Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm
As the number of connected devices and Internet of Things (IoT) devices grows, it is becoming more and more important to develop efficient security mechanisms to manage risks and vulnerabilities in IoT networks. Intrusion detection systems (IDSs) have been developed and implemented in IoT networks t...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-03-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2745.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849390104437587968 |
|---|---|
| author | Hussein Ridha Sayegh Wang Dong Bahaa Hussein Taher Muhanad Mohammed Kadum Ali Mansour Al-madani |
| author_facet | Hussein Ridha Sayegh Wang Dong Bahaa Hussein Taher Muhanad Mohammed Kadum Ali Mansour Al-madani |
| author_sort | Hussein Ridha Sayegh |
| collection | DOAJ |
| description | As the number of connected devices and Internet of Things (IoT) devices grows, it is becoming more and more important to develop efficient security mechanisms to manage risks and vulnerabilities in IoT networks. Intrusion detection systems (IDSs) have been developed and implemented in IoT networks to discern between regular network traffic and potential malicious attacks. This article proposes a new IDS based on a hybrid method of metaheuristic and deep learning techniques, namely, the flower pollination algorithm (FPA) and deep neural network (DNN), with an ensemble learning paradigm. To handle the problem of imbalance class distribution in intrusion datasets, a roughly-balanced (RB) Bagging strategy is utilized, where DNN models trained by FPA on a cost-sensitive fitness function are used as base learners. The RB Bagging strategy derives multiple RB training subsets from the original dataset and proper class weights are incorporated into the fitness function to attain unbiased DNN models. The performance of our IDS is evaluated using four commonly utilized public datasets, NSL-KDD, UNSW NB-15, CIC-IDS-2017, and BoT-IoT, in terms of different metrics, i.e., accuracy, precision, recall, and F1-score. The results demonstrate that our IDS outperforms existing ones in accurately detecting network intrusions with effective handling of class imbalance problem. |
| format | Article |
| id | doaj-art-9ecfbbd573fc4fe6985a6455b44ef04a |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-9ecfbbd573fc4fe6985a6455b44ef04a2025-08-20T03:41:46ZengPeerJ Inc.PeerJ Computer Science2376-59922025-03-0111e274510.7717/peerj-cs.2745Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithmHussein Ridha Sayegh0Wang Dong1Bahaa Hussein Taher2Muhanad Mohammed Kadum3Ali Mansour Al-madani4College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, Hunan, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, ChinaAs the number of connected devices and Internet of Things (IoT) devices grows, it is becoming more and more important to develop efficient security mechanisms to manage risks and vulnerabilities in IoT networks. Intrusion detection systems (IDSs) have been developed and implemented in IoT networks to discern between regular network traffic and potential malicious attacks. This article proposes a new IDS based on a hybrid method of metaheuristic and deep learning techniques, namely, the flower pollination algorithm (FPA) and deep neural network (DNN), with an ensemble learning paradigm. To handle the problem of imbalance class distribution in intrusion datasets, a roughly-balanced (RB) Bagging strategy is utilized, where DNN models trained by FPA on a cost-sensitive fitness function are used as base learners. The RB Bagging strategy derives multiple RB training subsets from the original dataset and proper class weights are incorporated into the fitness function to attain unbiased DNN models. The performance of our IDS is evaluated using four commonly utilized public datasets, NSL-KDD, UNSW NB-15, CIC-IDS-2017, and BoT-IoT, in terms of different metrics, i.e., accuracy, precision, recall, and F1-score. The results demonstrate that our IDS outperforms existing ones in accurately detecting network intrusions with effective handling of class imbalance problem.https://peerj.com/articles/cs-2745.pdfInternet of Things (IoT)Intrusion detection system (IDS)Flower pollination algorithm (FPA)Deep neural network (DNN)Imbalance class distributionBagging classifier |
| spellingShingle | Hussein Ridha Sayegh Wang Dong Bahaa Hussein Taher Muhanad Mohammed Kadum Ali Mansour Al-madani Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm PeerJ Computer Science Internet of Things (IoT) Intrusion detection system (IDS) Flower pollination algorithm (FPA) Deep neural network (DNN) Imbalance class distribution Bagging classifier |
| title | Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm |
| title_full | Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm |
| title_fullStr | Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm |
| title_full_unstemmed | Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm |
| title_short | Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm |
| title_sort | optimal intrusion detection for imbalanced data using bagging method with deep neural network optimized by flower pollination algorithm |
| topic | Internet of Things (IoT) Intrusion detection system (IDS) Flower pollination algorithm (FPA) Deep neural network (DNN) Imbalance class distribution Bagging classifier |
| url | https://peerj.com/articles/cs-2745.pdf |
| work_keys_str_mv | AT husseinridhasayegh optimalintrusiondetectionforimbalanceddatausingbaggingmethodwithdeepneuralnetworkoptimizedbyflowerpollinationalgorithm AT wangdong optimalintrusiondetectionforimbalanceddatausingbaggingmethodwithdeepneuralnetworkoptimizedbyflowerpollinationalgorithm AT bahaahusseintaher optimalintrusiondetectionforimbalanceddatausingbaggingmethodwithdeepneuralnetworkoptimizedbyflowerpollinationalgorithm AT muhanadmohammedkadum optimalintrusiondetectionforimbalanceddatausingbaggingmethodwithdeepneuralnetworkoptimizedbyflowerpollinationalgorithm AT alimansouralmadani optimalintrusiondetectionforimbalanceddatausingbaggingmethodwithdeepneuralnetworkoptimizedbyflowerpollinationalgorithm |