Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.

Network intrusion detection system is a significant guarantee means for network security. However, the current network intrusion detection system generally suffers from less prior knowledge which then leads to poor generalizing ability. Network intrusion detection system based on support vector machine is endowed with favorable generalizing ability even with small sample size (less prior knowledge). The paper first gives an introduction to the basic concept of intrusion detection and the basic principle of the classifier based on support vector machine, then discusses algorithm of support vector machine, and finally forms network intrusion detection system combining anomaly intrusion detection and misuse intrusion detection, based on support vector machine.

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

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.

The intrusion detection aims at distinguishing the attack data and the normal data from the network pattern database. It is an indispensable part of the information security system. Due to the variety of network data behaviors and the rapid development of attack fashions, it is necessary to develop a fast machine-learning-based intrusion detection algorithm with high detection rates and low false-alarm rates. In this correspondence, we propose a novel fuzzy method with genetic for detecting intrusion data from the network database. Genetic algorithm is an evolutionary optimization technique, which uses Directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which leads to enhancing the representation ability with a compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with Genetic proposes a new method that can deal with a mixed of database that contains both discrete and continuous attributes and also extract many important association rules to contribute and to enhance the Intrusion data detections ability. Therefore, the proposed method is flexible and can be applied for both misuse and anomaly detection in data-intrusion-detection problems. Also the incomplete database will include some of the missing data in some tuples and however, the proposed methods by applying some rules to extract these tuples. The Genetic-Fuzzy presents a data Intrusion Detection Systems for recovering data. It also include following steps in Genetic-Fuzzy rules: Process data model as a mathematical representation for Normal data.; Improving the process data model which improves the Model of normal data and it should represent the underlying truth of normal Data.; Uses cluster centers or centroids and use distances away from the centroids and convert the Data to Training Data. (8 pages)

Networks have the problem of security attacks like denial of service attacks and others. The firewalls and encrypted software’s does not provide a complete security solution for those attacks. Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this paper, we have proposed an effective Intrusion Detection System in which local agent collects data from its own system and it classifies anomaly behaviors using SVM classifier. Each local agent is capable of removing the host system from the network on successful detection of attacks. The mobile agent gathers information from the local agent before it allows the system to send data. Our system identifies successful attacks from the anomaly behaviors. Experimental results show that the proposed system has high detection rate and low false alarm rate which encourages the proposed system.

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.

Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.

This abstract presents the influence of meteorological and hydrological droughts on groundwater drought conditions through the calculation and analysis of four widely used drought indices: Standardized Precipitation Index (SPI), Reconnaissance Drought index (RDI), Streamflow Drought Index (SDI), and Groundwater Resource Index (GRI). These indices were computed using long-term observed data including precipitation, temperature, discharge, and groundwater levels to capture the temporal and spatial variability of drought events in the Chaliyar River Basin. By analyzing the spatial and temporal variability of groundwater levels and discharge rates and measuring the impact of future drought events under climate change conditions. The research utilizes historical data to understand the relationship between drought and groundwater availability, informing sustainable water management strategies. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. It begins with the collection of groundwater storage data, discharge data, and climate data, alongside outputs from global climate models (GCMs). The data is processed with a Gamma Test to determine the best combination of meteorological and hydrological drought indices as input variables. A Support Vector Machine (SVM) for groundwater simulation, which is optimized through Particle Swarm Optimization (PSO) to select the best SVM parameters. The developed SVM model undergoes validation using 5-fold cross- validation to ensure accuracy. Climate data downscaled for future scenarios, specifically RCP4.5 and RCP8.5, is then utilized to project drought events for the period 2022-2050. This enables an assessment of anticipated changes in groundwater storage and its behavior during future drought events under different climate conditions. This study investigates the interconnected effects of meteorological and hydrological droughts on groundwater storage in a changing climate. Using a combination of observational data, hydrological modelling, and climate projections, it assesses the impacts of drought on groundwater recharge and storage.

