Intrusion Identification Research Articles

A Distributed Intrusion Detection Framework for Vehicular Ad Hoc Networks via Federated Learning and Blockchain

The emergence of connected vehicles via Vehicular Ad Hoc Networks (VANETs) has revolutionized transportation but has also brought forth challenges in security and privacy due to their open architecture. Early detection of intrusions within VANETs is paramount for ensuring safe communication. This research presents an intelligent distributed approach that leverages federated learning (FL) and blockchain for intrusion detection in VANETs. Through FL, various neural network models were implemented to distribute model training among vehicles, thus preserving privacy. Quantitative evaluation metrics demonstrate the effectiveness of the proposed framework. For example, compared to a traditionally trained Stochastic Gradient Descent (SGD) model, the Federated Trained Model achieved higher precision across various attack types, ranging from 68% to 94%, and consistently outperformed in terms of recall, with rates ranging from 57% to 88%. These results highlight FL's superiority in detecting intrusions, evidenced by gains in accuracy, recall, and precision. Integration of FL with blockchain further strengthened security and privacy protection, ensuring data integrity during collaborative FL training across decentralized nodes. This novel framework addresses VANET vulnerabilities by facilitating privacy-preserving, collaborative anomaly monitoring in a trustworthy manner. Evaluations validate the performance advantages of FL for intrusion identification, supporting wider adoption of vehicular technologies. The study underscores the potential of combining FL and blockchain to enable robust, cooperative abnormality recognition crucial for maintaining reliability, safety, and trust in VANET operations.

An optimized multi-layer ensemble model for airborne networks intrusion detection

In view of the characteristics of airborne networks, such as the traffic data of 1553B data bus and public networks, numerous redundant and irrelevant data, and fewer but more types of attack behaviors, a highly representative balanced data subset is generated by combining K-means with synthetic minority oversampling technique (SMOTE). A feature selection (FS) method of fast correlation-based filter combined with information gain (IG-FCBF) is proposed to filter the key characteristics with 90 % cumulative importance. Aiming at the problem that it is not easy for a single classifier to accurately classify various types of attacks, as well as the difficulty in obtaining the best prediction results by adjusting default and manual parameters, a multi-layer ensemble model based on Bayesian optimization tree-structured Parzen estimator (BO-TPE) is proposed for airborne networks intrusion detection, combining the advantages of supervised learning, stacking ensemble method and hyperparameter optimization (HPO). The experimental results show the superiority and effectiveness of the model, as well as its universality in Integrated Avionics Systems (IAS) and onboard public networks, which provides a new approach for airborne networks intrusion identification and protection.

Dynamically stabilized recurrent neural network optimized with intensified sand cat swarm optimization for intrusion detection in wireless sensor network

Wireless Sensor Networks (WSNs) are susceptible to various security threats owing to its deployment in hostile environments. Intrusion detection system (IDS) contributes a critical role on securing WSNs by identifying malevolent activities and ensuring data integrity. Traditional IDS techniques often struggle with the dynamic and resource-constrained nature of WSNs. In this paper, Dynamically Stabilized Recurrent Neural Network Optimized with Intensified Sand Cat Swarm Optimization for Wireless Sensor Network Intrusion identification (DSRNN-ISCOA-ID-WSN) is proposed. Initially, the input data is amassed from WSN-DS dataset. After that, the pre-processing segment receives the data. In pre-processing stage, redundant and biased records are removed from input data with the help of Adaptive multi-scale improved differential filter (AMSIDF). Then the optimal are selected by utilizing Wolf-Bird Optimization Algorithm (WBOA). DSRNN is used to classify the data as Normal, Grey hole, Black hole, Time division multiple access (TDMA), and Flooding attacks. Then Intensified Sand Cat Swarm Optimization (ISCOA) is employed to optimize the weight parameters of DSRNN for accuracte classification. The proposed DSRNN-ISCOA-ID-WSN technique is implemented Python. The performance of the proposed DSRNN-ISCOA-ID-WSN approach attains 29.24 %, 33.45 %, and 28.73 % high accuracy; 30.53 %, 27.64 %, and 26.25 % higher precision when compared with existing method such as Machine Learning-Powered Stochastic Gradient Descent Intrusions Detection System for WSN Attacks (SGDA-ID-WSN), An updated dataset to identify threats in WSN (CNN-ID-WSN) and Denial-of-Service attack detection in WSN: a Low-Complexity Machine Learning Model (DTA-ID-WSN) respectively.

