{"title":"Network traffic analysis using Machine Learning Techniques in IoT Network","authors":"","doi":"10.4018/ijsi.289172","DOIUrl":null,"url":null,"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.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Software Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijsi.289172","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
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.
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.