algorithm Based on DCT Coefficients Shuffling and Decomposing 基于DCT系数变换与分解的JPEG图像加密算法的安全性分析

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8924887

Shanshan Li, Hongli Zhang, Li Zhao

引用次数: 0

Convergence Stability of Spiking Neural Networks with Stochastic Fluctuations 具有随机波动的脉冲神经网络的收敛稳定性

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925103

Chenhui Zhao, Shan He, Lin Li, Donghui Guo

引用次数: 0

Phishing URL Detection Via Capsule-Based Neural Network 基于胶囊神经网络的网络钓鱼URL检测

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925000

Yongjie Huang, Jinghui Qin, Wushao Wen

{"title":"Phishing URL Detection Via Capsule-Based Neural Network","authors":"Yongjie Huang, Jinghui Qin, Wushao Wen","doi":"10.1109/ICASID.2019.8925000","DOIUrl":"https://doi.org/10.1109/ICASID.2019.8925000","url":null,"abstract":"As a cyber attack which leverages social engineering and other sophisticated techniques to steal sensitive information from users, phishing attack has been a critical threat to cyber security for a long time. Although researchers have proposed lots of countermeasures, phishing criminals figure out circumventions eventually since such countermeasures require substantial manual feature engineering and can not detect newly emerging phishing attacks well enough, which makes developing an efficient and effective phishing detection method an urgent need. In this work, we propose a novel phishing website detection approach by detecting the Uniform Resource Locator (URL) of a website, which is proved to be an effective and efficient detection approach. To be specific, our novel capsule-based neural network mainly includes several parallel branches wherein one convolutional layer extracts shallow features from URLs and the subsequent two capsule layers generate accurate feature representations of URLs from the shallow features and discriminate the legitimacy of URLs. The final output of our approach is obtained by averaging the outputs of all branches. Extensive experiments on a validated dataset collected from the Internet demonstrate that our approach can achieve competitive performance against other state-of-the-art detection methods while maintaining a tolerable time overhead.","PeriodicalId":422125,"journal":{"name":"2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125783121","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

A Deep Learning Approach for Network Intrusion Detection Based on NSL-KDD Dataset 基于NSL-KDD数据集的深度学习网络入侵检测方法

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925239

Chongzhen Zhang, Fangming Ruan, Lan Yin, Xi Chen, Lidong Zhai, Feng Liu

引用次数: 37