一种基于机器学习算法的网络安全评估方法

互联网给人们生活和工作带来便利的同时,也增加了网络安全风险发生的概率.为减少这一危害的出现,提出一种基于机器学习的网络安全预测评估方法,该方法是利用支持向量机的非线性拟合能力和果蝇优化算法的全局优化能力,在时间序列的基础上建立网络安全态势评估模型.所提出的预测模型的决定系数为0.906997774231,最佳核函数参数...     

蝙蝠算法优化最二乘支持向量机的网络入侵检测

针对最小二支持向量机(LSSVM)参数选择难题,提出一种蝙蝠(BA)算法优化的LSSVM网络入侵检测模型(BA-LSSVM)。首先将LSSVM参数编码为蝙蝠个体,并以网络入侵检测正确率作为参数目标优化函数,然后通过模拟蝙蝠飞行过程找到LSSVM最优参数,最后根据最优参数建立网络入侵检测模型。在Matlab2012平台采用KDD CUP 99数据集进行仿真测试。仿真结果表明,相对于其它网络入侵检测模型,BA-LSSVM提高了网络入侵检测检测率,加快了网络入侵检测速度。     

人工蜂群优化支持向量机算法在网络安全中的应用

针对支持向量机(SVM)性能的影响,探讨了人工蜂群算法(ABC)对SVM参数优化方法,建立了SVM参数优化模型,并将其用于网络安全中的网络入侵模型中.采用KDD 1999数据集进行仿真实验,验证了方法的有效性,结果表明,与遗传算法等传统优化算法相比,ABC优化的SVM有效地降低运行时间,可以获得更高的网络入侵检测率.     

基于粒子群算法的网络安全评估优化方案设计

为了进一步改善网络安全评估的效果,提出一种基于粒子群算法的网络安全评估优化方案,网络安全评估是保护互联网和信息系统免受恶意攻击的关键任务,因此提高评估效果对于信息安全领域具有重要意义。介绍了一个融合PSO算法和贝叶斯网络的网络安全评估方案,目标是提升网络安全评估的准确性和效率。方案的流程包括数据处理、模式构建、关联数据挖掘、粒子群算法和贝叶斯网络训练等步骤。实验证明,所提出的方案比其他方法更准确,并可以识别各种攻击行为,为网络管理员提供决策支持。综上所述,所提出方法的应用将为网络安全领域的决策和应用提供有力的支持,促进信息系统的安全保护,未来的研究可以进一步探索如何应用该方法来解决更复杂的网络安全问题。     

改进蝙蝠算法优化极限学习机的图像分类

针对分类器中的极限学习机参数优化问题,本文提出一种改进蝙蝠算法优化极限学习机的图像分类模型。首先将极限学习机参数看作蝙蝠位置,然后采用改进蝙蝠算法进行求解。采用病毒群体感染主群体,主群体在历代个体间纵向传递信息,病毒群体通过感染操作在同代个体间横向传递信息,增强了算法跳出局部极小值的能力。最后根据最优参数建立图像分类模型,并对模型的性能进行仿真测试。仿真结果表明,相对于对比模型,本文模型不仅提高了图像分类正确率,而且加快了分类速度,是一种有效的图像分类模型。     

融合正弦余弦的蝙蝠算法

蝙蝠算法(Bat Algorithm,BA)是一种新颖的随机型搜索优化算法,针对蝙蝠算法收敛速度慢、寻优精度低的不足,提出了一种融合正弦余弦的蝙蝠算法(SCABA),即在算法迭代后期,引入正弦余弦操作来更新当前蝙蝠个体的位置,从而避免算法陷入局部最优,增强算法的全局寻优能力.通过6个标准测试函数对改进算法、MFBA和基本BA进行测试比较,仿真结果表明,改进算法是可行有效的,相比于基本BA算法,其收敛精度和鲁棒性有了很大程度地提高.     

基于贝叶斯算法的网络安全评估模型研究

网络安全评估涉及两个方面,即技术和管理.评估中存在大量不确定性,无法完全量化.因此,很难实现完全客观的网络信息安全风险评估.为此,该文提出了一种基于贝叶斯算法的网络安全评估模型,以分析和量化由各种威胁源引起的网络安全风险.通过专家知识的整合,阐明了贝叶斯算法的推理规则的条件概率矩阵,综合了专家对目标信息系统威胁程度的主...     

