Intrusion Detection In Wireless Sensor Networks Research Articles

MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs

AbstractIn the domain of cyber-physical systems, wireless sensor networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates machine learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the wireless sensor network dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary classification scenarios, it achieves an accuracy rate of 99.78%, and in multiclass classification scenarios, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs.

Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods

This paper proposes an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The study employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. The Synthetic Minority Excessively Technique is introduced for data balancing, followed by intrusion detection systems and network traffic categorization. The research evaluates a deep learning-based feed-forward neural network algorithm's accuracy, precision, recall, and F-measure across three vital datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. Comparative analysis against benchmark machine learning approaches is also conducted. The proposed algorithm demonstrates exceptional performance, achieving high accuracy and reliability in intrusion detection for WSNs. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security.

Hybrid Model-Based Intrusion Detection in Wireless Sensor Network on the Basis of Risk and Link Quality

Nowadays, Wireless Sensor Networks (WSN) face more security threats due to the increased service of data transmission at high speed in almost all applications. The security of the network must be ensured by identifying abnormal traffic and current emerging threats. The most promising model for safeguarding the core network from outside attacks is Intrusion Detection Systems (IDS). This work focuses on the introduction of clustering-based intrusion detection in WSN. Initially, clustering takes place, where the nodes are grouped under certain constraints via selecting the optimal Cluster Head (CH). The considered constraints are energy, delay, distance, risk, and link quality. This optimal selection takes place by a new hybrid optimization algorithm termed as Truncate Combined Bald Eagle Optimization (TCBEO) algorithm. The subsequent process is intrusion detection, where a hybrid detection model combining a Convolutional Neural Network (CNN) & Bi-directional Gated Recurrent unit (Bi-GRU) is employed, which is trained with features like improved entropy and correlation taking into consideration of constraints like energy and distance, respectively. Eventually, the suggested work’s effectiveness is affirmed against existing techniques using various performance metrics.

[formula omitted]CA-GAM-ID: Coupling of probabilistic principal components analysis with generalised additive model to predict the [formula omitted]barriers for intrusion detection

Drastic advancement in computing technology and the dramatic increase in the usage of explainable machine learning algorithms provide a promising platform for developing robust intrusion detection algorithms. However, the development of these algorithms is constrained by their applicability over specific scenarios of Wireless Sensor Networks (WSNs). We introduced a hybrid framework by combining Probabilistic Principal Component Analysis (P2CA) and Generalised Additive Model (GAM), which is performing well for all the scenarios of WSNs. To demonstrate our framework’s broad applicability, we evaluated its performance over three publicly available intrusion detection datasets (i.e., LT-FS-ID, AutoML-ID, and FF-ANN-ID), each from different scenarios. Our findings highlight that the presented framework can accurately predict the number of k−barriers for all three datasets. Furthermore, we conducted a comprehensive performance comparison between our proposed framework and benchmark algorithms, which revealed that our approach outperforms all of them. Additionally, we evaluated the framework’s versatility by testing its performance on datasets unrelated to intrusion detection, specifically ALE datasets. Notably, our approach accurately predicted the response variable in these datasets and exceeded the performance of its primary algorithm, further demonstrating its robustness and adaptability.The implications of this research are substantial. By developing a robust intrusion detection framework that performs well across diverse WSN scenarios, we address a critical need for reliable network security in various domains, including industrial IoT, smart cities, and environmental monitoring. Our findings not only enhance the understanding of intrusion detection in WSNs but also pave the way for developing more sophisticated and adaptable systems to safeguard sensitive data and critical infrastructure.

Border‐hunting optimization for mobile agent‐based intrusion detection with deep convolutional neural network

SummaryThe sensor nodes, which are available in the wireless sensor networks (WSN), are equipped with sensing abilities, and communication. Several domains require the sensor nodes to be arranged in aggressive surroundings, in which observing malicious activities within the sensor network. Therefore, the present research proposes the border‐hunting optimization‐based deep CNN (BHO‐DCNN) for the mobile agent (MA)‐based intrusion detection in WSN. The importance of the research relies on the BHO‐DCNN model for identifying the intrusion available in the network is established by integrating the optimization with its features through a deep classifier for detection in a precise manner. The algorithm follows the communal hierarchy, surrounding, group hunting, and prey attacking, which provides an enhanced rate of convergence in the method of detection. The analysis is achieved through the database IDS 2018 Intrusion CSVs depending on the performance like delay, alive nodes, normalized energy, as well as throughput. The obtained number of alive nodes through the developed BHO‐DCNN algorithm is 45, end‐to‐end delay is 0.2572 ms, normalized energy is 0.1622 J, as well as throughput, is 0.3125% for nodes 50 at the populate rate of 100, respectively.

