Network intrusion detection algorithm based on deep neural network

Jia, Yang; Wang, Meng; Wang, Yagang

doi:10.1049/iet-ifs.2018.5258

Cited by 86 publications

(41 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al [11] provided an aggregation approach using the deep belief network (DBN) and modified density peak clustering algorithm (MDPCA). Jia et al [12] designed an IDS based on a deep neural network (DNN).…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Long Short-Term Memory Network for Cyberattack Detection

Hou

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

With the continuous development of network technology, cyberattack detection mechanisms play a vital role in ensuring the security of computers and network systems. However, with the rapid growth of network traffic, traditional intrusion detection systems (IDSs) are far from being able to quickly and accurately identify complex and diverse network attacks, especially those related to low-frequency attacks. To enhance the overall security of the Internet, an IDS based on hierarchical long short-term memory (HLSTM) networks is proposed. With the introduction of HLSTM, the network can learn across multiple levels of temporal hierarchy over complex network traffic sequences. The system is evaluated on the well-known benchmark data set NSL-KDD for comparison with other existing methods. The experimental results demonstrate that compared with existing start-of-the-art methods, our system has better detection performance for different types of cyberattacks. In addition, the low-frequency network attack types have higher classification accuracy and a lower false detection rate.

show abstract

Section: Related Workmentioning

confidence: 99%

Hierarchical Long Short-Term Memory Network for Cyberattack Detection

Hou

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…They found that simple LSTM performed better than all other model in terms of accuracy where as BLSTM trained in just 190 second with accuray less than 90%. In [510], the authors have proposed a DNN based IDS. They have shown that the model achieves an accuracy of 99.9% which is better when compared to other traditional ML models.…”

Section: A Deep Learning In Intrusion Detectionmentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Kamarudin et al2017 [17]. NB, SVM, MLP, DT, NSL-KDD Jia et al2019 [18] NDNN KDD99 and NSL-KDD Gogoi et al 2013 [19] Multi-level hybrid intrusion detection method that KDD99, NSL-KDD and TUIDS Gao et al 2018 [20] J48, NB, NBT, RF, RT, MLP, SVM etc.…”

Section: Nsl-kddmentioning

confidence: 99%

Implementation of Machine Learning Algorithms on CICIDS-2017 Dataset for Intrusion Detection using WEKA

Panwar¹,

Negi²,

Raiwani³

2019

IJRTE

View full text Add to dashboard Cite

For protecting and securing the network, with Intrusion Detection Systems through hidden intrusion has become a popular and important issue in the network security domain. Detection of attacks is the first step to secure any system. In this paper, the main focus is on seven different attacks, including Brute Force attack, Heartbleed/Denial-of-service (DoS), Web Attack, Infiltration, Botnet, Port Scan and Distributed Denial of Service (DDoS). We rely on features derived from CICIDS-2017 Dataset for these attacks. By using various subset based feature selection techniques performance of attack has been identified for many features. Using these techniques, it has been determined the appropriate group of attributes for finding every attack with related classification algorithms. Simulations of these techniques present that unwanted feature can be removed from attack detection techniques and find the most valuable set of attributes for a definite classification algorithm with discretization and without discretization, which improve the performance of IDS.

show abstract

Network intrusion detection algorithm based on deep neural network

Cited by 86 publications

References 25 publications

Hierarchical Long Short-Term Memory Network for Cyberattack Detection

Hierarchical Long Short-Term Memory Network for Cyberattack Detection

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Implementation of Machine Learning Algorithms on CICIDS-2017 Dataset for Intrusion Detection using WEKA

Contact Info

Product

Resources

About