蝙蝠算法优化神经网络的无线传感器网络数据融合

为了降低无线传感器网络数据间的冗余性,提高数据传输量和降低通信能耗,提出一种蝙蝠算法优化神经网络算法的数据融合策略.首先每个簇首节点接收该区域的各传感器节点检测到的数据,然后采用蝙蝠算法优化BP神经网络进行数据融合,最后采用仿真实验对其性能进行测试.仿真结果表明,本文算法节省了感知节点的能量消耗,延长了无线传感器网络的生命周期时间,提高数据融合的精度.     

一种基于RVM回归的分类方法

支持向量机是用于分类与回归的技术.由于其自身的诸多缺点,如无法获得概率输出,需要估计一个误差参数C,以及必须使用Mercer核函数等.相关向量机算法,克服了SVM上述缺点,RVM能获得与SVM相比拟的推广性能,并且更为稀疏.在此基础上,文中介绍了一种RVM回归用于分类的新分类方法,用RVRC来表示.并通过实验证明了它的...     

网络安全评估方法研究进展

随着IT业迅速发展,网络安全也越来越受到重视。一种有效的网络安全评估方法不仅可以发现已有的网络安全漏洞,而且能够找出网络中潜在的漏洞并通过数学方法定位网络中最易受到攻击的节点,从而使企业网络能够在新一轮的网络攻击中存活下来,企业业务得以继续展开。文中总结了各种网络安全的评估方法并对其加以分类,在此基础上对网络安全评估方法的未来发展做了展望。     

Hybrid firefly bat algorithm (HFBA)–based network security policy enforcement for PSA

Network operators heavily depend on security services to secure their information technology infrastructures. On the other hand, due to the complexity of security policies, it is not appropriate to straightforwardly use previous pathwise enforcement approaches. In this paper, the enforcement problem of the security policy on middleboxes is formulated as a weighted K set covering problem that requires a policy space analysis tool. This tool is intended to be supported on range‐represented hyperrectangles, which are tagged using a prioritized R‐tree. This methodological work initially evaluates the topological features of diverse types of policies. Hybrid firefly bat algorithm–supported heuristic information shows the inherent difficulties of security policies and provides direction for the design of the enforcement algorithm. At the same time, a scopewise policy enforcement procedure is proposed, which requires a moderate number of enforcement network nodes for organizing the various policy subsets in a greedy manner. Our results demonstrate that the proposed hybrid firefly bat algorithm with policy space analysis offer greatly improved outcomes in terms of the rule overhead, network security, packet delivery ratio, packet loss ratio, and time efficiency above the set operations of the security policy.     

基于元胞蝙蝠算法的无线传感器网络节点定位研究

为了提高节点定位精度,解决定位误差较大的问题,提出了基于元胞蝙蝠算法的无线传感器网络节点定位算法,以此来获得更高的定位精度。首先将元胞自动机的思想融入蝙蝠算法,采用了改进的元胞限制竞争选择小生境技术和灾变机制,使得该算法在寻优过程中能够跳出局部极值,避免早熟现象,更快地收敛到全局最优解。通过标准测试函数的验证,表明了该改进算法在收敛深度和广度上的优势。之后将元胞蝙蝠算法应用到无线传感器网络节点定位上来提高定位精度。实测实验中,该算法在测试环境下平均定位误差在0.4 m以内,相比于改进PSO算法,获得更好的定位效果。     

群智能算法优化神经网络在网络安全的应用研究

网络安全是当前网络管理领域研究中的重点,针对BP神经网络的阈值和连接权值优化问题,提出一种群智能算法优化BP神经网络参数的方法,并将其应用于网络安全.首先对群智能算法中的生物地理学算法进行改进,加快其收敛速度,然后采用改进生物地理学算法择BP神经网络的阈值和连接权值,最后采用网络入侵数据集对其有效性和优越性进行测试.结果表明,生物地理学算法可以快速找到BP神经网络的最优阈值和连接权值,提高了网络入侵检测的正确率,可以有效的保护网络系统的安全.     

