TY - GEN
T1 - NetMiner
T2 - 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
AU - Sathi, Gowtham Reddy
AU - Vedullapalli, Lokesh
AU - Kishan, MacHarla Hemanth
AU - Zaman, Tarannum Shaila
AU - Islam, Md Tariqul
AU - Badr, Mahmoud M.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To ensure uninterrupted network communication among multiple sources, the network administrator needs to do real-time monitoring of network logs. Due to its huge volume and streaming nature, it is almost impossible to do a manual analysis of the logs generated by different network monitoring software. Moreover, these logs may contain a limited amount of data regarding the identity, location, capacity, etc. of a user due to privacy issues. Therefore, we propose an automated system named NetMiner for performing log analysis that requires only insensitive data such as the network's current status and messages related to that status. We collect recent 30 days logs using the network monitoring tool PRTG (Paessler Router Traffic Grapher) and perform data analysis by leveraging a data mining technique called Fpmax. Our proposed technique NetMiner can identify patterns that may lead to failure and warn the network administrator about upcoming failures. Our experimental results show that NetMiner is both effective and efficient in identifying failure-inducing patterns in logs generated by network monitoring software.
AB - To ensure uninterrupted network communication among multiple sources, the network administrator needs to do real-time monitoring of network logs. Due to its huge volume and streaming nature, it is almost impossible to do a manual analysis of the logs generated by different network monitoring software. Moreover, these logs may contain a limited amount of data regarding the identity, location, capacity, etc. of a user due to privacy issues. Therefore, we propose an automated system named NetMiner for performing log analysis that requires only insensitive data such as the network's current status and messages related to that status. We collect recent 30 days logs using the network monitoring tool PRTG (Paessler Router Traffic Grapher) and perform data analysis by leveraging a data mining technique called Fpmax. Our proposed technique NetMiner can identify patterns that may lead to failure and warn the network administrator about upcoming failures. Our experimental results show that NetMiner is both effective and efficient in identifying failure-inducing patterns in logs generated by network monitoring software.
KW - Data Mining
KW - Frequent Maximal Patterns
KW - Log Analysis
KW - Network Monitoring Software
KW - Sequential Pattern Mining
UR - http://www.scopus.com/inward/record.url?scp=85179839399&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179839399&partnerID=8YFLogxK
U2 - 10.1109/ICCCNT56998.2023.10308051
DO - 10.1109/ICCCNT56998.2023.10308051
M3 - Conference contribution
AN - SCOPUS:85179839399
T3 - 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
BT - 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 July 2023 through 8 July 2023
ER -