Optimization of key predistribution protocol based on supernetworks theory in heterogeneous WSN

Qi, Yuan; Ma, Chunguang; Zhong, Xiao-Rui; Du, Gang; Yao, Jiayi

doi:10.1109/tst.2016.7488744

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…This process is iterated recursively till it satisfies termination criteria. Hence, various efforts are used to develop unsupervised learning [15][16][17]. But it suffers from certain disadvantages where confident instances are self-labeled from decision boundaries.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

Sherubha¹,

Sasirekha²,

Anguraj³

et al. 2023

Computer Systems Science and Engineering

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The Cloud system shows its growing functionalities in various industrial applications. The safety towards data transfer seems to be a threat where Network Intrusion Detection System (NIDS) is measured as an essential element to fulfill security. Recently, Machine Learning (ML) approaches have been used for the construction of intellectual IDS. Most IDS are based on ML techniques either as unsupervised or supervised. In supervised learning, NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns. Similarly, the unsupervised model fails to provide a satisfactory outcome. Hence, to boost the functionality of unsupervised learning, an effectual auto-encoder is applied for feature selection to select good features. Finally, the Naïve Bayes classifier is used for classification purposes. This approach exposes the finest generalization ability to train the data. The unlabelled data is also used for adoption towards data analysis. Here, redundant and noisy samples over the dataset are eliminated. To validate the robustness and efficiency of NIDS, the anticipated model is tested over the NSL-KDD dataset. The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%, which is higher compared to J48, AB tree, Random Forest (RF), Regression Tree (RT), Multi-Layer Perceptrons (MLP), Support Vector Machine (SVM), and Fuzzy. Similarly, False Alarm Rate (FAR) and True Positive Rate (TPR) of Naive Bayes (NB) is 0.3 and 0.99, respectively. When compared to prevailing techniques, the anticipated approach also delivers promising outcomes.

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Section: Related Workmentioning

confidence: 99%

“…E ðx ðiÞ À q ðx ðiÞ jx ðiÞ ÞLðx ðiÞ ; g h ðf h ðx ðIÞ ÞÞÞ (17) The noisy inputs are attained from the corruption process. Some approaches include Gaussian noise and masking noise for setting random input features to zero.…”

Section: Auto-encoder For Feature Selectionmentioning

confidence: 99%

An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

Sherubha¹,

Sasirekha²,

Anguraj³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

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“…Some keys are unique, and it is utilized for establishing secured communication among the base station. Yuan et al [17] discuss SHELL protocol to secure the member nodes and CH with the benefit of robustness towards the compromise nodes. The capturing nodes are accountable for network scalability and nodes replacement.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

Arivazhagi¹,

Kumar²

2022

Computer Systems Science and Engineering

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Intelligent Intrusion Detection System (IIDS) for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall. The efficiency of IIDS highly relies on the algorithm performance. The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms. Here, a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework (SILF), is proposed to learn the attack features and reduce the dimensionality. It also reduces the testing and training time effectively and enhances Linear Support Vector Machine (l-SVM). It constructs an auto-encoder method, an efficient learning approach for feature construction unsupervised manner. Here, the inclusive certified signature (ICS) is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers. By training the samples in the preliminary stage, the selected features are provided into the classifier (lSVM) to enhance the prediction ability for intrusion and classification accuracy. Thus, the model efficiency is learned linearly. The multi-classification is examined and compared with various classifier approaches like conventional SVM, Random Forest (RF), Recurrent Neural Network (RNN), STL-IDS and game theory. The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy, precision, recall, F-measure, pvalue, MCC and so on. The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection. Here, the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

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“…For the sensors with limited sensing range, dynamic deployment is used to improve sensing performance. Yuan et al [13] developed an equilibrium model in order to find an optimum distribution strategy to improve the performance of predistribution protocols in terms of various parameters like cost, resilience, connectivity and lifetime. Heterogeneity is considered as an essential attribute of WSNs.…”

Section: Related Workmentioning

confidence: 99%