As the development of intelligent ships rapidly progresses, the importance of maritime network security escalates. Intrusion Detection Systems (IDS) serve as a pivotal defense mechanism against cyberattacks targeting these vessels. To augment the efficacy of IDS in detecting anomalous network traffic, this paper introduces a novel intrusion detection method utilizing both Transformer and Kolmogorov-Arnold Networks (KAN), designated as Transformer-KAN. This approach effectively mitigates the limitations of traditional intrusion detection algorithms, which include inadequate feature extraction capabilities and the excessive parameterization required to delineate complex patterns. The proposed method employs a Transformer encoder to discern long-range dependencies within input sequences, while KAN layers enhance the model’s ability to approximate complex patterns through non-linear transformations. This integrative strategy ensures elevated accuracy for the intrusion detection algorithm and proves particularly beneficial in intelligent ship environments, where training data may be sparse or computational resources limited. Experimental results confirm that the Transformer-KAN model attains exceptionally high accuracy in both binary and five-class classification tasks, outperforming the capabilities of standalone Transformer and KAN models. Comparative analyses with conventional algorithms—such as Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Deep Belief Networks (DBN), and Support Vector Machines (SVM)-further substantiate the superior effectiveness of the proposed method. This research establishes a new theoretical framework and provides a direction for the practical implementation of network security measures in contemporary intelligent ships.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.

In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network intrusion detection model of IPSO-RBF, made convergence and disturbance variation analysis on improving optimized particle swarm optimization, finally made a experimental simulation to this detection model, the result showed: compared with traditional models, the neural network intrusion detection of improved particle swarm optimization was faster and has a higher accuracy rate as well as efficiency.

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

The rapid growth of the Internet of Things (IoT) expands the attack surface and increases the need for effective intrusion detection under strict privacy and resource constraints. Conventional intrusion detection systems (IDSs) commonly rely on centralized data collection, which can expose sensitive traffic data, increase communication overhead, and create single points of failure. This paper presents a federated learning-based IDS (FL-IDS) that trains a convolutional neural network (CNN) collaboratively across distributed IoT clients without sharing raw traffic data. Each client performs local training on its private data, and a central server aggregates model updates using Federated Averaging (FedAvg) to form a global model. We evaluate the proposed FL-IDS on the Bot-IoT dataset after preprocessing and balancing class via under sampling. Experimental results show that the global model achieves 98.12% accuracy with strong precision, recall, and F1-score, and the learning curves indicate stable convergence across clients. The study demonstrates the feasibility of combining federated learning with deep models for privacy-preserving intrusion detection in distributed IoT settings, while also highlighting key limitations and directions for future work such as evaluation under non-IID client distributions and computational cost analysis.

Industrial processes often include shifting operating phases and dynamics, and system uncertainty. Industrial time series data may obey different distributions because of the time-varying characteristic. Therefore, a single global model cannot describe the local characteristics of multiple distributions. In this work, a hybrid GMM-IGPR model is proposed to solve this kind of time series prediction problem by using an improved Gaussian process regression (GPR) based on Gaussian mixture model (GMM) and a variant of the basic particles swarm optimization (PSO). In a first treatment to the time series, different distributions of the original dataset are characterized by adopting the GMM as a cluster method. Then, multiple localized GPR models are built to characterize the different properties between inputs and output within various clusters. In order to optimize the proposed algorithms, this paper utilizes the DEPSO which introduces differential evolution (DE) operator into the basic PSO algorithm to estimate hyperparameters of the GPR model, instead of using the traditional conjugate gradient (CG) method. Lastly, the Bayesian inference strategy is used to estimate the posterior probabilities of the test data with respect to different clusters. The various localized GPR models are integrated through these posterior probabilities as the weightings so that a global predictive model is developed for the final prediction. The effectiveness of the proposed algorithm is verified by means of a numerical example and a real industrial winding process. Statistical tests of experimental results compared with other popular prediction models demonstrate the good performance of the proposed model.