Enhanced Elman spike neural network based intrusion attack detection in software defined <scp>Internet of Things</scp> network

Varma, P. Ravi Kiran; Sathiya, R. R.; Vanitha, M.

doi:10.1002/cpe.7503

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…Sathya et al [28] introduced a classification method, which utilizes a dual weight update mechanism to differentiate between attack and non-attack data in IoT devices. Ravi Kiran Varma et al [29] proposed a software-defined IoT intrusion attack detection method based on enhanced Elman spiking neural network.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Su,

Wang,

et al. 2024

CMC

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Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more difficult. To address these issues, this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection. To fully utilize the multi-scale information in network traffic, a Multi-scale Dilated feature extraction (MD) block is introduced. This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model's receptive field. The proposed Max-feature-map Residual with Dual-channel pooling (MRD) block integrates the maximum feature map with the residual block. This module ensures the model focuses on key information, thereby optimizing computational efficiency and reducing unnecessary information redundancy. Experimental results show that compared to the latest methods, the proposed abnormal traffic detection model improves accuracy by about 2%.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Su,

Wang,

et al. 2024

CMC

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“…In this manuscript, Web Service Reliability and Scalability Determination Using DWSCNN optimized with Adolescent Identity Search Algorithm (WSRS‐DWSCNN) is proposed 40 . Figure 1 shows the Block Diagram of proposed Methodology.…”

Section: Proposed Methodologymentioning

confidence: 99%

Web service reliability and scalability determination using optimized depth wise separable convolutional neural network

Dhakshnamoorthy,

Sridhar,

Ramanathan

et al. 2024

Quality & Reliability Eng

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Web service composition (WSC), a distributed architecture, creates new services atop existing ones. Ensuring trust and assessing performance and dependability in online services coordination is essential. In this paper, “Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network” (WSRS‐DWSCNN) is proposed to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services. The proposed approach transforms the BPEL specification into a Depth Wise Separable Convolutional Neural Network (DWSCNN) and annotates it with probabilistic properties for prediction. The DWSCNN model classifies the outcomes as correct or incorrect, and to enhances the prediction of web service composition scalability and reliability, we optimize the DWSCNN's weight parameters using the Adolescent Identity Search Algorithm (AISA). The proposed technique is activated in Python and its efficacy is analyzed under some metrics, such as reliability, scalability, accuracy, sensitivity, specificity, precision, F‐measure. The proposed method provides 12.36%, 45.39%, and 25.97% better reliability, 41.39%, 11.39%, 34.16% better accuracy compared with existing methods like, Web service reliability prediction depending on machine learning (WSRS‐K‐means), reliability prediction method for multiple state cloud/edge‐basis network utilizing deep neural network (WSRS‐DNN‐BO), and improving reliability of mobile social cloud computing utilizing machine learning in content addressable network (WSRS‐CAN), respectively.

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Deep Neural Network-Based Intrusion Detection in Internet of Things: A State-of-the-Art Review

Li,

Fang,

Zhu

et al. 2024

Lecture Notes in Computer Science

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Enhanced Elman spike neural network based intrusion attack detection in software defined Internet of Things network

Cited by 10 publications

References 25 publications

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Web service reliability and scalability determination using optimized depth wise separable convolutional neural network

Deep Neural Network-Based Intrusion Detection in Internet of Things: A State-of-the-Art Review

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