Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems最新文献_第5页

Dynamic code whitelist for efficient analysis of Android code 动态代码白名单，用于有效分析Android代码

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264812

Jeongwoo Choi, Yongmin Kim, Jinwoo Lee, Jiman Hong

引用次数: 0

Hardware-accelerated cache simulation for multicore by FPGA 基于FPGA的多核硬件加速缓存仿真

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264766

Shih-Hao Hung, Yi-Mo Ho, C. Yeh, C. Liu, Chen-Pang Lee

{"title":"Hardware-accelerated cache simulation for multicore by FPGA","authors":"Shih-Hao Hung, Yi-Mo Ho, C. Yeh, C. Liu, Chen-Pang Lee","doi":"10.1145/3264746.3264766","DOIUrl":"https://doi.org/10.1145/3264746.3264766","url":null,"abstract":"Developers often use a virtual platform to develop software before the hardware is available. For software optimization, it is important to profile the cache misses of applications in a realistic operating environment under the virtual platform. In the multicore era, it is hard to simulate the coherence cache miss in a high speed way. In this paper, we propose a hardware-accelerated architecture to simulate the cache misses of a multicore system. We implement the cache miss simulator over a virtual platform with FPGA. Users can profile their software as running over the multicore system. The evaluation shows the throughput achieves 65 MB of trace log per second, when FPGA works in 100 MHz and about 570,000 logic elements are occupied to simulate 4 sets of L1 cache and 1 set of L2 cache in the multicore system with 4 virtual CPUs. The system achieves 1.6 to 2 times of speedup, when comparing with the popular cache miss simulator, Dinero IV. Dinero does less work and does not support coherence cache misses in the multicore system. The evaluation result shows high advantage to speed up the cache miss simulation of the multicore system by the hardware-accelerated architecture as well as FPGA.","PeriodicalId":186790,"journal":{"name":"Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126087400","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

Deep-learning based web UI automatic programming 基于深度学习的web UI自动编程

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264807

Bada Kim, Sangmin Park, Taeyeon Won, Junyoung Heo, Bongjae Kim

引用次数: 8

Vulnerability analysis of secure USB: based on the fingerprint authentication of product B 安全USB漏洞分析:基于产品B的指纹认证

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264813

Kyungroul Lee, Byeonggeun Son, Sun-Young Lee, Kangbin Yim

引用次数: 3

Machine learning-based colorectal cancer detection 基于机器学习的大肠癌检测

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264785

V. Blanes-Vidal, G. Baatrup, E. Nadimi

引用次数: 3

Implement of mobile server based on micro-webpage for multi-lingual support menu 基于微网页的多语种支持菜单移动服务器的实现

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264804

Seoyeon Kim, Jinmang Jung, T. Son, Chang-Hyoung Ryu, Heesung Woo

引用次数: 0

Sequence homology in circular RNA detection 环状RNA检测中的序列同源性

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264796

Mohammed Sayed, J. Hwang, J. Park

引用次数: 0

Depth-of-field effect in subjective and objective evaluation of image quality 景深效应是评价图像质量的主客观因素

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264757

Tingting Zhang, H. Nefs, Hantao Liu, L. Xia, Xiaofeng Liu, Xiaoli Wu

引用次数: 1

Ontology modeling for APT attack detection in an IoT-based power system 基于物联网的电力系统APT攻击检测本体建模

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264786

Gihoon Kim, Chang Choi, Junho Choi

引用次数: 6

Android malware detection using convolutional neural networks and data section images Android恶意软件检测使用卷积神经网络和数据切片图像

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264780

Jaemin Jung, Jongmoo Choi, Seong-je Cho, Sangchul Han, Minkyu Park, Young-Sup Hwang

{"title":"Android malware detection using convolutional neural networks and data section images","authors":"Jaemin Jung, Jongmoo Choi, Seong-je Cho, Sangchul Han, Minkyu Park, Young-Sup Hwang","doi":"10.1145/3264746.3264780","DOIUrl":"https://doi.org/10.1145/3264746.3264780","url":null,"abstract":"The paper proposes a new technique to detect Android malware effectively based on converting malware binaries into images and applying machine learning techniques on those images. Existing research converts the whole executable files (e.g., DEX files in Android application package) of target apps into images and uses them for machine learning. However, the entire DEX file (consisting of header section, identifier section, data section, optional link data area, etc.) might contain noisy information for malware detection. In this paper, we convert only data sections of DEX files into grayscale images and apply machine learning on the images with Convolutional Neural Networks (CNN). By using only the data sections for 5,377 malicious and 6,249 benign apps, our technique reduces the storage capacity by 17.5% on average compared to using the whole DEX files. We apply two CNN models, Inception-v3 and Inception-ResNet-v2, which are known to be efficient in image processing, and examine the effectiveness of our technique in terms of accuracy. Experiment results show that the proposed technique achieves better accuracy with smaller storage capacity than the approach using the whole DEX files. Inception-ResNet-v2 with the stochastic gradient descent (SGD) optimization algorithm reaches 98.02% accuracy.","PeriodicalId":186790,"journal":{"name":"Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128966773","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: 23