Real-Time Network Intrusion Detection System Based on Deep Learning

Dong, Yafei; Wang, Rong; He, Juntao

doi:10.1109/icsess47205.2019.9040718

Cited by 41 publications

(21 citation statements)

References 11 publications

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“…And the network uses BP and Adam to optimize. 16: end if 17: end while 18: return discriminator D where x z = G (x G ). Thereby, the generator can be expressed as…”

Section: Intrusion Detection Aiming At Multiple Attacksmentioning

confidence: 99%

“…It uses an auxiliary classifier generative adversarial network (AC-GAN) to expand abnormal data. The work in [16] uses the auto-encoder (AE) to reduce the data dimensionality and then realizes intrusion detection through AE-AlexNet based on deep learning. Using deep learning algorithms to train huge data sets in the CEC environment This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

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Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Nie

Wang

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users' privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-inthe-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method. Index Terms-Collaborative edge computing (CEC), generative adversarial network (GAN), intrusion detection, social internet of things (SIoT).

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“…And the network uses BP and Adam to optimize. 16: end if 17: end while 18: return discriminator D where x z = G (x G ). Thereby, the generator can be expressed as…”

Section: Intrusion Detection Aiming At Multiple Attacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Nie

Wang

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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“…Moreover, the rapid development of information and communications technology application has created a new challenge [8,9]. With the advent of the new technology era, passing the massive amount of data from different sources on the network generated in a short time is another problem because it is not easy to detect intrusive behaviors in these large quantities of data and fast network speed [10,11].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for Intrusion Detection

Salih

Ameen

Zeebaree

et al. 2021

AJRCoS

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Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

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“…Currently, researchers have proposed a variety of intrusion detection schemes based on these two models. Dong et al [12] proposed an intrusion detection model named AE-AlexNet based on deep learning. ey use AE to realize dimensionality reduction of highdimensional traffic.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Tian

2021

Security and Communication Networks

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Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.

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Real-Time Network Intrusion Detection System Based on Deep Learning

Cited by 41 publications

References 11 publications

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Deep Learning Approaches for Intrusion Detection

An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

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