An Enhanced Intrusion Detection System for IoT Networks Based on Deep Learning and Knowledge Graph

Yang, Xiuzhang; Peng, Guojun; Zhang, Dongni; Lv, Yangqi

doi:10.1155/2022/4748528

Cited by 18 publications

(12 citation statements)

References 37 publications

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“…Additionally, redundant IoT security data could lead to the gathering of irrelevant data and inaccurate conclusions. Machine learning or deep learning security models may not perform as well, be less accurate, or even be completely ineffective if the IoT data are incomplete in some way, such as by not being representative, being of poor quality, having irrelevant features, or being too small for training [ 134 ].…”

Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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“…For example, in [30], the authors propose a novel Graph Neural Netwok (GNN) model that learns from graph-structured information, including flow records and their relationships, instead of relying solely on flow records. Similarly, other studies, such as [39], [7], [2], [36], and [40], employ DNN for IoT tasks. In [41], the authors investigated the use of DL for intrusion detection and proposed the Recurrent Neural Network (RNN) intrusion detection in binary and multiclass classification tasks.…”

Section: Related Workmentioning

confidence: 99%

Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning

Hamidouche,

Popko,

Ouni

2023

Proceedings of the International Conference on the Internet of Things

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This work provides a comparative analysis illustrating how Deep Learning (DL) surpasses Machine Learning (ML) in addressing tasks within Internet of Things (IoT), such as attack classification and device-type identification. Our approach involves training and evaluating a DL model using a range of diverse IoT-related datasets, allowing us to gain valuable insights into how adaptable and practical these models can be when confronted with various IoT configurations. We initially convert the unstructured network traffic data from IoT networks, stored in PCAP files, into images by processing the packet data. This conversion process adapts the data to meet the criteria of DL classification methods. The experiments showcase the ability of DL to surpass the constraints tied to manually engineered features, achieving superior results in attack detection and maintaining comparable outcomes in device-type identification. Additionally, a notable feature extraction time difference becomes evident in the experiments: traditional methods require around 29 milliseconds per data packet, while DL accomplishes the same task in just 2.9 milliseconds. The significant time gap, DL's superior performance, and the recognized limitations of manually engineered features, presents a compelling call to action within the IoT community. This encourages us to shift from exploring new IoT features for each dataset to addressing the challenges of integrating DL into IoT, making it a more efficient solution for real-world IoT scenarios.

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“…Their proposed algorithm attains a maximum performance and classifies the attacks respectively. Lian et al [31] developed a Decision tree with a Recursive feature elimination-based to choose the features. They used a stacking fusion model to fuse various ML algorithms for the detection of attacks using the NSL-KDD dataset and secured more than 98% of accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of the proposed model DTP-CRFE-ODNN is compared with the various feature [31], Improved Principal Component Analysis [27] and Modified kNN [21]. The analyzed results are shown in Table 7.…”

Section: Comparative Analysis Of Proposed Attack Detection Model With...mentioning

confidence: 99%

“…Likewise, the proposed feature selection with classification model is compared with other classifiers such as CNN-BiLSTM [31], CNN-DBN [26] and Auto Encoder with DNN [8] in order to prove the efficiency of the complete system. The results are compared in terms of detection rate and AUC and results are shown in Figures 5-6.…”

Section: Comparative Analysis Of Proposed Attack Detection Model With...mentioning

confidence: 99%

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Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree¹,

Krishnamoorthi²

2022

ITC

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The industrial revolution in recent years made massive uses of Internet of Things (IoT) applications like smart cities’ growth. This leads to automation in real-time applications to make human life easier. These IoT-enabled applications, technologies, and communications enhance the quality of life, quality of service, people’s well-being, and operational efficiency. The efficiency of these smart devices may harm the end-users, misuse their sensitive information increase cyber-attacks and threats. This smart city expansion is difficult due to cyber attacks. Consequently, it is needed to develop an efficient system model that can protect IoT devices from attacks and threats. To enhance product safety and security, the IoT-enabled applications should be monitored in real-time. This paper proposed an efficient feature selection with a feature fusion technique for the detection of intruders in IoT. The input IoT data is subjected to preprocessing to enhance the data. From the preprocessed data, the higher-order statistical features are selected using the proposed Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) model. This method efficiently eliminates the redundant and uncorrelated features which will increase resource utilization and reduces the time complexity of the system. Then, the request from IoT devices is converted into word embedding using the feature fusion model to enhance the system robustness. Finally, a Deep Neural network (DNN) has been used to detect malicious attacks with the selected features. This proposed model experiments with the BoT-IoT dataset and the result shows the proposed model efficiency which outperforms other existing models with the accuracy of 99.2%.

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An Enhanced Intrusion Detection System for IoT Networks Based on Deep Learning and Knowledge Graph

Cited by 18 publications

References 37 publications

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

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