Intrusion Detection Model for Wireless Sensor Networks Based on MC-GRU

A crucial line of defense for the security of wireless sensor network (WSN) is intrusion detection. This research offers a new MC-GRU WSN intrusion detection model based on convolutional neural networks (CNN) and gated recurrent unit (GRU) to solve the issues of low detection accuracy and poor real-time detection in existing WSN intrusion detection algorithms. MC-GRU uses multiple convolutions to extract network data traffic features and uses the high-level features output after convolution operations as input parameters of the GRU network, which strengthens the learning of spatial and time series features of traffic data and improves the detection performance of the model. The experiment results based on the WSN-DS dataset show that the overall detection accuracy of the four types of attack of black hole, gray hole, flooding, and scheduling and normal behaviors reaches 99.57%, and it is also better than the existing WSN intrusion detection algorithms in real-time performance and classification ability.

A New Technique for Wireless Sensor Network Intrusion Prevention Using Dense Artificial Neural Networks

Abstract: A wide range of industries, including security, healthcare, and industrial settings, currently employ wireless sensor networks (WSNs). With a limited power supply, bandwidth, and energy consumption, WSNs are unique. While traditional networks can be protected in many ways, WSNs cannot be protected in the same way. In order to enhance the service safety of wireless sensor networks, new concepts as well as methodologies were needed. In WSNs, intrusion prevention is the most important issue. For WSN Intrusion Detection (IDS), this research used Dense Artificial Neural Networks (DANN) to develop a DL technique (DeepANN). Compared to the previous models, the proposed Analytical model achieved a 95 percent accuracy rate. The ANN model outperforms the other ML models, with F1 scores of 99, 98, and 96.

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network.

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.

A cross-layer based optimized feature selection scheme for intrusion detection in wireless sensor network

The Wireless Sensor Networks (WSNs) contain a significant quantity of sensor nodes that computes and communicates for data transmission. The data packet sensed and transmitted contains various cross layer feature set that includes many important information. Many essential aspects, which include storage capability, consumption of energy, and, computational power should be taken into account while dealing with the data packets. On the other hand, many past researchers have carried out their work in order to detect intrusion utilizing cross-layer packets but fail in detecting them at the same time. Cross-layer and feature selection techniques play a key role in building an efficient Intrusion Detection System (IDS). An advantage of using the cross-layer technique is to achieve a higher correlation among different layers of the protocol so that one layer can use the parametric information of the other layer by breaking the traditional layer barriers. In this work, we propose a cross-layer based multi-feature selection model for intrusion detection in WSNs. Firstly, an optimized multi-feature selection algorithm is proposed for selecting efficient and useful features from the cross-layered architecture of the network. Secondly, a multi class intrusion detection model is proposed for the classification of different cross-layer based intrusion in the network. The proposed algorithm is developed for providing total security to cross-layer based networks by selecting prominent features and detecting intrusion at the same time. The simulation results are utilizing on real-time intrusive data from the network by analyzing the proposed model.

Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

As an effective security protection technology, intrusion detection technology has been widely used in traditional wireless sensor network environments. With the rapid development of wireless sensor network technology and wireless sensor network applications, the wireless sensor network data traffic also grows rapidly, and various kinds of viruses and attacks appear. Based on the temporal correlation characteristics of the intrusion detection dataset, we propose a multicorrelation-based intrusion detection model for long- and short-term memory wireless sensor networks. The model selects the optimal feature subset through the information gain feature selection module, converts the feature subset into a TAM matrix using the multicorrelation analysis algorithm, and inputs the TAM matrix into the long- and short-term memory wireless sensor network module for training and testing. Aiming at the problems of low detection accuracy and high false alarm rate of traditional machine learning-based wireless sensor network intrusion detection models in the intrusion detection process, a wireless sensor network intrusion detection model combining two-way long- and short-term memory wireless sensor network and C5.0 classifier is proposed. The model first uses the hidden layer of the bidirectional long- and short-term memory wireless sensor network to extract the features of the intrusion detection data set and finally inputs extracted features into the C5.0 classifier for training and classification. In order to illustrate the applicability of the model, the experiment selects three different data sets as the experimental data sets and conducts simulation performance analysis through simulation experiments. Experimental results show that the model had better classification performance.

Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection

The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.

Research on Computer Network Technology System Based on Artificial Intelligence Technology

This article introduces computer network intrusion detection systems and their classification based on artificial intelligence technology, and points out the challenges that intrusion detection systems (IDS) are facing in wireless sensor computer networks. On this basis, a hierarchical multi-layer wireless sensor network intrusion detection system and technology based on artificial intelligence technology Agent is proposed, and its network structure, working principle and performance are analysed.

Research on Attack and Defense Game Model for Improving the Efficiency of Network Intrusion Detection System

The widespread use of wireless networks has expanded people’s ability to obtain information, but its inherent network characteristics make it more vulnerable to cyber-attacks. Existing intrusion detection systems usually only target specific attack methods, but are incapable of other attacks. Based on this research background, taking game theory as the theoretical basis, it analyses the offensive and defensive process in wireless sensor networks, and demonstrates the necessity of implementing an intrusion detection system by analysing model equilibrium solutions. In view of the diversity of attack methods of network intruders, the game model is further improved, and a non-cooperative complete information static game model is established. By analysing the mixed Nash equilibrium solution of the model, the best defines strategy of the intrusion detection system is obtained and the system is balanced. Energy consumption and detection efficiency. Simulation experiment results show that the wireless sensor network intrusion detection system based on offensive and defensive game can not only effectively resist multiple network attacks, but also reduce the energy consumption caused by the intrusion detection system and prolong the service life of the network.

Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey

Machine Learning is capable of providing real-time solutions that maximize the utilization of resources in the network thereby increasing the lifetime of the network. It is able to process automatically without being externally programmed thus making the process more easy, efficient, cost-effective, and reliable. ML algorithms can handle complex data more quickly and accurately. Machine Learning is used to enhance the ability of the Wireless Sensor Network environment. Wireless Sensor Networks (WSN) is a combination of several networks and it is decentralized and distributed in nature. WSN consists of sensor nodes and sinks nodes which have a property of self-organizing and self-healing. WSN is used in other applications, such as biodiversity and ecosystem protection, surveillance, climate change tracking, and other military applications.Now-a-days, a huge development is seen in WSNs due to the advancement of electronics and wireless communication technologies, several drawbacks like low computational capacity, small memory, and limited energy resources infrastructure needs physical vulnerability to require source measures where privacy plays a key role.WSN is used to monitor the dynamic environments and to adapt to such situation sensor networks need Machine Learning techniques to avoid unnecessary redesign. Machine learning techniques survey for WSNs provide a wide range of applications in which security is given top priority. To secure data from attackers the WSNs system should be able to delete the instruction if any hackers/attackers are trying to steal data.

Wireless sensor network intrusion detection system based on MK-ELM

Advances in digital electronics, wireless communications, and electro-mechanical systems technology have revolutionized the society and economy across the globe by enabling the development of low-cost, low-power, and multi-functional sensor nodes, from which the sensor networks are realized by leveraging the features of sensing, data processing, and communication present in these nodes. Though the energy of the wireless sensor network (WSN) nodes is limited, the detection of existing intrusion detection systems in WSN is weakly accurate further. To reduce the energy consumption of nodes in WSNs during detection processing, we propose a hierarchical intrusion detection model that clusters the nodes in a WSN according to their functions. Even more, to improve the detection accuracy of abnormal behavior of the WSN intrusion detection system and reduce the false alarm rate, it is considered in this research the usage of the classification algorithm of kernel extreme learning machine, following to Mercer Property to synthesize multi-kernel functions. We realize the optimal linear combination by testing and applying the multi-kernel function and build a multi-kernel extreme learning machine to WSN intrusion detection systems. Simulation results show that the system not only guarantees a high detection accuracy but also dramatically reduces the detection time, being well suited for resource-constrained WSNs.

