Network Intrusion Detection Based on Hybrid Rice Algorithm Optimized Extreme Learning Machine

In the era of the rapid development of the Internet, network security issues are receiving increasing attention. Aiming at the shortcomings of extreme learning machines in network intrusion detection, this paper uses Antlion optimization algorithm (ALO) to optimize parameters in extreme learning machine (ELM) to obtain a network intrusion inspection model (ALO-ELM) with higher detection accuracy.), And compare this algorithm model with the network intrusion detection model optimized using GA and PSO. The experimental results show that the network intrusion model established in this paper performs the best. It can improve the accuracy of network intrusion detection to a certain extent, and improve the detection efficiency and real-time performance.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

In order to improve the accuracy of network intrusion detection, it is necessary to optimize Support Vector Machine (SVM) parameters. In view of the advantages of Elephant Herding Optimization (EHO) algorithm, such as simple control parameters and easy combination with other algorithms, this paper tries to optimize these parameters by using an Improved EHO (IEHO) algorithm. The IEHO-SVM algorithm is then proposed for parameters optimization, in order to improve the accuracy of network intrusion detection. The simulation experiment uses the KDD CUP99 data set for verification analysis. The experimental results show that, compared with the Particle Swarm Optimization (PSO)-SVM algorithm, Month-flame Optimization (MFO)-SVM algorithm and the basic EHO-SVM algorithm, the IEHO-SVM algorithm not only improves the global search ability of network intrusion, but also increases the accuracy rate of network intrusion detection by an average of 7.36%, 4.23% and 5.56% respectively, and reduces the false alarm rate by an average of 3.04%, 2.41% and 3.07% respectively, which aims at improving the efficiency of network intrusion detection.

At present, the electric power information system has been more widely used. The power sector in the realization of management information at the same time, its security problems are also increasingly severe. It can be said that the security problem of the electric power information has become a hidden danger to the safe and stable operation of the power system. There is a corresponding intrusion problem in the power information network, which affects the stability and security of the power system to a great extent. Aiming at the problem of a long time and low accuracy of network intrusion detection in the power monitoring systems, this paper proposes a method of network intrusion detection in power monitoring systems based on data mining technology. The method can ensure the safe and stable operation of the power monitoring system. This method needs to extract the data characteristics of network intrusion as the initial data set of intrusion detection method, and then apply the clustering partition method to preprocess the data to provide accurate data basis for intrusion detection method, and finally, based on data mining technology, complete the intrusion detection of power monitoring system network. A simulation experiment is designed to test the feasibility and accuracy of the method, and the experimental results show that, compared with the traditional intrusion detection method, the proposed method is faster by about 1.8s, the intrusion detection accuracy is higher by 15.6% for 500 groups of data, and the false alarm rate is lower by 12.3%. It can be proved that the design method in this paper has high efficiency and accuracy, and can provide technical support for the safe operation of the power system.

With the rapid development of network technology, active defending of the network intrusion is more important than before. In order to improve the intelligence and accuracy of network intrusion detection and reduce false alarms, a new deep neural network (NDNN) model based intrusion detection method is designed. A NDNN with four hidden layers is modelled to capture and classify the intrusion features of the KDD99 and NSL‐KDD training data. Experiments on KDD99 and NSL‐KDD dataset shows that the NDNN‐based method improves the performance of the intrusion detection system (IDS) and the accuracy rate can be obtained as high as 99.9%, which is higher when compared with other dozens of intrusion detection methods. This NDNN model can be applied in IDS to make the system more secure.

