Network Intrusion Detection Based on an Improved Long‐Short‐Term Memory Model in Combination with Multiple Spatiotemporal Structures

Huang, Xiaolong

doi:10.1155/2021/6623554

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…e error backpropagation algorithm then uses the output sensitivity of the neural network to quickly calculate the hyperparameters of the layers of the neural network, which include thresholds and weights, in order to achieve the goal of ensuring that the output values of the neural network are as close to the true values as possible [18]. Denote the training data set as (x 1 , y 1 ), (x 2 , y 2 ), .…”

Section: Mlp Classificationmentioning

confidence: 99%

Research on DoS Traffic Detection Model Based on Random Forest and Multilayer Perceptron

Huang

Zhang

et al. 2022

Security and Communication Networks

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Denial of service (DoS) attack is a typical and extremely destructive attack, which poses a serious threat to the Internet security and is highly concealed, making it difficult to detect. In response to this problem, the paper proposes an efficient DoS attack traffic detection method, Random Forest and Multilayer Perceptron hybrid network attack detection algorithm (RF-MLP). At first, it is adopted that the random forest algorithm can be used for feature selection and the optimal threshold can be determined by drawing a learning curve; therefore the optimal feature subset is determined. Then the optimal feature subset is used as the input of the multilayer perceptron for training. We will analyze the experimental results obtained using different configurations by varying the number of training neurons and the number of hidden layers of the multilayer perceptron network in order to improve the accuracy and reduce the number of false results. Using the real network traffic CICIDS2017 dataset and UNSW-NB15 dataset to evaluate the method in this paper, the results show that the model can effectively detect and classify DoS attacks, the accuracy rate can reach 99.83% and 93.51%, and there is also a significant reduction in the false alarm rate, verifying the effectiveness of the method and its ease of use.

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Section: Mlp Classificationmentioning

confidence: 99%

Research on DoS Traffic Detection Model Based on Random Forest and Multilayer Perceptron

Huang

Zhang

et al. 2022

Security and Communication Networks

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“…In IDS, signature-based approaches use known patterns whereas anomaly detection works for unknown patterns [6]. The IDS proposed in [7] operates hierarchically by using control systems. The local information is obtained and then the results are transferred to the upper level after review [8].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection and Prevention System for Secure Multimedia sharing in Future Internet

ASHRAF,

ULLAH,

TAHIRA

et al. 2024

Preprint

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IP Multimedia Subsystem (IMS) supports high-speed transmission of multi-media like data, audio, and video between mobiles and other wireless devices. IMS is the most suitable for multimedia communication across multiple networks using the Internet of Things (IoT) and cloud computing. IoT-IMS can efficiently manage the huge amount of data generated by smart devices for audio-video during live streaming applications. During these services, the main problem is that an intruder can launch a Session Initialization Protocol (SIP) messages flooding attack on IMS that does not have the functionality to prevent itself from these types of attacks. In this paper, an intrusion detecting and preventing system (IDPS) is proposed that eﬀectively detects the register flooding and spoofing attacks using SIP messages. We proposed a watermark embedding algorithm on the user side and an extraction algorithm on receiving side. A test-bed is set up for IMS where we have considered four different scenarios to evaluate the IDPS approach as compared to counterparts. Results illustrate the dominance of our scheme in terms of response time, register flooding attack detection ratios, CPU load utilization, fault detection ratio, and memory utilization. The fault detection ratio averagely 98.18

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“…In the past few years, many IDS techniques have been presented based on various approaches, such as mathematical formulations and data mining techniques like machine learning. Poor performances are caused by the difficulty in managing the high-dimensional network traffic data by these statistical formulations and conventional machine learning models [16][17][18]. Furthermore, most existing techniques used only binary classification, such as whether it is an attack.…”

Section: Introductionmentioning

confidence: 99%

An Effective Deep Learning Based Multi-Class Classification of DoS and DDoS Attack Detection

Silivery

Kovvur

Kumar

2023

Int. j. electr. comput. eng. syst. (Online)

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In the past few years, cybersecurity is becoming very important due to the rise in internet users. The internet attacks such as Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks severely harm a website or server and make them unavailable to other users. Network Monitoring and control systems have found it challenging to identify the many classes of DoS and DDoS attacks since each operates uniquely. Hence a powerful technique is required for attack detection. Traditional machine learning techniques are inefficient in handling extensive network data and cannot extract high-level features for attack detection. Therefore, an effective deep learning-based intrusion detection system is developed in this paper for DoS and DDoS attack classification. This model includes various phases and starts with the Deep Convolutional Generative Adversarial Networks (DCGAN) based technique to address the class imbalance issue in the dataset. Then a deep learning algorithm based on ResNet-50 extracts the critical features for each class in the dataset. After that, an optimized AlexNet-based classifier is implemented for detecting the attacks separately, and the essential parameters of the classifier are optimized using the Atom search optimization algorithm. The proposed approach was evaluated on benchmark datasets, CCIDS2019 and UNSW-NB15, using key classification metrics and achieved 99.37% accuracy for the UNSW-NB15 dataset and 99.33% for the CICIDS2019 dataset. The investigational results demonstrate that the suggested approach performs superior to other competitive techniques in identifying DoS and DDoS attacks.

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Network Intrusion Detection Based on an Improved Long‐Short‐Term Memory Model in Combination with Multiple Spatiotemporal Structures

Cited by 13 publications

References 36 publications

Research on DoS Traffic Detection Model Based on Random Forest and Multilayer Perceptron

Research on DoS Traffic Detection Model Based on Random Forest and Multilayer Perceptron

Intrusion Detection and Prevention System for Secure Multimedia sharing in Future Internet

An Effective Deep Learning Based Multi-Class Classification of DoS and DDoS Attack Detection

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