基于网络安全维护的计算机网络安全技术运用

随着我国计算机网络技术不断发展,计算机网络安全问题也得到了社会各个阶层的关注.基于此,本文重点探究影响计算机网络安全的主要因素,进而提出计算机网络安全技术的应用.     

A network security situation prediction model based on wavelet neural network with optimized parameters

The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN) with optimized parameters by the Improved Niche Genetic Algorithm (INGA). The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA) so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) and WNN.     

基于蝙蝠算法的模糊综合评价的英语教学质量评价研究

针对模糊综合评价法权重分配缺乏理论基础和完全凭经验的缺点,提出一种基于蝙蝠算法(Bat Algorithm,BA)的模糊综合评价模型。通过BA优化模糊综合评价法的权重,实现权重的最优化选择。运用模糊评价矩阵构建确定各个评价指标权重的判断矩阵,运用BA算法对判断矩阵的权重进行自适应选择。研究结果表明,BA优化模糊综合评价法的结果更加符合实际情况,效果较好。     

Proficient link state routing in mobile ad hoc network-based deep Q-learning network optimized with chaotic bat swarm optimization algorithm

Mobile ad-hoc network (MANET) is a category of ad-hoc network that can be reconfigurable its network. MANETS are self-organized networks, that can use the wireless links to connect various networks via mobile nodes: but it consumes more energy and it also has routing problems. This is the major drawback of being connected with the MANET technology. Therefore, this study proposes a new protocol as deep Q-learning network optimized with chaotic bat swarm optimization algorithm (CBS)-based optimized link state routing (OLSR) (CBS-OLSR) for MANET. This protocol reduces MANET energy usage and adopts OLSR multi-point relay (MPR) technology. MANET's OLSR and the CBS algorithm utilize a similar method to locate the best optimum path from source to destination node. By embedding the new improved deep Q-learning and OLSR algorithms, both are used for optimizing the MPR sets selection, it can efficiently diminish the energy consumption in the network topology, but automatically increase the lifespan of the network. It also enhances the package delivery ratio and decreases end-to-end delay. The experimental outcomes prove that the proposed protocol is reliable and proficient that is appropriate for numerous MANET applications.     

基于复Givens矩阵与蝙蝠优化的卷积盲分离算法

针对传统卷积混合盲分离待求参数多、分离效果易受分离矩阵初值影响的局限性,提出了基于复Givens矩阵与蝙蝠优化的频域求解算法。算法采用复Givens矩阵表示分离矩阵,减少了待求参数,降低了求解难度和计算量。利用蝙蝠算法代替梯度算法优化求解旋转角度完成各频点线性瞬时混合复信号的盲分离,全局收敛性更强。此外,由于对源信号的先验知识要求较少,可以分离服从多种分布的信号。仿真实验表明,该算法可有效地实现卷积混合盲分离。     

基于攻击图模型的网络安全评估研究

介绍了利用攻击图模型方法分析计算机网络攻击行为的基本原理,给出了攻击图生成算法。研究了利用攻击图对网络系统安全性进行风险分析的方法。通过一个虚拟的网络环境对攻击图生成算法和网络系统的安全性分析评估方法进行了验证。     

Survey on network system security metrics

With the improvement for comprehensive and objective understanding of the network system,the research and application of network system security metrics (NSSM) are noticed more.The quantitative evaluation of network system security is developing towards precision and objectification.NSSM can provide the objective and scientific basis for the confrontation of attack-defense and decision of emergency response.The global metrics of network system security is a crucial point in the field of security metrics.From the perspective of global metrics,the status and role of global metrics in security evaluation were pointed out.Three development stages of metrics (perceiving,cognizing and deepening) and their characteristics were analyzed and summarized.The process of global metrics was described.The metrics models,metrics systems and metrics tools were analyzed,and their functions,interrelations,and features in security metrics were pointed out.Then the technical challenges of global metrics of network systems were explained in detail,and ten opportunities and challenges were summarized in tabular form.Finally,the next direction and development trend of network system security metrics research were forecasted.The survey shows that NSSM has a good application prospect in network security.