Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.

Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.

Plasma SCF/c-kit Levels in Patients with Dipper and Non-Dipper Hypertension

Objective The aim of this study was to investigate the relationship between peripheral plasma stem cell factor (SCF)/c-kit levels and the types of dipper and non-dipper hypertension in hypertensive patients.Methods This cross-sectional study included newly diagnosed hypertensive patients who underwent 24-hour ambulatory blood pressure monitor (ABPM) between January 2009 and 2012 in Jiangning city. Patients were divided into the dipper group and the non-dipper group according to ABPM measurements. The levels of SCF and its receptor c-kit, tumor necrosis factor-α (TNF-α) and interleukin 6 (IL-6) in peripheral blood were measured via enzyme-linked immunosorbent assays. The serum levels of glucose and lipid were examined as well. The levels of SCF/c-kit were compared between the dippers and the non-dippers; and their correlation with 24-hour mean systolic blood pressure (MSBP), 24-hour mean diastolic blood pressure (MDBP), TNF-αand IL-6 were investigated using linear regression analyses statistically.Results A total of 247 patients with newly diagnosed hypertension were recruited into the study, including 116 non-dippers and 131 dippers. The levels of peripheral plasma SCF were higher in non-dipper group (907.1±52.7 ng/L vs. 778.7±44.6 ng/L; t=2.837, P<0.01), and the levels of c-kit were higher in non-dipper group too (13.2±1.7 μg/L vs 9.57±1.4 μg/L; t=2.831, P<0.01). Linear regression analysis revealed that SCF/ckit levels were significantly positively correlated with MSBP, MDBP, plasma TNF-α, and IL-6 levels (all P<0.01).Conclusions Peripheral plasma SCF/c-kit levels are higher in patients with non-dipper hypertension than those with dipper one, and significantly correlate with 24-hour MSBP, 24-hour MDBP, serum TNF-α and IL-6 levels.

Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases.By varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal cases.In addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification decision.To guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO algorithm.To test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and F1-score.Moreover,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics.The minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.

Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance.The bandwidth restriction associated with small antennas can be solved using metamaterial antennas.Machine learning is gaining popularity as a way to improve solutions in a range of fields.Machine learning approaches are currently a big part of current research,and they’re likely to be huge in the future.The model utilized determines the accuracy of the prediction in large part.The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and gain.The basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)algorithm.To choose the best features from the dataset,the binary(bDTO)algorithm is exploited.The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically.In addition,two other ensembles are incorporated in the conducted experiments for comparison.These ensembles are average ensemble and K-nearest neighbors(KNN)-based ensemble.The comparison is performed in terms of eleven evaluation criteria.The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

Using Polaris and the Big Dipper to approximate local standard time

In this paper we introduce methods for approximating local standard time in the Northern Hemisphere using Polaris and the Big Dipper as well as alternative reference stars, and describe in detail how to construct a device we call a dipperclock to facilitate this process. An alternative method which does not require a dipperclock is also discussed. Ways of constructing dipperclocks which glow in the dark are presented. The accuracy of dipperclocks is examined, both theoretically and through field testing. A java program is provided for creating dipperclocks customized to a particular year-long time period and place to get improved accuracy. Basic astronomical definitions and justifications of the results are provided. We also discuss the use of dipperclocks to find longitude and latitude.

Virtual simulation analysis and verification of seed-filling mechanism for dipper hill-drop precision direct rice seeder

In order to improve the seeder’seed-filling ability of the dipper hill-drop precision direct rice seeder,and to meet the mechanization requirement of high speed operation,the self-designed seeder was taken as the objective to explore its seed-filling mechanism and the movement status of rice seed in seed box from the perspective of mechanics.The force models of seed-filling process by dipper were established,and the influential regularity of its rotation speed to compressive resistance of seed population was analyzed as well.The image processing Module-Clipping of discrete element simulation software EDEM was used in the virtual simulation analysis for the process of the seed filling into the dipper,and the velocity relation curve and the force changing curve between rotation speed and seeds were obtained.According to the virtual experiment,the composite filling force of seeds,i.e.the qualified rate on filled rice seed amounts was the largest when rotation speed was at 40 r/min.The performance test bed of seeder was used to verify the simulation results,in which the qualified rate on scooped rice seed amounts was taken as the index,and six rotation speeds of seed-filling dipper were also selected for analysis of seed-filling ability of the device.The results are as follows:with the increase of working speed,the qualified rate on filled rice seed amounts fluctuated with a trend of cosine curve,the largest value was 94.16%occurred when the rotation speed of seed-filling dipper was at 40 r/min.The variation trend of simulation value was approximately consistent with that of verification value.The study can provide a reference for the research and development of mechanical seeder.