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r 2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

Catenary components state detection method based on the dimension reduction-kernel extreme learning machine

The Network Security Monitoring System (NSMS) can use Big Data (BD) and K-means DT (K-means with distance threshold) algorithms to automatically learn and identify abnormal patterns in the network, improving the accuracy of network threat detection. In this article, KDD Cup 1999 and NSL KDD were selected as NSMS for dataset analysis. Preprocess the data; Extract statistical information, time series information, and traffic distribution characteristics. Value device DT further classifies regular attacks, remote location (R2L) attacks, and user to root (U2R) permission attacks. The experimental results show that the hybrid intrusion detection algorithm based on K-means DT achieves a network attack detection accuracy of 99.2% and a network attack detection accuracy of 98.9% on the NSL-KDD dataset. Hybrid intrusion detection algorithms can effectively improve the accuracy of network intrusion detection (NID). The hybrid intrusion detection system proposed in this article performs well on different datasets and can effectively detect various types of network intrusion attacks, with better performance than other algorithms. The NSMS designed in this article can cope with constantly changing network threats.

Network intrusion detection is of great significance for network security in Local Area Network (LAN). Traditional methods such as firewalls do not completely protect against attacks on the LAN due to lack of continuous learning. Recently, the ability of convolutional neural networks (CNN) to extract features in the field of computer vision has received extensive attention. CNN can automatically extract effective complex features to adapt to constantly changing environments, which is especially important in network intrusion detection. In this paper, we focus on network security in the LAN. We propose an approach based on CNN to implement intrusion detection in LAN. This approach can effectively identify network attacks and has an accuracy of 98.34% on the KDD99 dataset. The experimental results show that the proposed approach based on the CNN has high accuracy in intrusion detection.

The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.

The electricity demand of power system and power market change with the development of economy, short-term electricity demand forecasting play a very important role in spot power Market. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so an electricity demand forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, it has shown than ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy. Under the spot power market, accurately predicting the electricity demand trend and reducing the deviation of electricity expenditure have become an important means for the market players to make profits.

Aiming at solving the problems of low accuracy and high false alarm rate for network intrusion detection, an algorithm of intrusion detection based on PSO and kernel Extreme learning is proposed in this article. Particle Swarm Optimization (PSO) is a kind of swarm intelligent algorithm. Kernel extreme learning machine (KELM) is a kind of classical learning method of kernel machine with its fast learning rate and strong generalization ability, but the selection of kernel function and parameters of KELM directly affects its classification performance. In this article, PSO is used to optimize the kernel parameters of kernel limit learning machine. The global kernel function with strong generalization ability of linear combination and the local kernel function with strong learning ability are used to form multi-kernel limit learning machine, which can improve the performance of single kernel Extreme Learning Machine (ELM) classifier. Finally, the performance of the algorithm is compared and analyzed through experiments and the experimental results verify the validity of this algorithm.

To assist specialists in detecting breast cancer on mammograms with better accuracy and less time consuming, this paper proposes an approach based on improved sunflower optimization algorithm (ISFO) and extreme learning machine (ELM). Firstly, features were extracted by using lifting scheme and gray-level co-occurrence matrix (GLCM). Then, the parameters of ELM were optimized by (ISFO) to obtain the final classification results. Finally, in order to avoid overfitting, the proposed model’s performance was evaluated with k-fold random stratified cross validation, and the experiments compared the model with other models on MIAS datasets. The experimental results show that the proposed model has higher classification accuracy, shorter learning time and stronger robustness on mammograms classification task. Thus, this method could be a promising application in bio-medical and provide a basis for the early diagnosis of breast cancer.

To capture the uncertainty of bus travel time prediction, we propose an approach ELM-PSOLF to build prediction intervals (PIs). The approach adopts ELM (extreme learning machine) to generate PIs. Further, the parameters of ELM are tuned using PSOLF (particle swarm optimization with levy flight). Experiment results show that ELM-PSOLF can produce high-quality PIs.

Structural damage assessment is an important part of structural health monitoring. Nevertheless, there is the defect of low accuracy in the existed evaluation methods. In order to solve this problem, a new model for structural damage assessment using particle swarm optimization (PSO) and extreme learning machine (ELM) named PELM is proposed in this paper. Firstly, PSO is used to calculate the best parameters of ELM, and then the optimized ELM model is adopted to evaluate the structural damage. PELM model and other two different models are used to assess the structural damages of a pile structure as an experiment, the results show that PELM model has a higher prediction accuracy than SVM model and ELM model.