EURASIP Journal on Information Security最新文献_第9页

Managing confidentiality leaks through private algorithms on Software Guard eXtensions (SGX) enclaves 通过软件保护扩展(SGX)飞地上的私有算法管理机密性泄漏

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-09-05 DOI: 10.1186/s13635-019-0091-5

Kubilay Ahmet Küçük, D. Grawrock, Andrew C. Martin

引用次数: 6

Situation prediction of large-scale Internet of Things network security 大规模物联网网络安全态势预测

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-08-28 DOI: 10.1186/s13635-019-0097-z

Wenjun Yang, Jiaying Zhang, Chundong Wang, Xiuliang Mo

{"title":"Situation prediction of large-scale Internet of Things network security","authors":"Wenjun Yang, Jiaying Zhang, Chundong Wang, Xiuliang Mo","doi":"10.1186/s13635-019-0097-z","DOIUrl":"https://doi.org/10.1186/s13635-019-0097-z","url":null,"abstract":"The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"182 ","pages":"1-9"},"PeriodicalIF":3.6,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506684","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

Crowdsourcing for click fraud detection 众包点击欺诈检测

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-07-22 DOI: 10.1186/s13635-019-0095-1

Riwa Mouawi, I. Elhajj, A. Chehab, A. Kayssi

引用次数: 13

Fine-grain watermarking for intellectual property protection 用于知识产权保护的细粒度水印

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-07-12 DOI: 10.1186/s13635-019-0094-2

S. Rizzo, Flavio Bertini, D. Montesi

引用次数: 30

Towards the application of recommender systems to secure coding 浅谈推荐系统在安全编码中的应用

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-06-13 DOI: 10.1186/s13635-019-0092-4

Fitzroy D. Nembhard, Marco M. Carvalho, Thomas C. Eskridge

引用次数: 16

A new method of generating hard random lattices with short bases 一种生成短基硬随机格的新方法

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-06-10 DOI: 10.1186/s13635-019-0088-0

Chengli Zhang, Wenping Ma, Hefeng Chen, Feifei Zhao

引用次数: 0

Metadata filtering for user-friendly centralized biometric authentication 元数据过滤，用户友好的集中生物识别认证

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-06-06 DOI: 10.1186/s13635-019-0093-3

Christian Gehrmann, Marcus Rodan, Niklas Jönsson

{"title":"Metadata filtering for user-friendly centralized biometric authentication","authors":"Christian Gehrmann, Marcus Rodan, Niklas Jönsson","doi":"10.1186/s13635-019-0093-3","DOIUrl":"https://doi.org/10.1186/s13635-019-0093-3","url":null,"abstract":"While biometric authentication for commercial use so far mainly has been used for local device unlock use cases, there are great opportunities for using it also for central authentication such as for remote login. However, many current biometric sensors like for instance mobile fingerprint sensors have too large false acceptance rate (FAR) not allowing them, for security reasons, to be used in larger user group for central identification purposes. A straightforward way to avoid this FAR problem is to either request a user unique identifier such as a device identifier or require the user to enter a unique user ID prior to making the biometric matching. Usage of a device identifier does not work when a user desires to authenticate on a previously unused device of a generic type. Furthermore, requiring the user at each login occasion to enter a unique user ID, is not at all user-friendly. To avoid this problem, we in this paper investigate an alternative, most user-friendly approach, for identification in combination with biometric-based authentication using metadata filtering. An evaluation of the adopted approach is carried out using realistic simulations of the Swedish population to assess the feasibility of the proposed system. The results show that metadata filtering in combination with traditional biometric-based matching is indeed a powerful tool for providing reliable, and user-friendly, central authentication services for large user groups.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"179 1","pages":"1-17"},"PeriodicalIF":3.6,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506685","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

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm 基于迭代权重优化算法的生物特征识别精度提高

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-05-28 DOI: 10.1186/s13635-019-0089-z

Pallavi Deshpande, P. Mukherji, A. Tavildar

引用次数: 5

A deep learning framework for predicting cyber attacks rates 用于预测网络攻击率的深度学习框架

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-05-22 DOI: 10.1186/s13635-019-0090-6

Xing Fang, Maochao Xu, Shouhuai Xu, Peng Zhao

引用次数: 42

Machine learning-based dynamic analysis of Android apps with improved code coverage 基于机器学习的Android应用动态分析，提高代码覆盖率

IF 3.6

EURASIP Journal on Information Security Pub Date : 2019-04-29 DOI: 10.1186/s13635-019-0087-1

Suleiman Y. Yerima, Mohammed K. Alzaylaee, Sakir Sezer

{"title":"Machine learning-based dynamic analysis of Android apps with improved code coverage","authors":"Suleiman Y. Yerima, Mohammed K. Alzaylaee, Sakir Sezer","doi":"10.1186/s13635-019-0087-1","DOIUrl":"https://doi.org/10.1186/s13635-019-0087-1","url":null,"abstract":"This paper investigates the impact of code coverage on machine learning-based dynamic analysis of Android malware. In order to maximize the code coverage, dynamic analysis on Android typically requires the generation of events to trigger the user interface and maximize the discovery of the run-time behavioral features. The commonly used event generation approach in most existing Android dynamic analysis systems is the random-based approach implemented with the Monkey tool that comes with the Android SDK. Monkey is utilized in popular dynamic analysis platforms like AASandbox, vetDroid, MobileSandbox, TraceDroid, Andrubis, ANANAS, DynaLog, and HADM. In this paper, we propose and investigate approaches based on stateful event generation and compare their code coverage capabilities with the state-of-the-practice random-based Monkey approach. The two proposed approaches are the state-based method (implemented with DroidBot) and a hybrid approach that combines the state-based and random-based methods. We compare the three different input generation methods on real devices, in terms of their ability to log dynamic behavior features and the impact on various machine learning algorithms that utilize the behavioral features for malware detection. Experiments performed using 17,444 applications show that overall, the proposed methods provide much better code coverage which in turn leads to more accurate machine learning-based malware detection compared to the state-of- the- art approach.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"218 1","pages":"1-24"},"PeriodicalIF":3.6,"publicationDate":"2019-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506688","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: 22