“兔毫”与五胡时代西北地区的丧葬文化传播

吐鲁番衣物疏及实物出土的“兔毫”(狐毛)既不是制笔原料,也不是当时人财富的象征。衣物疏中与“兔毫”经常同时出现的“黄桑棺”“弩机”等,也在同时代的河西等地存在,表明两地在丧葬文化上具有一脉相承性。放置于墓主人头部的兔毫与棺木档板或壁画中的北斗、神树、手爪囊等共同构成了相对完整的墓葬逻辑。以千束、万束的兔毫等丝缕结成前往北斗、祈求长生的阶梯,从而达到灵魂升天的目的。又通过手脚指甲含血、被肉,完成复形,继续享受荣华富贵。这是西汉以来北斗求道升仙原始信仰的体现。

良渚文化琮、璧的“权”“衡”功用及其纹饰的天文蕴意

良渚文化玉琮是“权”(秤锤),玉璧则是可以度量长度的“尺”,其他玉器如玉璜等,都曾作为度量衡的标准器物。度量衡在重量、长度、容积方面的确定性要求,使这类器物成了“瑞信”和权力的象征。由于上古度量衡的确定与律历紧密相关,而律历的产生关乎天上的北极星。北斗九星是古时的北极,所以北斗第四星名权星,第五星名玉衡,北斗斗杓也称玉衡;斗柄口的玄戈、招摇两星又名“天蜂”,这天上的“大蜂”是权、衡,也是北斗的象征。良渚文化玉琮上的上下两个神面(兽面)纹,既非猪纹,也非鸟纹,而是蜂纹。后世器物上所谓的“蝉纹”,或是蜂纹之误。

THE BIG DIPPER GUIDE

China has high expectations for its domestically produced global positioning system When a catastrophic earthquake measuring 8.0 on the Richter scale rocked Wenchuan County in China’s

血压昼夜节律变化与颅内动脉粥样硬化斑块特征的相关性研究

目的调查血压昼夜节律变化与颅内动脉粥样硬化斑块负荷及易损性的相关性。方法回顾性分析一个颅内动脉粥样硬化卒中影像队列中的267例卒中患者的临床及影像特征,根据24 h动态血压将其分为杓型血压组、非杓型血压组和反杓型血压组,通过高分辨磁共振血管壁成像及图像分析获得颅内动脉Willis环近端血管壁斑块特征,包括最大管壁厚度、斑块内出血、中重度狭窄(狭窄程度≥50%)、多发斑块(斑块数量≥3个)等指标。比较3组基线特征及颅内动脉粥样硬化斑块特征,校正混杂因素后,采用logistic回归分析血压昼夜节律变化模式与颅内动脉粥样硬化斑块特征的关系。结果杓型血压组、非杓型血压组和反杓型血压组分别有36、119及112例。(1)反杓型血压组年龄更大(反杓型血压组∶非杓型血压组∶杓型血压组=67.3∶64.6∶61.9岁,P=0.042),合并糖尿病比例更高(反杓型血压组∶非杓型血压组∶杓型血压组=46.4%∶41.2%∶22.2%,P=0.037);血压指标中,反杓型血压组24 h平均收缩压更高(反杓型血压组∶非杓型血压组∶杓型血压组=144∶139∶136 mmHg,P=0.025)。(2)杓型血压组、非杓型血压组和反杓型血压组的平均最大管壁厚度分别为2.39 mm、2.48 mm和2.52 mm(P=0.554),斑块内出血比例分别为33.3%(12/36)、36.1%(43/119)和37.5%(42/112)(P=0.901);3组中重度狭窄及多发斑块比例差异亦无统计学意义(中重度狭窄比例在杓型血压组、非杓型血压组和反杓型血压组分别为22.2%、32.8%和37.5%,P=0.236;多发斑块比例在3组分别为63.9%、73.9%和75.0%,P=0.407)。(3)多因素logistic回归分析结果显示:年龄(OR 1.053,95%CI 1.027~1.080,P<0.001)和糖尿病(OR 2.194,95%CI 1.186~4.057,P=0.012)与颅内动脉多发斑块独立相关。未发现血压昼夜节律变化模式与颅内动脉多发斑块、中重度狭窄以及斑块内出血存在相关性(均P>0.05)。结论增龄和糖尿病是颅内动脉多发斑块的独立危险因素,血压昼夜节律变化与颅内动脉粥样硬化斑块易损性之间的关系还有待进一步研究。

