A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.     

内存映射文件及其在广义舒适度仿真中的应用

本文分析了win32系统下的虚拟内存系统模式及内存映射文件的工作机理,介绍了如何用windows API将文件映射至内存,利用原型页表入口技术实现了多个进程之间的数据共享。通过实验数据得出,采用内存映射文件读取高速服役列车的广义舒适度仿真中的超大数据文件,其效率远远高于传统的基于文件I/O方式的数据存取。     

A lightweight authentication scheme based on self‐updating strategy for space information network

The security of space information network (SIN) is getting more and more important now. Because of the special features of SIN (e.g., the dynamic and unstable topology, the highly exposed links, the restricted computation power, the flexible networking methods, and so on), the security protocol for SIN should have a balance between security properties and computation/storage overhead. Although a lot of security protocols have been proposed recently, few can provide overall attacks resistance power with low computation and storage cost. To solve this problem, in this paper we propose a lightweight authentication scheme for space information network. It is mainly based on the self‐updating strategy for user's temporary identity. The scheme consists of two phases, namely, the registration phase and the authentication phase. All the computing operations involved are just hash function (h), the bit‐wise exclusive‐or operation (⊕), and the string concatenation operation (||), which are of low computation cost. The security properties discussion and the attacks–resistance power analysis show that the proposed authentication scheme can defend against various typical attacks, especially denial of service attacks. It is sufficiently secure with the lowest computation and storage costs. Furthermore, the formal security proof in SVO logic also demonstrates that the scheme can satisfy the security goals very well. Copyright © 2016 John Wiley & Sons, Ltd.     

SERVQUAL模型在医疗行业的应用设计研究

本文探讨了感知服务评价模型-SERvQuAL模型在医疗行业的适用性,结合医疗服务特点,建立了以沟通性为前导变量的SERVQUAL的医疗感知服务质世理论模型.     

中国通信转型战略管理模型构建与实证

随着我国社会经济体制变革的不断深入,通信企业在快速发展的同时,实现战略转型管理是提高企业综合的重要途径。本文论述了中国通信浙江省通信产业服务有限公司宁波市分公司转型的五个发展阶段,分析了通信企业转型战略管理模型构建遇到的难题,阐述了目前企业管理模式中存在的问题,针对问题进行研究与分析,提出在新的社会形势下,通信企业应该要如何实现转型,构建新型的管理模式,使得企业能更好地应对激烈的市场竞争。希望通过本文分析能实现通信企业的转型,并且顺利实现现代化管理。     

网格环境中可靠性增强的资源预留策略

提出一种基于随机服务模型的可靠性量化计算方法,该策略采用随机服务系统描述网格资源的预留服务模型,通过分析预留请求的统计特征来评估接纳当前预留请求不发生违约的概率,然后依据备选资源集合中的违约概率来进行预留请求接纳和任务资源选择,从而提高资源预留时的可靠性。实验表明,与传统的预留策略相比,该策略在提高资源利用率、降低违约率以及对动态负载压力变化情况下的反应方面,表现出更好的有效性、适应性。     

位置业务技术研究及部署规划探讨

位置业务被誉为继短消息之后移动通信领域的又一杀手锏.为适应发展形势的需要,文中从多方面对不同类型的位置业务进行说明,然后给出位置业务平台实施方案,重点就现网部署及异地定位等关键问题进行探讨,最后给出了实际部署规划过程中的性能需求,所得结果具有理论意义及实用参考价值.     

基于Web Services数据抽取的研究与实践

通过对Web Services技术和数据抽取的研究,提出了基于Web Services技术的数据抽取解决方案。重点介绍了增量数据自动化产生方法,提出了基于Web Services技术的解决方案,该方案是建立在数据端到端安全传输规范和并行数据抽取的基础之上,通过使用简单的标准适配器和接口,来完成粗粒度数据交换服务和高效的数据传输,能够满足大型异构企业环境的数据交换需求。基于Web Services技术的数据抽取提高了数据抽取的安全性和执行效率,使企业数据交换更加方便、灵活、可靠,伸缩性也更强。     

电信运营商M2M运营管理平台探讨

M2M运营管理平台是电信运营商汇聚行业应用,为行业应用提供业务能力的重要平台。本文首先分析了国际标准化组织对M2M的基本业务需求及功能架构,然后从电信运营商实际网络运营情况和M2M行业应用特点出发,探讨了M2M运营管理平台的功能架构。同时,提出了一种基于M2M平台扩展的M2M终端/网关通信管控方法,为行业应用提供全面的通信管理服务。     

基于上下文相似度和社会网络的移动服务推荐方法

针对传统的基于协同过滤的移动服务推荐方法存在的数据稀疏性和用户冷启动问题,提出一种基于上下文相似度和社会网络的移动服务推荐方法(Context-similarity and Social-network based Mobile Service Recommendation,CSMSR).该方法将基于用户的上下文相似度引入个性化服务推荐过程,并挖掘由移动用户虚拟交互构成的社会关系网络,按照信任度选取信任用户;然后结合基于用户评分相似度计算发现的近邻,分别从相似用户和信任用户中选择相应的邻居用户,对目标用户进行偏好预测和推荐.实验表明,与已有的服务推荐方法TNCF、SRMTC及CF-DNC相比,CSMSR方法有效地缓解数据稀疏性并提高推荐准确率,有利于发现用户感兴趣的服务,提升用户个性化服务体验.