Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize ***-rently,the Inte...
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Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize ***-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human ***,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN *** findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy *** investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)***,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application ***,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.
Purpose-In wireless sensor networks,improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data *** the traditional data aggregation techniques,cluster-base...
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Purpose-In wireless sensor networks,improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data *** the traditional data aggregation techniques,cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster ***,the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated ***,due to this reason,the energy of each communicating node is gradually decreased and the network lifetime is also *** increase the lifetime of the network,the proposed algorithm uses two sets:Dominating set and hit ***/methodology/approach-The proposed algorithm uses two sets:Dominating set and hit *** dominating set constructs an unequal clustering,and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base *** simulation results also infer that the proposed optimized unequal clustering algorithm(OUCA)is greater in improving the network lifetime to a maximum amount of 22%than the existing cluster head selection approach considered for ***-In this paper,lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit *** dominator set chooses the dominator based on the remaining energy and its node degree of each *** optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the *** proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit *** simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.
Remote correspondence is quickly taking the place of wired correspondence, posing new difficulties for Transmission Control Protocol (TCP). TCP is enhanced in a few ways to detect this issue. This paper addresses the ...
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Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medic...
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A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, ...
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Predicting brain age from MRI scans is crucial for understanding neurological development and aging-related disorders. Robust preprocessing methods and flexible model architectures are required because variations in i...
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Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated *** this paper, we emphasize another crucial quality that it should possess, i.e...
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Blockchain technology is critical in cyber *** most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic *** of the ethical and legal implications of a patient’s medical data...
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Blockchain technology is critical in cyber *** most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic *** of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in *** image secrecy is highly vulnerable to various types of *** a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data *** resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare *** blockchain algorithm preserves the security of the medical image by performing hash *** execution of this research is carried out by MATLAB *** suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound *** the simulation,94.9%of malicious communications were recognized and identified effectively,according to the total outcomes *** suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.
A key aspect of artificial intelligence is continual learning. The capacity of a model to incrementally learn from a new task and adapt to it without losing the previous knowledge. A fundamental challenge that comes t...
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Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial *** the first reported incident in 1995,its impact keeps on ***,during COVID-19,due to the increase in d...
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Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial *** the first reported incident in 1995,its impact keeps on ***,during COVID-19,due to the increase in digitization,there is an exponential increase in the number of victims of phishing *** deep learning and machine learning techniques are available to detect phishing ***,most of the techniques did not use efficient optimization *** this context,our proposed model used random forest-based techniques to select the best features,and then the Brown-Bear optimization algorithm(BBOA)was used to fine-tune the hyper-parameters of the convolutional neural network(CNN)*** test our model,we used a dataset from Kaggle comprising 11,000+*** addition to that,the dataset also consists of the 30 features that are extracted from the website uniform resource locator(URL).The target variable has two classes:“Safe”and“Phishing.”Due to the use of BBOA,our proposed model detects malicious URLs with an accuracy of 93%and a precision of 92%.In addition,comparing our model with standard techniques,such as GRU(Gated Recurrent Unit),LSTM(Long Short-Term Memory),RNN(Recurrent Neural Network),ANN(Artificial Neural Network),SVM(Support Vector Machine),and LR(Logistic Regression),presents the effectiveness of our proposed ***,the comparison with past literature showcases the contribution and novelty of our proposed model.
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