15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献_第2页

Training of intelligent intrusion detection system using neuro fuzzy 利用神经模糊训练智能入侵检测系统

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888688

Biswajit Panja, Olugbenga Ogunyanwo, Priyanka Meharia

{"title":"Training of intelligent intrusion detection system using neuro fuzzy","authors":"Biswajit Panja, Olugbenga Ogunyanwo, Priyanka Meharia","doi":"10.1109/SNPD.2014.6888688","DOIUrl":"https://doi.org/10.1109/SNPD.2014.6888688","url":null,"abstract":"Intrusion detection systems classify computer activities into two main categories: normal and suspicious activities. In order to achieve the classification, Intrusion detection systems use software computing techniques including neural networks and neuro fuzzy networks to categorize network activities and specify what category of attack is being generated. Neuro-Fuzzy classifiers are used for the initial classification of the initial network traffic. An inference system, Fuzzy inference systems is further used to determine whether the activity is normal or malicious. Efficient IDS systems are those capable of reducing false positives and generate high rate attack detection. However, fuzzy inference systems use human knowledge to create their fuzzy rule. In order to introduce a more accurate way of classifying network traffic, we introduce the use of Genetic Algorithms in conjunction with ANFIS so as to optimize data classification and obtain the best results. Genetic algorithms use a set of genetic operators such as mutation, crossover and selection on current population to reproduce similar patterns that will be used repeatedly until a particular criterion is met.","PeriodicalId":272932,"journal":{"name":"15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122769021","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: 13

Scalable bootstrap clustering for massive data 用于海量数据的可伸缩自举集群

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888693

Haocheng Wang, Fuzhen Zhuang, Xiang Ao, Qing He, Zhongzhi Shi

引用次数: 2

AES-128 ECB encryption on GPUs and effects of input plaintext patterns on performance gpu上的AES-128 ECB加密及输入明文模式对性能的影响

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888707

Ayaz H. Khan, M. Al-Mouhamed, A. Almousa, Allam Fatayar, A. Ibrahim, A. Siddiqui

{"title":"AES-128 ECB encryption on GPUs and effects of input plaintext patterns on performance","authors":"Ayaz H. Khan, M. Al-Mouhamed, A. Almousa, Allam Fatayar, A. Ibrahim, A. Siddiqui","doi":"10.1109/SNPD.2014.6888707","DOIUrl":"https://doi.org/10.1109/SNPD.2014.6888707","url":null,"abstract":"In the recent years, the Graphics Processing Units (GPUs) have gained popularity for general purpose applications, immensely outperforming traditional optimized CPU based implementations. A class of such applications implemented on GPUs to achieve faster execution than CPUs include cryptographic techniques like the Advanced Encryption Standard (AES) which is a widely deployed symmetric encryption/decryption scheme in various electronic communication domains. With the drastic advancements in electronic communication technology, and growth in the user space, the size of data exchanged electronically has increased substantially. So, such cryptographic techniques become a bottleneck to fast transfers of information. In this work, we implement the AES-128 ECB Encryption on two of the recent and advanced GPUs (NVIDIA Quadro FX 7000 and Tesla K20c) with different memory usage schemes and varying input plaintext sizes and patterns. We obtained a speedup of up to 87x against an advanced CPU (Intel Xeon X5690) based implementation. Moreover, our experiments reveal that the different degrees of pattern repetitions in input plaintext affect the encryption performance on GPU.","PeriodicalId":272932,"journal":{"name":"15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129565082","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: 15

Ensuring the integrity and non-repudiation of remitting e-invoices in conventional channels with commercially available NFC devices 确保使用商用NFC设备在传统渠道汇出电子发票的完整性和不可抵赖性

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888705

Shi-Cho Cha, Yuh-Jzer Joung, Yen-Chung Tseng, Shih-Chieh Huang, Guan-Heng Chen, Chih-Teng Tseng

