Table 1.
Comparative Analysis with the existing state-of-the-art methods.
Fig 1.
Proposed flow chart.
Table 2.
Parameters.
Table 3.
Comparative analysis with existing WSN encryption methods.
Fig 2.
Illustrate essential parameters (e.g., Destination Port, Context Identifier, Pattern, Next, and DCI) and correlation matrix of these features for Sinkhole threat detection, emphasizing model efficiency validated through simulations and real-world datasets.
Fig 3.
Count of normal as well as sinkhole attacks.
Displays the tally of normal nodes and the presence of sinkhole attacks, following dataset collection and parameter definition. The classification assigns two classes to nodes, distinguishing normal nodes from those exhibiting specific attacks.
Fig 4.
Description of normal as well as sinkhole traffic.
Outlines the sinkhole attack mitigation function, involving detection by analyzing node traffic history and initiating mitigation if deviations from the typical range are identified. The figure visually depicts normal and sinkhole traffic patterns.
Table 4.
Accuracy and mitigation rate.
Table 5.
Energy consumption.
Table 6.
Comparative table of performance metrics.
Fig 5.
Illustrated the WSN nodes are spread across the simulation with initial topographic dimensions of 500 500 and linked to nine cluster heads. The nodes then start to travel randomly at a speed of 5 meters per second after that.
Fig 6.
Discusses the encryption time grows linearly as the round number and block size increase.
The simulation, starting at 20 seconds, analyzes the FlexenTech cipher’s encryption time concerning the round number and block size. With block sizes from 4 to 128 bits and 128 rounds, the figure illustrates a linear growth pattern in encryption time and network lifetime.
Fig 7.
Comparison of the AES-256 encryption technique and SFlexCrypt encryption timings for data of different sizes. Comparison is shown between SFlexCrypt and AES-256 encryption techniques for different data sizes. The results indicate that SFlexCrypt outperforms AES-256, showcasing its efficiency with variable encryption parameters.
Fig 8.
Power consumption and residual analysis with power simulation time.
Analysis of simulation time utilizing our SFlexCrypt’s flexible selection of encryption settings and two measures of network power consumption and residual.
Fig 9.
Energy consumption comparison of Flex and Sflex.
Comparing SFlexCrypt with fixed-parameter approaches (FlexenTech), the figure demonstrates SFlexCrypt’s ability to reduce power consumption and enhance residual power, emphasizing its efficiency over time across different network scenarios.
Fig 10.
Network lifetime using FlexCrypt, AES, TEA and SflexCrypt.
Shows an analysis of the network’s lifespan using the FlexCrypt, AES, TEA, and SflexCrypt techniques with various starting power values.
Fig 11.
Confusion matrix of LR and KNN, GNB, MNB, RF.
Presents the confusion matrix for logistic regression (LR), k-nearest neighbors (KNN), Gaussian Naive Bayes (GNB), Multinomial Naive Bayes (MNB), and Random Forest (RF) after threat detection, offering a comprehensive view of the classification performance.
Fig 12.
Accuracy of LR, KNN, GNB, MNB, RF.
Displays the accuracy of LR, KNN, GNB, MNB, and RF models, providing an overview of their effectiveness in correctly classifying attack instances.
Fig 13.
Threat detection, as well as attack mitigation.
Highlights the results of threat detection and mitigation, showcasing the proposed method’s success in accurately identifying Helloflood, Sinkhole, and Wormhole attacks with a mitigation rate of 97.31%.
Fig 14.
Metrics comparison b/w Sflexrcypt & flexcrypt.
Compares metrics between Sflexcrypt and Flexcrypt, providing insights into their performance differences in areas like energy consumption, packet delivery ratio, packet loss, and delay.
Fig 15.
Metrics comparison Overview b/w Sflexrcypt & flexcrypt.
Offers an overview of metric comparisons between Sflexcrypt and Flexcrypt, emphasizing Sflexcrypt’s superior performance across various categories such as energy efficiency, packet delivery ratio, packet loss, and delay based on hypothetical data.
Table 7.
Comparative analysis with the existing state-of-the-art methods.