Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

Liu, Jingyu; Yang, Dongsheng; Lian, Mengjia; Li, Mingshi

doi:10.1109/access.2021.3063671

Cited by 71 publications

(33 citation statements)

References 45 publications

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“…In [18], by the use of Naive Bayesian network explored the Bayesian networks for ID, in which leaf node = features and root node = class connection. Later, [20] to ID, the application of the Naive Bayes network is identi ed and by means of detailed experimental analysis, challenge of KDDCup 99 the winning entries with which compared, in 'Probe' and 'U2R' categories better performance is given by a Bayesian network. In [21], on parison-window estimators based method of nonparametric density estimation was studied by the use of Normal distribution and Gaussian kernels.…”

Section: Related Workmentioning

confidence: 99%

Optimization of BPN Parameters Using PSO for Intrusion Detection in Cloud Environment

Sajith

Nagarajan

2022

Preprint

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The usage of internet is getting increased in all aspect, like from building various models that are fully connect with internet to the usage of digital media for 24/7. As this is rising in a way, on the other side the concern about “data security” is raising and everybody’s information needs to be protected from any other attacks or can say there shouldn’t be no data leakage. So, for detecting these attacks, intrusion detection system is placed. But placing this traditional Intrusion Detection System (IDS) will increase the concerns of security even more, so to amp the process integration of latest technology such deep learning comes in action. Thereby this paper proposes an IDS using Back-propagation Network (BPN) where intrusions are identified based on the system calls that we collected in the dataset KDD cup 99. Also, to increase the optimization of neural network, we used Particle Swarm Optimization (PSO) thereby increase the accurate detection of the system saying if that is normal or abnormal in behavior. We evaluate our proposed model with other methods like ANFIS, F-GNP, FCM in which the proposed model gives 96.5% accurate detection.

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Section: Related Workmentioning

confidence: 99%

Optimization of BPN Parameters Using PSO for Intrusion Detection in Cloud Environment

Sajith

Nagarajan

2022

Preprint

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“…This section provides an account of related research that was conducted in the domain of IDS using ML techniques. Moreover, this section serves as a survey of various IDS Liu et al [22] implemented an IDS system for IoT using a Particle Swarm Optimization (PSO)-based technique for feature selection and the Support Vector Machine(SVM) ML algorithm for classification. The PSO method used in this research is based on the Light Gradient Boosting Machine (LightGBM).…”

Section: Related Workmentioning

confidence: 99%

An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

Kasongo

2021

IEEE Access

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The evolution of the Internet and cloud-based technologies have empowered several organizations with the capacity to implement large-scale Internet of Things (IoT)-based ecosystems, such as Industrial IoT (IIoT). The IoT and, by virtue, the IIoT, are vulnerable to new types of threats and intrusions because of the nature of their networks. So it is crucial to develop Intrusion Detection Systems (IDSs) that can provide the security, privacy, and integrity of IIoT networks. In this research, we propose an IDS for IIoT that was implemented using the Genetic Algorithm (GA) for feature selection, and the Random Forest (RF) model was employed in the GA fitness function. The models used for the intrusion detection processes include classifiers such as the RF, Linear Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Extra-Trees (ET), and Extreme Gradient Boosting (XGB). The GA-RF generated 10 feature vectors for the binary classification scheme and seven feature vectors for the multiclass classification procedure. The UNSW-NB15 is used to assess the effectiveness and the robustness of our proposed approach. The experimental outcomes demonstrated that for the binary modeling process, the GA-RF achieved a test accuracy (TAC) of 87.61% and an Area Under the Curve (AUC) of 0.98, using a feature vector that contained 16 features. These results were superior to existing IDS frameworks.

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“…Indeed, PSO has been widely used in large areas of research such as in the application of face recognition systems [36], artificial neural network [37], Internet of Things [38], reliability engineering [39], power-system [40], indoor navigation [41], control-systems [42], EEG signals [43], deep-learning [44], wireless sensor networks [45], cloud computing [46], energy grid [47], Image segmentation [48], and electromagnetics [49,50].…”

Section: Hybridizationmentioning

confidence: 99%

A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

et al. 2021

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Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

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Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

Cited by 71 publications

References 45 publications

Optimization of BPN Parameters Using PSO for Intrusion Detection in Cloud Environment

Optimization of BPN Parameters Using PSO for Intrusion Detection in Cloud Environment

An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

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