Network traffic analysis using Machine Learning Techniques in IoT Network

IF 0.6 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
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引用次数: 1

Abstract

Internet of things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. Cyber threats would become potentially harmful and lead to infecting the machines, disrupting the network topologies, and denying services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation prevent the network from malicious traffics. In this research, a support vector machine learning technique was used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and prevent the network from malicious traffic. Static and dynamic analysis of malware has been done. Mininet emulator was selected for network design, VMware fusion for creating a virtual environment, hosting OS was Ubuntu Linux, network topology was a tree topology. Wireshark was used to open an existing pcap file that contains network traffic. The support vector machine classifier demonstrated the best performance with 99% accuracy.
物联网网络中使用机器学习技术的网络流量分析
物联网设备不是很智能,而且资源有限;因此,他们很容易受到网络威胁。网络威胁可能会变得有害,并导致感染机器,破坏网络拓扑,拒绝向合法用户提供服务。人工智能驱动的方法和先进的基于机器学习的网络调查可以防止网络遭受恶意流量。在本研究中,使用支持向量机学习技术对正常和异常流量进行分类。已经进行了网络流量分析,以检测和防止网络中的恶意流量。对恶意软件进行了静态和动态分析。网络设计选用Mininet模拟器,VMware融合创建虚拟环境,主机操作系统为Ubuntu Linux,网络拓扑为树形拓扑。Wireshark被用来打开一个包含网络流量的现有pcap文件。支持向量机分类器的性能最好,准确率达到99%。
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来源期刊
International Journal of Software Innovation
International Journal of Software Innovation COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
1.40
自引率
0.00%
发文量
118
期刊介绍: The International Journal of Software Innovation (IJSI) covers state-of-the-art research and development in all aspects of evolutionary and revolutionary ideas pertaining to software systems and their development. The journal publishes original papers on both theory and practice that reflect and accommodate the fast-changing nature of daily life. Topics of interest include not only application-independent software systems, but also application-specific software systems like healthcare, education, energy, and entertainment software systems, as well as techniques and methodologies for modeling, developing, validating, maintaining, and reengineering software systems and their environments.
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