TY - GEN
T1 - Malware Analysis and Classification Using Grid Search Optimization
AU - Ilham, Karina Fitriwulandari
AU - Ahmad, Tohari
AU - Putra, Muhammad Aidiel Rachman
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Internet of Things (IoT) technology is experiencing rapid growth, but security and privacy remain a major concern. This is because IoT devices are vulnerable to being targets of malware attacks. On the other hand, detecting malware in IoT networks is challenging. Previous research has introduced several methods for IoT malware detection using several Machine Learning algorithms. However, only a few studies discussed parameter optimization in machine learning models. Thus, this research aims to develop a malware detection model using hyperparameter optimization with grid search in several Machine Learning algorithms. Several Machine Learning algorithms are utilized, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and k Nearest Neighbors (k-NN). This research aims to improve IoT network security by developing a model to mitigate and detect the presence of malware attacks. The experiment using the IoT23 dataset shows a good result with the RF model. RF obtained the best result, achieving accuracy of 99.09%, precision of 98.54%, recall of 99.05%, and an F1 score of 98.79%.
AB - Internet of Things (IoT) technology is experiencing rapid growth, but security and privacy remain a major concern. This is because IoT devices are vulnerable to being targets of malware attacks. On the other hand, detecting malware in IoT networks is challenging. Previous research has introduced several methods for IoT malware detection using several Machine Learning algorithms. However, only a few studies discussed parameter optimization in machine learning models. Thus, this research aims to develop a malware detection model using hyperparameter optimization with grid search in several Machine Learning algorithms. Several Machine Learning algorithms are utilized, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and k Nearest Neighbors (k-NN). This research aims to improve IoT network security by developing a model to mitigate and detect the presence of malware attacks. The experiment using the IoT23 dataset shows a good result with the RF model. RF obtained the best result, achieving accuracy of 99.09%, precision of 98.54%, recall of 99.05%, and an F1 score of 98.79%.
KW - Grid Search
KW - Information Security
KW - Internet of Things
KW - Intrusion Detection System
KW - Malware
KW - Network Security
UR - http://www.scopus.com/inward/record.url?scp=85211116080&partnerID=8YFLogxK
U2 - 10.1109/ICCCNT61001.2024.10725236
DO - 10.1109/ICCCNT61001.2024.10725236
M3 - Conference contribution
AN - SCOPUS:85211116080
T3 - 2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
BT - 2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
Y2 - 24 June 2024 through 28 June 2024
ER -