An Effective Deep Learning Based Scheme for Network Intrusion Detection

Zhang, Hongpo; Wu, Chase Q.; Gao, Shan; Wang, Zongmin; Xu, Yizhen; Liu, Yongpeng

doi:10.1109/icpr.2018.8546162

Cited by 55 publications

(41 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model not only excelled in detection rate, precision and F1 score, but also outperformed the current existing IDS model in terms of training and detection time. However, when the researchers made use of the method in [44] to perform feature reduction on the UNSW-NB15 dataset after one-hot encoding, the number of features has changed from 208 to 202, which was only 6 fewer. If a more effective special diagnosis extraction method is used, the NIDS will be more perfect.…”

Section: B Intrusion Detection Technologymentioning

confidence: 99%

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

et al. 2021

View full text Add to dashboard Cite

With the advent of the "Internet plus" era, the Internet of Things (IoT) is gradually penetrating into various fields, and the scale of its equipment is also showing an explosive growth trend. The age of the "Internet of Everything" is coming. The integration and diversification of IoT terminals and applications make IoT more vulnerable to various intrusion attacks. Therefore, it is particularly important to design an intrusion detection model that guarantees the security, integrity and reliability of the IoT. Traditional intrusion detection technology has the disadvantages of low detection rate and poor scalability, which cannot adapt to the complex and changeable IoT environment. In this paper, we propose a particle swarm optimization-based gradient descent (PSO-LightGBM) for the intrusion detection. In this method, PSO-LightGBM is used to extract the features of the data and inputs it into one-class SVM (OCSVM) to discover and identify malicious data. The UNSW-NB15 dataset is applied to verify the intrusion detection model. The experimental results show that the model we propose is very robust in detecting either normal or various malicious data, especially small sample data such as Backdoor, Shellcode and Worms. INDEX TERMS Intrusion detection, Internet of Things (IoT), particle swarm optimization (PSO), oneclass SVM(OCSVM)

show abstract

Section: B Intrusion Detection Technologymentioning

confidence: 99%

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This subsection investigates the recently proposed auto-encoder-based misuse detection schemes such as [45][46][47][48][49][50][51][52]. For instance, in [53], Al-Qatf et al presented STL-IDS, a deep learning approach based on selftaught learning (STL) framework for feature learning and dimension reduction.…”

Section: Auto-encoder Based Schemesmentioning

confidence: 99%

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems 

Lánský

Mohammadi

Mohammed

et al. 2021

Preprint

View full text Add to dashboard Cite

The ever-increasing complication and severity of the computer networks' security attacks have inspired security researchers to apply various machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques that recently have been widely used by intrusion detection systems (IDS) to secure computer networks and hosts' performance. This survey article focuses on the signature-based IDS using deep learning techniques and puts forward an in-depth survey and classification of these schemes. For this purpose, it first presents the essential background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods applied in each of them. It describes how deep learning networks are utilized in the misuse detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.

show abstract

“…In this way, the risk of learning the identity function instead of extracting features is eliminated. Regrettably, there are little literatures about traffic classification using DAE, while some work used DAE for IDS and Network Anormaly Detection [45], [46].…”

Section: ) Aementioning

confidence: 99%

A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning

et al. 2019

View full text Add to dashboard Cite

The rapid adoption of mobile devices has dramatically changed the access to various networking services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted features, and frequent features update. With the excellent ability of automatic feature learning, Deep Learning (DL) undoubtedly becomes a highly desirable approach for mobile services traffic classification, especially encrypted traffic. This survey paper looks at emerging research into the application of DL methods to encrypted traffic classification of mobile services and presents a general framework of DL-based mobile encrypted traffic classification. Moreover, we review most of the recent existing work according to dataset selection, model input design, and model architecture. Furthermore, we propose some noteworthy issues and challenges about DL-based mobile services traffic classification.

show abstract

An Effective Deep Learning Based Scheme for Network Intrusion Detection

Cited by 55 publications

References 20 publications

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems

A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning

Contact Info

Product

Resources

About

An Effective Deep Learning Based Scheme for Network Intrusion Detection

Cited by 55 publications

References 20 publications

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems&nbsp;

A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning

Contact Info

Product

Resources

About

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems