Intrusion detection of wireless sensor networks based on IPSO algorithm and BP neural network

Lu, Xue; Han, Dong‐Guk; Duan, Letian; Tian, Qiuting

doi:10.1504/ijcse.2020.107344

Cited by 11 publications

(13 citation statements)

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“…All possible solutions are evaluated according to the fitness selection procedure along with classifier algorithms, namely KNN [ 63 ], DT [ 64 ], SVM [ 65 ], DL [ 21 ], and NB [ 59 ], to obtain the highest performance accuracy among the classification algorithms and features selected for each solution. For the purpose of maintaining an adequate balance between all the selected features in each of the minimum solutions and providing maximum accuracy for feature selection, the fitness function, i.e., objective function in (11) is used in the WC technique to evaluate solutions in M pop :

where γ R (D) is the rating of classification error for a given classifier; |R| is the total items in the selected subset, |M| is the total number of features in the dataset, ∂ and Φ are two parameters that represent the importance of the classification quality and subset length, ∂ ∈ [0, 1] and Φ = ( 1 − α ) [ 50 , 66 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In order to illustrate the effect of feature selection techniques on the accuracy of detection using the WSN-DS dataset, we integrated machine learning classifier algorithms along with feature selection techniques as mentioned previously to detect the most important features of the WSN-DS dataset. The WC technique was benchmarked with various feature selection techniques such as PSO [ 21 ], SA [ 22 ], HS [ 19 ], and GA [ 23 ] using the same classifier algorithms and dataset. The output of these operations is shown in Table 7 .…”

Section: Implementation and Evaluationmentioning

confidence: 99%

“…This is used to determine which data features are most important in the IDS process [ 19 ], and helps in understanding data, minimizing computation requirements, and improving prediction performance. Examples of feature selection technologies are Water Cycle (WC) [ 20 ], Particle Swarm Optimization (PSO) [ 21 ], Simulated Annealing (SA) [ 22 ], Harmony Search (HS) [ 19 ], and Genetic Algorithm (GA) [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Ahmad

Wazirali

Bsoul

et al. 2021

Sensors

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Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Implementation and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Ahmad

Wazirali

Bsoul

et al. 2021

Sensors

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“…2 depicts various categories of detection approaches and machine learning techniques that are used in detection attacks in WSN networks. [23,44] In a simple review of the functioning of supervised algorithms, which we will deal with in this study, the KNN works based on the manifold hypothesis. If the majority of the neighbors of the sample are from the same class, then the sample is likely to be from that class as well.…”

Section: Machine Learning In Intrusion Detection Approachmentioning

confidence: 99%

“…The error is propagated back from the output layer to the input layer in the back-propagation step, and weights between output neurons and hidden neurons are changed. Weights are updated using the gradient descent approach [44]. Moreover, another different type of deep learning that also uses backpropagation is LSTM.…”

Section: Machine Learning In Intrusion Detection Approachmentioning

confidence: 99%

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Wazirali¹,

Ahmad²

2022

Computers, Materials &Amp; Continua

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Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time. We examined the performance metrics of different machine learning classification categories such as K-Nearest Neighbour (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gboost, Decision Tree (DT), Naïve Bayes, Long Short Term Memory (LSTM), and Multi-Layer Perceptron (MLP) on a WSN-dataset in different sizes. The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets, and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics. The performance metrics used in these validations were accuracy, F1-score, False Positive Ratio (FPR), False Negative Ratio (FNR), and the training execution time. Moreover, the test results showed the Gboost algorithm got 99.6%, 98.8%, 0.4% 0.13% in accuracy, F1-score, FPR, and FNR, respectively. At training execution time, it obtained 1.41 s for the average of all training time execution datasets. In addition, this paper demonstrated that for the numeric statistical data type, the best results are in the size of the dataset ranging from 3000 to 6000 records and the percentage between categories is not less than 50% for each category with the other categories. Furthermore, this paper investigated the effect of Gboost on the WSN lifetime, which resulted in a 32% reduction compared to other Gboost-free scenarios.

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Intrusion Detection System Based on Feature Reduced Back Propagation Neural Network Using Ant Colony Optimization

Dubey¹,

Bhujade²

2022

Cyber Technologies and Emerging Sciences

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Intrusion detection of wireless sensor networks based on IPSO algorithm and BP neural network

Cited by 11 publications

References 0 publications

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Intrusion Detection System Based on Feature Reduced Back Propagation Neural Network Using Ant Colony Optimization

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