TY - GEN
T1 - HyDeck
T2 - 9th International Conference on Information Technology and Digital Applications, ICITDA 2024
AU - Mandala Putra, Raden Bimo
AU - Sanjani, Lukman Arif
AU - Renggana, Christiant Dimas
AU - Idris, Fahmi
AU - Shiddiqi, Ary Mazharuddin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The Internet of Things (IoT) is currently experiencing rapid growth in various sectors of human life, leading to significant advancements in technology and convenience. However, this rapid development is accompanied by security issues that pose threats to users. Traditional firewall technology falls short in identifying abnormal activities in network traffic, highlighting the necessity for an effective intrusion detection system (IDS). This research addresses this need by developing a model for intrusion detection that combines K-Means clustering, K-Nearest Neighbors (KNN), and Decision Tree algorithms to enhance performance on imbalanced datasets. To ensure the robustness of the model, the data is divided into several folds and subjected to a thorough cross-validation process. The results indicate that the developed model achieves better accuracy for each data fold compared to a standard decision tree classifier. This improvement underscores the significant potential of hybrid models in greatly enhancing the security and reliability of IoT networks.
AB - The Internet of Things (IoT) is currently experiencing rapid growth in various sectors of human life, leading to significant advancements in technology and convenience. However, this rapid development is accompanied by security issues that pose threats to users. Traditional firewall technology falls short in identifying abnormal activities in network traffic, highlighting the necessity for an effective intrusion detection system (IDS). This research addresses this need by developing a model for intrusion detection that combines K-Means clustering, K-Nearest Neighbors (KNN), and Decision Tree algorithms to enhance performance on imbalanced datasets. To ensure the robustness of the model, the data is divided into several folds and subjected to a thorough cross-validation process. The results indicate that the developed model achieves better accuracy for each data fold compared to a standard decision tree classifier. This improvement underscores the significant potential of hybrid models in greatly enhancing the security and reliability of IoT networks.
KW - Decision Tree
KW - Internet of Things
KW - Intrusion Detection System
KW - K-Means
KW - K-Nearest Neighbors
UR - https://www.scopus.com/pages/publications/85216560409
U2 - 10.1109/ICITDA64560.2024.10809767
DO - 10.1109/ICITDA64560.2024.10809767
M3 - Conference contribution
AN - SCOPUS:85216560409
T3 - Proceeding of 2024 9th International Conference on Information Technology and Digital Applications, ICITDA 2024
BT - Proceeding of 2024 9th International Conference on Information Technology and Digital Applications, ICITDA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 November 2024 through 8 November 2024
ER -