Intrusion detection of wireless sensor networks based on IPSO algorithm and BP neural network

The sensor nodes of wireless sensor networks (WSNs) are deployed to an open and unsupervised region, and they are vulnerable to various types of attacks. Intrusion detection system can detect network attacks that nodes suffer from. This paper combines improved particle swarm optimisation (IPSO) algorithm and back-propagation neural network (BPNN), named IPSO-BPNN. We propose an intrusion detection model of WSNs based on a hierarchical structure. First, we use IPSO algorithm to optimise the initial parameters of BPNN to avoid falling into the local optimum. Then, we apply IPSO-BPNN to the intrusion detection of WSNs. Finally, we use benchmark NSL-KDD and UNSW-NB15 datasets to verify the performance of the IPSO-BPNN. The simulation results show that IPSO-BPNN has faster convergence speed, higher detection accuracy rate and lower false positive rate compared with BPNN and BPNN optimised by PSO algorithm, which can meet the WSNs intrusion detection requirements.

A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept

Nowadays Wireless Sensor Network (WSN) mainly faces security issue during packet transmission between different sensor nodes in network combined with data mining. To overcome this challenge an efficient clustering technique called adaptive chicken swarm optimization algorithm is proposed for cluster head (CH) selection. By this adaptive method the time consumption is reduced to a greater extend along with that the lifetime of the network and the scalability is improved alternatively. Additionally a two stage classification technique known as adaptive SVM classification a supervised learning technique is proposed with Intrusion Detection System (IDS) where an acknowledgement based method is utilized for reporting the malicious sensor nodes. By this acknowledgement different types of attacks such as DOS, probe, U2R, R2L are detected incorporation with Intrusion Detection System (IDS). Once detected a high level security mechanism along with intrusion response is provided to other sensor nodes by which a secure packet transmission occurs between different sensor nodes. The proposed methodology is implemented in python platform and the comparison results provided with existing methods proves a better result.

Intrusion Detection in Wireless Sensor Networks with an Improved NSA Based on Space Division

Inspired by the biological immune system, many researchers apply artificial immune principles to intrusion detection in wireless sensor networks, such as negative selection algorithms, danger theory, and dendritic cell algorithms. When applying the negative selection algorithm to wireless sensor networks, the characteristics of wireless sensor networks, such as frequent changes in network topology and limited resources, are not considered too much, which makes the detection effect to need improvement. In this paper, a negative selection algorithm based on spatial partition is proposed and applied to hierarchical wireless sensor networks. The algorithm first analyzes the distribution of self-set in the real-valued space then divides the real-valued space, and several subspaces are obtained. Selves are filled into different subspaces. We implement the negative selection algorithm in the subspace. The randomly generated candidate detector only needs to be tolerated with selves in the subspace where the detector is located, not all the selves. This operation reduces the time cost of distance calculation. In the detection process of detectors, the antigen which is to be detected only needs to match the mature detectors in the subspace where the antigen is located, rather than all the detectors. This operation speeds up the antigen detection process. Theoretical analysis and experimental results show that the algorithm has better time efficiency and quality of detectors, saves sensor node resources and reduces the energy consumption, and is an effective algorithm for wireless sensor network intrusion detection.

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm.

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

Intelligent Differential Evolution based Feature Selection with Deep Neural Network for Intrusion Detection in Wireless Sensor Networks

Wireless sensor network (WSN) is mainly utilized for data gathering and surveillance applications. As WSN is majorly deployed in harsh and hostile environments, security remains a critical issue which needs to be resolved. An intrusion detection system (IDS) is one of the proficient ways used to determine the presence of abnormal behaviors (i.e. intrusions) in the network. Earlier studies have focused on the design of machine learning (ML) and deep learning (ML) models to design IDS. With this motivation, this paper presents an intelligent differential evolution based feature selection with deep neural network (IDEFS-DNN) for intrusion detection in WSN. The proposed IDEFS-DNN model aims to select optimum set of features and classify the intrusions in the network. In addition, the IDEFS-DNN technique involves the design of IDEFS technique to choose a subset of optimum features. Moreover, the chosen features are fed into the DNN technique for classification purposes. The usage of IDEFS technique helps to reduce the complexity and increase the classifier outcome. In order to portray the improved performance of the IDEFS-DNN technique, wide ranging experiments take place on benchmark datasets and the results are inspected under varying aspects. The simulation results ensured the enhanced intrusion detection performance of the IDEFS-DNN technique over the other IDS models.