Effectiveness of Sniffer Using Natural Language in Learning Computer Network Traffic

Abstract

Computer networks are currently very active in the development of technology that is around us. Seeing this, of course knowledge of the network will be needed if there is a problem on the network. Scapy is a Python module that allows for sending, sniffing and dissecting a packet on a network. This capability allows users to create an application that can dissect how the workings of a network packet. Researchers will create a protocol traffic learning application on a computer network using Scapy and natural language to convey the results of the ongoing sniffing process. The application uses natural language to convey the translation of the sniffing process. The translation result of the sniffing process by using the natural language of this application is expected to be effective and can facilitate and make users understand and learn about the work process of a network packet. To measure the effectiveness of the use of natural language for the translation of the sniffing process a questionnaire was distributed to students of the SMKN 1 Denpasar school majoring in Computer and Network Engineering. The results of the distribution of the questionnaire were then calculated using a Likert scale and then the results obtained that the original results of the sniffing process got a Likert scale value of 37%. While the results of sniffing that have been translated get a value of 73%. This shows respondent better understands the results that have been translated compared to the original results that have not been translated.