大规模物联网网络安全态势预测

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS 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
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引用次数: 6

摘要

物联网(Internet of Things, IoT)是近年来在各个领域迅速发展起来的一项新技术。随着物联网技术在生产和生活中的不断应用,物联网的网络安全问题日益突出。为了应对物联网技术发展带来的挑战,本文重点研究网络安全态势感知。网络安全态势感知是物联网网络安全的基础。网络安全态势预测本质上是一种时间序列预测问题。因此,有必要构造适合于时间序列数据的修正函数来修正传统支持向量机(SVM)的核函数。提出了一种改进的物联网网络安全态势感知模型。得到序列核支持向量机,并采用粒子群优化方法对相关参数进行优化。通过对某高校校园物联网边界数据的采集,验证了该方法的可行性。最后,通过与PSO-SVM的比较,证明了该方法在提高物联网网络安全态势预测精度方面的有效性。实验结果表明,pso -时间序列核支持向量机在网络安全态势预测方面优于pso -高斯核支持向量机。Hadoop平台的应用也提高了数据处理的效率。
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Situation prediction of large-scale Internet of Things network security
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.
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来源期刊
EURASIP Journal on Information Security
EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
8.80
自引率
0.00%
发文量
6
审稿时长
13 weeks
期刊介绍: The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal. Topics relevant to the journal include, but are not limited to: • Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion) • Steganography and Steganalysis • Fingerprinting and traitor tracing • Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography • Biometrics (fusion, multimodal biometrics, protocols, security issues) • Digital forensics • Multimedia signal processing approaches tailored towards adversarial environments • Machine learning in adversarial environments • Digital Rights Management • Network security (such as physical layer security, intrusion detection) • Hardware security, Physical Unclonable Functions • Privacy-Enhancing Technologies for multimedia data • Private data analysis, security in outsourced computations, cloud privacy
期刊最新文献
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