TY - GEN
T1 - Browsing Behavior Analysis from Wi-Fi Logs Based on Community Detection
T2 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
AU - Saputri, Mei Silviana
AU - Wibisono, Ari
AU - Krisnadhi, Adila
AU - Yohanes, Adhi Yuniarto Laurentius
AU - Faisal, Teuku Amrullah
AU - Utama, Alvin Wardhana
AU - Ariefa, Muhammad Azmi Malik
AU - Ramadhani, Andre
AU - Muda, Aminur
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - As the growth of technology, the need of internet increases. Wireless internet technology or called Wi-Fi is dominate. Hence, monitoring the usage of Wi-Fi is crucial. Community detection is a technique which can be used to understand browsing behavior analysis. Using this approach, a website is represented as a node in a graph and the transition over websites is represented as an edge where the weight of an edge is calculated based on the frequency of website transition. In this study, we implement two community detection algorithms which support directed and weighted graph, i.e Girvan-Newman and Infomap to know the communities of websites accessed by Wi-Fi users. An evaluation metrics named modularity is employed to access the quality of both algorithms. The results of our experiment show that Infomap performs better to detect communities than Girvan-Newman. Based on browsing behavior analysis, it can be understood that the Wi-Fi network is mostly used for education, entertainment and government purposes. However, we also obtain insight regarding the misbehavior activities which need prevention.
AB - As the growth of technology, the need of internet increases. Wireless internet technology or called Wi-Fi is dominate. Hence, monitoring the usage of Wi-Fi is crucial. Community detection is a technique which can be used to understand browsing behavior analysis. Using this approach, a website is represented as a node in a graph and the transition over websites is represented as an edge where the weight of an edge is calculated based on the frequency of website transition. In this study, we implement two community detection algorithms which support directed and weighted graph, i.e Girvan-Newman and Infomap to know the communities of websites accessed by Wi-Fi users. An evaluation metrics named modularity is employed to access the quality of both algorithms. The results of our experiment show that Infomap performs better to detect communities than Girvan-Newman. Based on browsing behavior analysis, it can be understood that the Wi-Fi network is mostly used for education, entertainment and government purposes. However, we also obtain insight regarding the misbehavior activities which need prevention.
KW - Community detection
KW - Girvan-Newman
KW - Infomap
KW - Modularity
KW - Wi-Fi logs
UR - http://www.scopus.com/inward/record.url?scp=85055492612&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2018.8471720
DO - 10.1109/IWBIS.2018.8471720
M3 - Conference contribution
AN - SCOPUS:85055492612
T3 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
SP - 87
EP - 92
BT - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 May 2018 through 13 May 2018
ER -