.png){kind=icon}
返回
Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network
.png){kind=icon}
OA
AI
DOI：10.7717/peerj-cs.2333.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back- propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSWNB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.
Keyword:
Artificial neural network
Artificial bee colony
Metaheuristics
Deep Autoencoder
Network intrusion detection systems (NIDS)
Anomaly detection
UNSW-NB15
Swarm intelligence
NF-UNSW-NB15-v2
期刊
IF:
2.5
论文数:
3.4K
被引数:
6.9K
机构
引用论文
A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration混合人工蜂群算法-人工神经网络预测爆炸产生的地面振动
PREAI
分享收藏Bifurcated Triel Bonds—Hydrides and Halides of 1,2-Bis(Dichloroboryl)Benzene and 1,8-Bis(Dichloroboryl)Naphthalene.png)
Crystals
OAAI
CrystalsIF0
OAAI分享收藏Machine learning for email spam filtering: review, approaches and open research problemsHELIYONOAAI
HELIYONIF3.6
OAAI分享收藏
查看更多内容