Deep learning approach for intrusion detection in IoT-multi cloud environment

Selvapandian, D.; Santhosh, R.

doi:10.1007/s10515-021-00298-7

Cited by 30 publications

(20 citation statements)

References 22 publications

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“…In order to test the overall detection performance of intrusion data, this paper takes the KDD99 data set as the benchmark method, uses reference [22,25] as the comparison method, and runs in the same experimental environment as the M-CNN detection method to verify the optimal performance of the proposed method.…”

Section: Analysis Of Detection Resultsmentioning

confidence: 99%

“…e main idea of deep learning is to recognize and classify new network traffic data in real-time by using a trained classification model. Reference [22] proposed an intrusion detection system based on deep learning for the multi-cloud Internet of things environment, and the feasibility of the method was verified based on the NSL-KDD data set; Reference [23] determined the network security level, constructed a security intrusion detection system based on real-time sequence and extreme learning machine model, and analyzed the state of the Internet of things network. Based on the security interoperability requirements of the Internet of things, reference [24] built a distributed intrusion detection model based on the bidirectional long and short-term memory model to realize the network protection of the Internet of things for smart contracts; Reference [25] adopted the stacking depth learning method to analyze and determine the network state of the SCADA system in the power network.…”

Section: Related Workmentioning

confidence: 99%

“…e data used to support the findings of this study are included in this article. Method Accuracy (%) e proposed method 93.87 Reference [22] 92.18 Reference [25] 91.09…”

Section: Data Availabilitymentioning

confidence: 99%

See 2 more Smart Citations

Network Intrusion Detection Method Based on Multi-Scale CNN in Internet of Things

Yin

Chen

2022

Mobile Information Systems

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Network intrusion detection is a powerful means to identify and analyze the state of the Internet of things. For the reliability requirements of the Internet of things, an intrusion detection analysis method of the Internet of things based on a deep network model is proposed. First, based on the Inception network architecture as the backbone network, this method constructs a multi-scale convolutional neural network (M-CNN) intrusion detection analysis network model. The long-term and short-term memory network models are introduced into M-CNN to enhance the local feature extraction ability of the model. At the same time, batch normalization and global average pooling layers are introduced to make the data distribution of each layer uniform, reduce the model training time, reduce the model calculation gradient, and further improve the efficiency of the network. The simulation experiment takes the KDDcup99 data set as an example, and the results show that the M-CNN intrusion detection model can achieve better results. The precision P r e and recall R e of the detection model are 93.90% and 93.59%, respectively.

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Section: Analysis Of Detection Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Network Intrusion Detection Method Based on Multi-Scale CNN in Internet of Things

Yin

Chen

2022

Mobile Information Systems

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“…Selvapandian et al have introduced a deep learning‐based LeNet model that considered surveillance feed as input and identifies anomaly characteristics 20 . In the present technology, there are several models that use data analytics for image processing.…”

Section: Literature Reviewmentioning

confidence: 99%

“…14 ∶ Random partition of feature vector ∶ F V = F V1 + F V2 (20) 15 ∶ Instantaneous anomaly detection To confirm the presence of an anomaly in the input frames, the process leads to the employment of proposed AI which accepts training data and test data (frame) as represented in Equations ( 17)- (19). The obtained features are partitioned via Equation ( 20) and the anomaly is detected using the process defined in Equation (21).…”

Section: Algorithm 1 Anomaly Detection Using Proposed Ai Modelmentioning

confidence: 99%

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

Govardhan¹,

Surendranath

Sundararajan³

2022

Concurrency and Computation

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In recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)-based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant-sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5-fold cross-validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

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Comparative Analysis to Develop a Dimensionality Reduction Model for Classifying Intrusion Detection Systems

Olufemi,

Achimugu,

Usman

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Deep learning approach for intrusion detection in IoT-multi cloud environment

Cited by 30 publications

References 22 publications

Network Intrusion Detection Method Based on Multi-Scale CNN in Internet of Things

Network Intrusion Detection Method Based on Multi-Scale CNN in Internet of Things

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

Comparative Analysis to Develop a Dimensionality Reduction Model for Classifying Intrusion Detection Systems

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