Tree-based Machine Learning Ensembles and Feature Importance Approach for the Identification of Intrusions in UNR-IDD Dataset

Detection of intrusions from network data with the use of machine learning techniques has gained great attention in the past decades. One of the key problems in the network security domain is the availability of representative datasets for testing and evaluation purposes. Despite several efforts by researchers to release datasets that can be used for benchmarking attack detection models, some of the released datasets still suffer from one limitation or the other. Thus, some researchers at the University of Nevada released a dataset named UNR-IDD dataset which was argued to be free from some of the limitations of the past datasets. This study proposed Tree-based ensemble approaches for building binary intrusion identification models from the UNR-IDD dataset. Decision Tree algorithms are used as base classifiers in the Extra Trees, Random Forest and AdaBoost-based intrusion detection models. The results of the experimental analyses carried out indicated that the three ensembles performed excellently when feature selection was used compared to when all features were applied. For instance, Extra Trees model achieved an accuracy of 0.97, precision of 0.98, recall of 0.98 and f1-score of 0.98. Similarly, Random Forest model achieved an accuracy of 0.98, precision of 0.98, recall of 0.99 and f1-score of 0.98. Adaboost-based model had an accuracy of 0.96, precision of 0.96, recall of 0.99 and f1-score of 0.98. It was deduced that Random Forest intrusion classification model achieved slight overall best results when compared to the other models built. It is concluded that the three homogeneous ensemble models achieved very promising results while feature importance was used as attribute selection method.

Network intrusion detection: An optimized deep learning approach using big data analytics

Managing enormous amounts of data, such as big data, and detecting network traffic intrusions are inefficiently handled by current computing technologies. Traditional analytical techniques cannot manage the incursions in continuous internet traffic and the enormous log data of server activity, leading to many inaccurate results and a prolonged training period. As a result, this research provides an efficient deep learning-based approach to enhance the attack identification task by addressing the basic big data complexity linked to many heterogeneous security data types. This framework employs a novel feature selection method incorporatingthe Aquila Optimizer (AO) and Fuzzy Entropy Mutual Information (FEMI) algorithms to pick distinctive characteristics. Subsequently, a modified canonical correlation-based technique is applied to combine selected characteristics. Then, the intrusion identification and categorization are carried out using the optimized ResNet152V2 method. Additionally, data augmentation using Auxiliary Classifier Generative Adversarial Network (ACGAN) is performed. Finally, we used the CICDDoS2019 and ToN-IoT datasets to validate the suggested methodology. By comparing the presented approach to several baseline methods, the effectiveness of the suggested methodology is assessed using various performance measures, including F1-score, recall, precision, accuracy, confusion matrix, and ROC curve. Finally, simulation results show that the suggested strategy is superior to other existing techniques and demonstrate that it is a resilient solution for network intrusion detection.

Intrusion detection system based on the beetle swarm optimization and K‐RMS clustering algorithm

SummaryIntrusion detection is a cyber‐security method that is significant for network security. It is utilized to detect behaviors that compromise security and privacy within a network or in the context of a computer system. To enhance the identification, an Intrusion Detection System Based on the Beetle Swarm Optimization and K‐RMS Clustering Algorithm cluster‐based hybrid classifiers is proposed in this manuscript. Here, the data is amassed from CICIDS2017 dataset. Then the data is preprocessed to eradicate the unwanted noise. After completing the preprocessed data, it can be clustered by using K‐RMS clustering algorithm. This algorithm cluster the entire data to the associated cluster set depending on the data behavior. The classification algorithm is considered to predict the data as normal or attacking behaviors. The hybrid classification is used to predict the data. The solitary predictor aims to achieve high detection rates and accuracy. The hybrid classifiers, such as support vector machines, artificial neural networks are applied to recognize the normal or intruder. The performance of the SVM‐ANN‐IDS method attains 22.05%, 15.87%, 27.25% higher accuracy, 23.90% and 28.53% higher precision, 29.29%, 19.19% and 23.27% higher specificity and 18.28%, 24.36% and 27.49% greater recall when compared to the existing models, like developing novel deep‐learning model to improve network intrusion categorization (DNN‐IDS), Intrusion identification scheme on real‐time data traffic under machine learning techniques along feature selection method (RNN‐SVM‐IDS) and recurrent deep learning basis feature fusion ensemble meta‐classifier for intellectual network intrusion identification scheme (RNN‐IDS) respectively.

