Wireless sensor networks (WSN) have seen immense use in everyday life, like health, battle-field administration, and disaster administration. Nodes inside WSN are more vulnerable to safety attacks like data replay and...
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Wireless sensor networks (WSN) have seen immense use in everyday life, like health, battle-field administration, and disaster administration. Nodes inside WSN are more vulnerable to safety attacks like data replay and eavesdropping attacks. Node capture attacks function as destructive attacks that let attackers physically seize sensor nodes, reconfigure the structures, and deploy new nodes. An efficient architecture consists of a number of protocols for safe key creation and node capture attack revocation. A pairwise key establishment addresses arbitrary inputs from the pair of nodes implicated for the secure key establishment. Thus, the detailed exploration of various attack models to enhance key management security is a critical research direction in WSN security. Our model approaches the node capture attack problem from an attacker's viewpoint. The proposed model discovers the optimal collection of nodes likely to be attacked for node capturing. Based on the optimization algorithm i.e., fruit fly, the proposed model identifies multiple objectives like the set of dominating nodes, the vulnerability in paths, traveling cost, node contribution, and dominant rank and computes the optimal set of nodes with higher destructiveness. This indicates that the suggested node capture model has significant performance in the aspect of the least cost and lower attacking rounds. In this proposed model, we present an improved fruit fly optimization based attacking model consisting of several objectives as node strength, node and key participation rank, dominant rank and cost for capturing nodes in the system. Our approach outperforms existing attack models like RA, MLA, MTA, MKA, FGA, FFOA, and MA in terms of largest traffic compromised, lowest total attacking rounds, key captured, and least energy cost. The results demonstrated that the proposed method attained a path compromise probability up to 91% and reduced the cost by 60% in a network size of 100 nodes. The deduction in th
In the era of digital transformation and increasing concerns regarding data privacy, the concept of Self-Sovereign Identity (SSI) has attained substantial recognization. SSI offers individuals greater control over the...
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Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variabilit...
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Background: Only a fraction of the produced social media data is usable in mental health assessment. So the problem of sufficient training data for deep learning approaches arises. Data sufficiency can be presented in...
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Traffic rerouting is a technique used to optimize traffic flow and reduce congestion by redirecting vehicles to alternate routes. The work done in this research focuses on a specific case scenario covering a 25 k...
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Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in ***,dynamic resource allocation and multi-connectivity can be adopt...
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Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in ***,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective *** this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground *** goal was to mitigate co-channel interference while maximizing long-term system *** problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this *** simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interfer...
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Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively un...
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Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.
In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies ...
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In the realm of agricultural automation, the precise identification of crop stress holds immense significance for enhancing crop productivity. Existing methods primarily focus on controlled environments, which may not...
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