{"title":"Ensuring the integrity and non-repudiation of remitting e-invoices in conventional channels with commercially available NFC devices","authors":"Shi-Cho Cha, Yuh-Jzer Joung, Yen-Chung Tseng, Shih-Chieh Huang, Guan-Heng Chen, Chih-Teng Tseng","doi":"10.1109/SNPD.2014.6888705","DOIUrl":"https://doi.org/10.1109/SNPD.2014.6888705","url":null,"abstract":"Despite the globally recognized advantages of e-invoicing and various efforts to implement such systems, retailers and stores may still have difficulties in promoting purely paperless e-invoices due to the lack of a convenient and secure way for consumers to receive and retrieve the e-invoices. As such, paper-based invoices may still be issued along with e-invoices, contradicting an important benefit of e-invoicing - paper consumption reduction. Thanks to the advances in smart phones and Near Field Communication (NFC) technologies, e-invoices can be delivered via NFC-enabled smartpones, allowing consumers to examine the content immediately after transactions and to easily retrieve them later on. Still, an extra security mechanism is needed to ensure the integrity and non-repudiation of the content, as invoices may bear some value and thus become the target of a security attack. In this paper, we propose a secure NFC-based e-invoice remitting scheme using standard NFC P2P communications, and discuss how it fulfills major security requirements, including authenticity, integrity, and non-repudiation. The proposed system is also implemented and tested in Taiwan's e-invoicing system.","PeriodicalId":272932,"journal":{"name":"15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674199","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

An improvement of micro area selection technique for the diffusion of ICT infrastructure 信息通信技术基础设施扩散的微区域选择技术改进

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888722

M. Iwashita, T. Kurosawa, A. Inoue, K. Nishimatsu

引用次数: 0

ONTOSSN: Scientific social network ontology ONTOSSN:科学的社会网络本体

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888677

Eya Ben Ahmed, Wafa Tebourski, W. Karaa, F. Gargouri

引用次数: 4

Detection range fitting of slit type Obrid-Sensor 狭缝式光学传感器的探测距离拟合

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888738

Shota Nakashima, Shenglin Mu, Tatsuya Ichikawa, Hiromasa Tomimoto, S. Okabe, Kanya Tanaka, Yuhki Kitazono, S. Serikawa

引用次数: 2

Strategy-guided requirements development and validation 战略引导的需求开发和验证

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888717

Xiaofeng Cui, R. Paige

引用次数: 3

An obfuscation method to build a fake call flow graph by hooking method calls 一种混淆方法，通过挂钩方法调用来构建假的调用流图

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888726

Kazumasa Fukuda, Haruaki Tamada

引用次数: 2

A machine learning technique for predicting the productivity of practitioners from individually developed software projects 一种机器学习技术，用于从单独开发的软件项目中预测从业者的生产力

15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2014-09-01 DOI: 10.1109/SNPD.2014.6888690

Cuauhtémoc López Martín, Arturo Chavoya-Pena, M. Meda-Campaña

{"title":"A machine learning technique for predicting the productivity of practitioners from individually developed software projects","authors":"Cuauhtémoc López Martín, Arturo Chavoya-Pena, M. Meda-Campaña","doi":"10.1109/SNPD.2014.6888690","DOIUrl":"https://doi.org/10.1109/SNPD.2014.6888690","url":null,"abstract":"Context: Productivity management of software developers is a challenge in Information and Communication Technology. Predictions of productivity can be useful to determine corrective actions and to assist managers in evaluating improvement alternatives. Productivity prediction models have been based on statistical regressions, statistical time series, fuzzy logic, and machine learning. Goal: To propose a machine learning model termed general regression neural network (GRNN) for predicting the productivity of software practitioners. Hypothesis: Prediction accuracy of a GRNN is better than a statistical regression model when these two models are applied for predicting productivity of software practitioners who have individually developed their software projects. Method: A sample obtained from 396 software projects developed between the years 2005 and 2011 by 99 practitioners was used for training the models, whereas a sample of 60 projects developed by 15 practitioners in the first months of 2012 was used for testing the models. All projects were developed based upon a disciplined development process within a controlled environment. The accuracy of the GRNN was compared against that of a multiple regression model (MLR). The criteria for evaluating the accuracy of these two models were the Magnitude of Error Relative to the estimate and a t-paired statistical test. Results: Prediction accuracy of an GRNN was statistically better than that of an MLR model at the 99% confidence level. Conclusion: An GRNN could be applied for predicting the productivity of practitioners when New and Changed lines of code, reused code, and programming language experience of practitioners are used as independent variables.","PeriodicalId":272932,"journal":{"name":"15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124203797","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: 8