Intrusion Detection in Green Internet of Things: A Deep Deterministic Policy Gradient-Based Algorithm

Nie, Laisen; Sun, Wentao; Wang, Shupeng; Ning, Zhaolong; Rodrigues, Joel J. P. C.; Wu, Yixuan; Li, Shengtao

doi:10.1109/tgcn.2021.3073714

Cited by 37 publications

(9 citation statements)

References 27 publications

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“…Further, Q-learning [15], TCNN [22] and MCRNN [23] are used as comparative methods for experimental verification of algorithm optimization. All NIDS methods run separately in the same environment.…”

Section: Resultsmentioning

confidence: 99%

“…Statistical learning-based methods use the statistical features of normal activity to capture network traffic activity and create profiles that represent its random behavior [12]. Siddiqi et al [13] combines normalization methods with statistical learning methods to enhance the performance of supervised classifiers and achieve network traffic intrusion monitoring; Alzubi [14] constructs a network monitoring model based on the statistical Dirichlet model to ensure the stable operation of industrial wireless sensor networks; Nie et al [15] statistically analyzes the features of network traffic, and uses deep reinforcement learning (Q-learning) as the backbone network to build an NIDS model to provide various services for users. However, these models are difficult to break through the limitations of high-dimensional curse.…”

Section: A R T I C L Ementioning

confidence: 99%

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RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Xiang

2023

J Wireless Com Network

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Network intrusion detection system (NIDS) can effectively sense network attacks, which is of great significance for maintaining the security of cyberspace. To meet the requirements of efficient and accurate network status monitoring, a NIDS model using Transformer-based fusion deep learning architecture is proposed. Firstly, GAN-Cross is used to expand minority class sample data, thereby alleviating the issues of imbalanced minority class about the original dataset. Then, the Transformer module is used to adjust the ML-CNN-BiLSTM model to enhance the feature encoding ability of the intrusion model. Finally, the data enhancement model and feature enhancement model are integrated into the NIDS model, the detection model is optimized, the features of network state data are extracted at a deeper level, and the generalization ability of the detection model is enhanced. Some simulation experiments using UNSW-NB15 datasets show that the proposed fusion architecture can achieve efficient analysis of complex network traffic datasets, with an accuracy of 0.903, effectively improving the detection accuracy of NIDS and its ability to detect unknown attacks. The proposed model has good application value in ensuring the stable operation of network systems.

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Section: Resultsmentioning

confidence: 99%

Section: A R T I C L Ementioning

confidence: 99%

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Xiang

2023

J Wireless Com Network

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“…Park et al (2) not only adopted an autoencoder for intrusion detection but also utilized a generative adversarial network (GAN) to produce synthetic data to address data imbalance issues found in common AI-based network IDS system design. Nie et al (3) proposed an intrusion detection algorithm to tackle specifically distributed denial of service (DDoS) with deep reinforcement learning to predict past network statistical features. Wu et al (4) introduced big data mining into an intelligent intrusion detection algorithm, which was implemented first by feature selection using a fuzzy rough set, feature extraction using a deep convolutional neural network (DCNN), and also GAN.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in IoT Network Traffic Using Markov Model

Liu,

Huang,

Lee

et al. 2024

Sensors and Materials

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The rapid development of IoT-related technology accelerates the increase in network traffic volume. Hence, network traffic monitoring and analysis are more challenging than before in terms of possible malicious acts due to the immense traffic volume. Being a crucial measure to identify malicious network traffic that might enter a private network, an intrusion detection algorithm has always been an ongoing research topic, owing to its importance in cybersecurity. In this work, we aim to enhance cybersecurity in industrial IoT by performing intrusion detection on the generated network traffic. Therefore, we present a lightweight intrusion detection algorithm based on the Markov model, taking advantage of the source and destination payload lengths, and connection states defined in Zeek logs. We are able to detect intrusive network traffic with high accuracy, using the empirical probability law and Hellinger distance. The pattern similarities between the normal traffic and the cyberattack traffic are the key to our detection method. Lastly, the algorithm is evaluated with ToN_IoT public datasets, followed by an analysis of the experimental results.

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“…The conventional DL methods deep belief network (DBN) stacked noise autoencoder (SNAE), stacked sparse autoencoder (SSAE), stacked contractive autoencoder (SCAE), stacked autoencoder (SAE)], were presented for executing the comparative simulation with the method in this study. Nie et al [16] formulated an identifier (ID) method that depends on deep reinforcement learning (DRL) that follows the trend of traffic flow through the extraction of statistical features of previous network traffic for traffic prediction. Afterwards, uses traffic predictors for employing ID.…”

Section: Introductionmentioning

confidence: 99%

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

Sheeba¹,

Sharmila²,

Alkhayyat³

et al. 2023

Computer Systems Science and Engineering

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Lately, the Internet of Things (IoT) application requires millions of structured and unstructured data since it has numerous problems, such as data organization, production, and capturing. To address these shortcomings, big data analytics is the most superior technology that has to be adapted. Even though big data and IoT could make human life more convenient, those benefits come at the expense of security. To manage these kinds of threats, the intrusion detection system has been extensively applied to identify malicious network traffic, particularly once the preventive technique fails at the level of endpoint IoT devices. As cyberattacks targeting IoT have gradually become stealthy and more sophisticated, intrusion detection systems (IDS) must continually emerge to manage evolving security threats. This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning (IDMBOA-DL) algorithm. In the presented IDMBOA-DL model, the Hadoop MapReduce tool is exploited for managing big data. The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets. Finally, the sine cosine algorithm (SCA) with convolutional autoencoder (CAE) mechanism is utilized to recognize and classify the intrusions in the IoT network. A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm. The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

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Intrusion Detection in Green Internet of Things: A Deep Deterministic Policy Gradient-Based Algorithm

Cited by 37 publications

References 27 publications

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Intrusion Detection in IoT Network Traffic Using Markov Model

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

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