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Machine Learning and Its Applicability in Networking
Published in Gulshan Shrivastava, Sheng-Lung Peng, Himani Bansal, Kavita Sharma, Meenakshi Sharma, New Age Analytics, 2020
Another efficient methodology to gather data either offline or online is in utilization of tools related to measurement and monitoring of data. With the help of these tools several aspects related to networking like sampling rate, location, and duration of monitoring can be managed. In particular, various protocols meant for monitoring of network like simple network management protocol (SNMP), IP flow information export (IPFIX) can be utilized in monitoring (Harrington et al., 2002; Claise, 2008). Further, there exist active as well as passive monitoring (Fraleigh, 2001). In the case of active monitoring, probing is being performed in the network to collect required data from the network traffic. In contrary, the data is collected by learning from the behavior of actual data across the network. After collection, the data is splitted into various parts involving training, validation, and testing. On the basis of training dataset, the interconnectivity between the nodes is adjudged for designing a ML prototype. Further, the validation aids in selecting a framework from the available set of guidelines. In the last, testing of dataset is being performed to check the performance level of the designed prototype.
Malware detection for IoT devices using hybrid system of whitelist and machine learning based on lightweight flow data
Published in Enterprise Information Systems, 2023
Masataka Nakahara, Norihiro Okui, Yasuaki Kobayashi, Yutaka Miyake, Ayumu Kubota
IP Flow Information Export (IPFIX), standardised by the Internet Engineering Task Force (IETF) in RFC7011(Claise, Trammell, and Aitken 2013), is a commonly available flow information. IPFIX was designed to aggregate traffic flows at specific points and manage the network, making it suitable for use in this research. An example of the fields included in an IPFIX record is shown in Table 2.