动态血压指标对老年高血压患者蛛网膜下腔阻滞后低血压的预测价值

目的 探讨动态血压指标对老年高血压患者蛛网膜下腔阻滞后低血压的预测价值。方法 选取2020年6月至2021年12月青海红十字医院收治的119例于蛛网膜下腔阻滞下接受骨科手术的老年(年龄≥60岁)高血压患者为研究对象,根据蛛网膜下腔阻滞后是否发生低血压将研究对象分为血压正常组(85例)和低血压组(34例),比较分析两组患者的临床特征和动态血压指标。分析老年高血压患者蛛网膜下腔阻滞后发生低血压的独立影响因素以及非杓型血压对老年高血压患者蛛网膜下腔阻滞后发生低血压的预测价值。结果 低血压组患者的年龄、美国麻醉医师协会(American Society of Anesthesiologists,ASA)分级为Ⅱ级的比例、使用β受体阻滞剂的比例以及非杓型血压的比例均显著大于或高于血压正常组(P<0.05)。多因素logistic回归分析结果显示,使用β受体阻滞剂、非杓型血压是老年高血压患者蛛网膜下腔阻滞后发生低血压的独立危险因素(P<0.05)。受试者工作特征曲线分析结果显示,年龄、ASA分级为Ⅱ级、使用β受体阻滞剂三联指标预测老年高血压患者蛛网膜下腔阻滞后发生低血压的曲线下面积为0.64,敏感度为76.2%,特异度为56.9%。年龄、ASA分级为Ⅱ级、使用β受体阻滞剂、非杓型血压四联指标预测老年高血压患者蛛网膜下腔阻滞后发生低血压的曲线下面积为0.81,敏感度为90.6%,特异度为64.7%。四联指标预测模型的效能显著高于三联指标(χ^(2)=16.254,P<0.001)。结论 非杓型血压是老年高血压患者蛛网膜下腔阻滞后发生低血压的独立危险因素,且加入非杓型血压指标的预测模型对老年高血压患者蛛网膜下腔阻滞后发生低血压的预测效能显著升高。

不同血压类型对老年2型糖尿病患者心肾血管事件的影响分析

目的分析不同血压类型对老年2型糖尿病(T2DM)患者随访3年发生心肾血管事件的影响,并分析影响心肾血管事件的因素。方法前瞻性队列研究。连续纳入2016年1月至2019年1月商丘市第一人民医院收治的829例老年T2DM患者,根据24 h动态血压监测情况分为三组:杓型血压组(417例)、非杓型血压组(304例)和反杓型血压组(108例)。随访3年,主要研究终点是心肾血管事件,包括心血管死亡、非致死性心肌梗死、非致死性卒中、大量蛋白尿、肾功能持续下降和需要持续肾脏替代治疗的复合终点。结果829例老年T2DM患者,女性448例(54.0%),平均年龄为(67.9±6.8)岁。三组中反杓型血压组患者的年龄更大、女性更多、吸烟比例更高,更多合并高血压、高血脂、心肌梗死、卒中和心房颤动,空腹血糖、糖化血红蛋白和尿白蛋白/肌酐比的水平更高,相对室壁厚度和左室质量指数更高,应用钙通道阻滞剂的患者更多(均为P<0.05)。中位随访32个月,共有82例心肾血管事件,Kaplan-meier生存分析结果显示,与杓型血压组比较,反杓型血压组和非杓型血压组患者的非致死性心肌梗死(7.40%、3.29%比2.16%,分别为HR=2.837,95%CI:1.409~5.712,P=0.004;HR=1.442,95%CI:1.055~1.971,P=0.022)和心肾血管事件(21.30%、10.20%比6.71%,分别为HR=2.342,95%CI:1.163~4.716,P=0.017;HR=1.284,95%CI:1.012~1.629,P=0.040)的发生风险均显著增高。校正后,多因素Cox回归分析结果显示,年龄(HR=1.152)、女性(HR=1.146)、吸烟(HR=1.437)、高血压(HR=1.519)、陈旧心肌梗死(HR=1.413)、既往卒中(HR=1.462)、心房颤动(HR=1.129)、糖化血红蛋白(HR=1.263)、尿白蛋白/肌酐比(HR=1.269)、左室质量指数(HR=1.147)、夜间收缩压(HR=1.219)、夜间舒张压(HR=1.106)、非杓型血压(HR=1.519)、反杓型血压(HR=2.337)和使用血管紧张素转换酶抑制剂(HR=0.862)均为心肾血管事件的相关因素。结论反杓型血压和非杓型血压增加老年T2DM患者发生心肾血管事件、特别是非致死性心肌梗死的风险。

Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies used in IoT applications,have led to major security concerns.Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient.Several artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent years.Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features selected.On the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent decades.In this paper,we proposed a hybrid optimization algorithm for feature selection in IDS.The proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as GWDTO.The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance.On the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its superiority.In addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed approach.Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.