基于软件定义的网络,使用机器学习技术对网络流量进行分类
Ayodeji Olalekan Salau1,2, Melesew Mossie Beyene3
1Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria. ayodejisalau98@gmail.com.
Scientific reports
|August 29, 2024
概括
这项研究将机器学习 (ML) 与软件定义网络 (SDN) 集成,以高效地分类网络流量. 决策树模型实现了99.81%的准确性,提高了服务质量 (QoS) 和加密流量检测.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 机器学习 机器学习
背景情况:
- 传统的网络流量分类方法由于加密和动态流量而失败.
- 现有的软件定义网络 (SDN) 和机器学习 (ML) 方法在准确性和实时检测方面存在局限性.
- 需要强大的方法来有效地分类各种网络流量类型.
研究的目的:
- 评估各种监督和无监督机器学习 (ML) 模型在SDN环境中的网络流量分类的有效性.
- 为了比较不同的ML算法在分类域名系统 (DNS),Telnet,Ping和语音流量的性能.
- 展示一种集成的ML-SDN方法,用于准确高效的实时流量分类.
主要方法:
- 使用分布式互联网流量生成器 (D-ITG) 工具模拟网络流量.
- 实施监督的 (物流回归,决策树,随机森林,AdaBoost,支持矢量机) 和无监督的 (K-means集群) ML模型.
- 利用软件定义网络 (SDN) 在 Mininet 中实现网络架构和流量生成,在 Anaconda Python 环境中进行分类.
主要成果:
- 决策树监督学习模型实现了最高的分类准确率99.81%.
- 拟议的ML-SDN集成与其他测试的算法相比,在线和实时流量方面表现出优越的性能.
- 该方法有效地分类了各种流量类型,包括加密包.
结论:
- 将机器学习 (ML) 与软件定义网络 (SDN) 集成,为网络流量分类提供了高效和准确的解决方案.
- 这种方法提高了服务质量 (QoS),可以检测加密的数据包,并支持深度数据包检查.
- 决策树模型显示了对实时网络流量管理和安全的重大前景.
相关概念视频
01:30Classification of Signals
420
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
420
01:26Classification of Systems-I
177
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
177
01:31Classification of Systems-II
137
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
137