Intrusion detection learning algorithm through network mining

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.

The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of the network intrusion detection method, which combines a Q-learning based reinforcement learning with a deep feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor, which is set as 0.001 under 250 episodes of training, yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.

Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks

One of the toughest and hardest problems in the field of network security is a network intrusion detection system (NIDS). The selection of features is an important part of processing information and creating a program. Functionalities may affect the achievement or failure of a specific kind of intrusion in the network. Because of the high intrusion information dimension, the selection of features is not completely solved. The new optimization algorithm with strong search capabilities is an improved tree seeds Algorithm (ITSA). The Improved tree seed algorithm and K-Nearest Neighbor (ITSA-KNN) method suggested selecting features to improve the intrusion detection identification performance in this study. In the ITSA process, the effective characteristic of input data is extracted, & KNN is utilized for classification. This document uses several UCI data and KDDCUP 99 data sets to test the model's efficiency. Experimental results show that the proposed model will delete irrelevant characteristics or reduce classifier input dimensions. It can also increase network intrusion sensing accuracy or efficiency.KeywordsIntrusion detectionNetwork intrusion detectionFeature selectionOptimization problemsTree seed algorithmImproved tree-seed algorithmK-nearest neighbor

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

With the rapid development of Internet and information technology, detecting network intrusion behaviors have been attracted more and more attentions. In this paper, we proposed a novel network intrusion detection algorithm using a hybrid ant colony and support vector machine model. Main ideas of SVM are that it denotes a representation of the examples as points in space, and examples of the separate categories are separated by a gap. Afterwards, framework of the detecting network intrusion system is given, which is designed to promote the accuracy of detecting network intrusion by optimizing parameters of support vector machine with ant colony algorithm. Finally, four types of network attack behaviors are utilized in this experiment, that is, a) DOS, b) R2L, c) U2R, and d) Probe. Experimental results demonstrate that the proposed method is able to detect network intrusion with high accuracy.

With the rapid development of Internet technologies, network security threats have significantly increased, emphasizing the need for accurate and efficient network intrusion detection systems (IDS). While traditional IDS methods rely on manual feature selection or deep learning, these approaches often fail to capture the full complexity of network data, especially the diverse structural and content‐related features. To address these limitations, this paper proposes a novel self‐supervised contrastive learning algorithm for network intrusion detection based on a graph isomorphism network ( SCGIN‐ID ). The method transforms network traffic into graph representations, converting the task into a node classification problem. By sampling positive and negative subgraphs centered on target nodes, the proposed approach leverages graph isomorphism networks to capture both structural and content‐based anomalies through self‐supervised contrastive learning. Extensive experiments on two benchmark datasets demonstrate that SCGIN‐ID achieves superior performance compared to five representative baseline models, providing an effective solution for comprehensive and accurate network intrusion detection.

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.

Intrusion detection is an important issue for network security. Many machine learning based detection technologies have been put forward and obtained rather good results, such as Bayesian classifiers, Extreme learning machine. However, the performance of these methods is dependent on parameters to a great extent. Aiming at learning the optimal parameters of extreme learning machine for network intrusion detection, the paper proposes a new network intrusion detection based on hybrid rice algorithm optimized extreme learning machine (HRO-ELM). First, the extreme learning machine parameters are encoded as rice gene location, and the accuracy of test results represents the fitness value of the algorithm. Then the optimal parameters of the extreme learning machine are found by simulating rice breeding behavior, and a network intrusion detection classifier is established. Finally, the KDD99 data set is used and simulation results show that the HRO-ELM improves the accuracy of network intrusion detection and can meet the requirements of network intrusion online detection.

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.

A new round of scientific and technological revolution and industrial reform promote the intelligent development of automobile and promote the deep integration of automobile with Internet, big data, communication and other industries. At the same time, it also brings network and data security problems to automobile, which is very easy to cause national security and social security risks. Intelligent vehicle Ethernet intrusion detection can effectively alleviate the security risk of vehicle network, but the complex attack means and vehicle compatibility have not been effectively solved. This research takes the vehicle Ethernet as the research object, constructs the machine learning samples for neural network, applies the self coding network technology combined with the original characteristics to the network intrusion detection algorithm, and studies a self-learning vehicle Ethernet intrusion detection algorithm. Through the application and test of vehicle terminal, the algorithm generated in this study can be used for vehicle terminal with Ethernet communication function, and can effectively resist 34 kinds of network attacks in four categories. This method effectively improves the network security defense capability of vehicle Ethernet, provides technical support for the network security of intelligent vehicles, and can be widely used in mass-produced intelligent vehicles with Ethernet.

Mining network data for intrusion detection through combining SVMs with ant colony networks

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

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.

The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based cyberattack detection framework. The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions. The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations. To optimally train the proposed DAE, a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy. The efficacy of the suggested framework is evaluated via two standard and open-source datasets. The proposed DAE achieved the accuracies of 98.86%, and 98.26% for NSL-KDD, 99.32%, and 98.79% for the UNSW-NB15 dataset in binary class and multi-class scenarios. The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes. Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.