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Human intrusion detection for high-speed railway perimeter under all-weather condition

PurposeThe paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.Design/methodology/approachThis paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.FindingsSimulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.Originality/value(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs

Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real-world applications such as intrusion identification in network systems, detection of ecosystem disturbances, and detection of epidemic outbreaks. In this article, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: (i) how to compare graph snapshots across time, (ii) how to capture temporal dependencies, and (iii) how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short-term and long-term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that (i) LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, (ii) MultiLAD’s advantage over contenders significantly increases when additional views are available, and (iii) MultiLAD is highly robust to noise from individual views. In five real-world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.

A Learning Approach for The Identification of Network Intrusions Based on Ensemble XGBoost Classifier

The limitation of signature-based Intrusion Detection systems has given rise for the popularity of Machine learning (ML) approaches r for building such intrusion detection systems (IDSs). ML is a sub-filed of Artificial Intelligence that enables algorithms to learn from data and its applications have been widely accepted and used in many domains. To achieve a promising ML-based model that can identify attacks and intrusions in networks and the cyber space, different stages of machine learning approach like pre-processing, attribute selection, model building, hyper parameter tuning can be very important. CICIDS2017 intrusion dataset was used for all the experimentations. This study focuses on building cyber threat detection model based on the ensemble feature selection and classification method. Innovative approaches were used for the analysis and pre-processing of the dataset. Thereafter, XGboost algorithm was used for selecting relevant features from the default input attributes in each of the captures. Thereafter, the reduced features were employed in the identification of cyber intrusions. The average accuracy achieved in the 8 captures of the dataset is 98% while precision is 0.98. Also, recall is 0.98, f1-score is 0.98 while AUC ROC score is 0.99. The study concluded that XGBoost-based model was able to achieve promising results based on the proper dataset encoding, feature importance-based feature selection and tuning of the algorithm for intrusion identification.

Efficient Ensemble-based Phishing Website Classification Models using Feature Importance Attribute Selection and Hyper parameter Tuning Approaches

The internet is now a common place for different business, scientific and educational activities. However, there are bad elements in the internet space that keep using different attack techniques to perpetrate evils. Among these categories are people who use phishing techniques to launch attacks in the enterprise networks and internet space. The use of machine learning (ML) approaches for phishing attacks classification is an active research area in the field of cyber security. This is because phishing attack detection is a good example of intrusion identification tasks. These machine learning techniques can be categorized as single and ensemble learners. Ensemble learners have been identified to be more promising than the single classifiers. However, some of the ways to achieve an improved ML-based detection models are through feature selection/dimensionality reduction as well as hyper parameter tuning. This study focuses on the classification of phishing websites using ensemble learning algorithms. Random Forest (RF) and Extra Trees ensembles were used for the phishing classification. The models built from the algorithms are optimized by applying a feature importance attribute selection and hyper parameter tuning approaches. The RF-based phishing classification model achieved 99.3% accuracy, 0.996 recall, 0.983 f1-score, 0.996 precision and 1.000 as AUC score. Similarly, Extra Trees-based model attained 99.1% accuracy, 0.990 as recall, F1-score was 0.981, precision of 0.990 while AUC score is 1.000. Thus, the RF-based phishing classification model slightly achieved better classification results when compared with the Extra Trees own. The study concluded that attribute selection and hyper parameter tuning approaches employed are very promising.

Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET

Designing of intrusion detection system (IDS), and mobile ad hoc networks (MANET) prevention technique with examined detection rate, memory consumption with minimum overhead are vital concerns. Node mobility and node energy are the two optimization problems in MANETs wherein nodes travel uncertainly in any direction, evolving in a continuous change of topology. With the proposed approach, a Centrality Coati Optimization Algorithm based Cluster Gradient for multi attack intrusion identification is devised. This study focuses on the problems of node mobility and energy to develop a clustering algorithm for cluster head selection in MANET that is incited by Dual Network Centrality. Compact cluster formation is carried out by Coati Optimization Algorithm (COA). The Multi-head Self-Attention based Gated Graph Convolutional Network (MSA-GCNN) with a hybrid type of IDS recognizes several attacks, including DoS and Zero-Day attacks. The proposed technique is implemented in NS-2 network simulator. The performance of proposed approach is examined under some parameters, like attack detection rate, memory consumption, computational time for detecting the intruder. The outcomes display that the proposed technique decreases the IDS traffic and entire consumption of memory and sustains a higher attack identification rate with less computational time. The proposed technique attains 4.299 %, 10.375 % and 6.935 % Accuracy, 5.262 %, 8.375 % and 7.945 % Precision, 7.282 %, 10.365 % and 5.935 % Recall, 9.272 %, 5.355 % and 8.965 % ROC is higher compared with the existing methods such as, Epsilon Swarm Optimized Cluster Gradient along deep belief classifier for multiple attack intrusion detection (ESOC-MA-ID-MANET), Intrusion Detection secure solution for intrusion detection in cloud computing utilizing hybrid deep learning approach called EOS-IDS and improved heap optimization (IHO-MA-ID-MANET) for induction detection technique respectively.

Traffic Load Learning Towards Early Detection of Intrusion in Industrial mMTC Networks

The fifth generation (5G) network offers a powerful infrastructure for Industrial IoT in that its support on massive machine-type communications (mMTC). Recently, grant-free (GF) access has been recognized as the promising new access approach for mMTC, however, it also introduces the potential risk. To timely discover intrusion, in this paper we propose a learning network by extracting traffic-load information from the states (success, collision, and idle) of access resources observed at media access control (MAC) and physical (PHY) layers. In particular, our learning network consists of three concatenated function modules, i.e., traffic-load estimation, traffic-load prediction, and intrusion identification. Moreover, our proposed learning network is able to identify two types of intrusion: false data dissemination and congestion attack. Simulation results indicate that the proposed scheme can effectively capture the number of active devices, provide reasonable prediction by using history records, and eventually, achieve more accurate detection compared with baseline approaches.

Feature Selection and Dynamic Network Traffic Congestion Classification based on Machine Learning for Internet of Things

The network traffic congestion classifier is essential for network monitoring systems. Network traffic characterization is a methodology to classify traffic into several classes supporting various attributes. In this paper, payload-based classification is suggested for network traffic characterization. It has a broad scope of utilization like network security assessment, intrusion identification, QoS supplier, et cetera; furthermore, it has significance in investigating different suspicious movements in the network. Numerous supervised classification techniques like Support Vector Machines and unsupervised clustering methods like K-Means connected are used in traffic classification. In current network conditions, minimal supervised data and unfamiliar applications influence the usual classification procedure's performance. This paper implements a methodology for network traffic classification using clustering, feature extraction, and variety for the Internet of Things (IoT). Further, K-Means is used for network traffic clustering datasets, and feature extraction is performed on grouped information. KNN, Naïve Bayes, and Decision Tree classification methods classify network traffic because of extracted features, which presents a performance measurement between these classification algorithms. The results discuss the best machine learning algorithm for network congestion classification. According to the outcome, clustering (k-means) with network classification (Decision Tree) generates a higher accuracy, 86.45 %, than other clustering and network classification

BBBC-DDRL: A hybrid big-bang big-crunch optimization and deliberated deep reinforced learning mechanisms for cyber-attack detection

Currently, the wireless networks are increasingly used in all kinds of applications due to their adaptability, flexibility, and easy-access. Without a predetermined architecture, wireless sensor networks are often deployed and unmanaged. Due to these traits, the wireless networks are more vulnerable to attacks, and an opponent can easily track traffic because the data are transmitted over the air. In the existing studies, a different types of attack detection methodologies are used for intrusion identification and classification. But, they having problems in terms of inaccurate prediction, high false positives, and lack of reliability. Thus, the proposed research work intends to implement a sophisticated and novel cyber-security model for safeguarding wireless networks. Here, the Graph Referencing Normalization (GRN) algorithm is specifically used to preprocess the datasets based on the normalization vector. Then, the cutting-edge Big-Bang Big-Crunch (BBBC) optimization technique is employed to obtain the relevant features from the normalized data for analyzing the characteristics of attacks. The Deliberate Deep Reinforced Learning (DDRL) classification process is used to predict the normal and attacking data based on the features. By using the GRN, BBBC and DDRL mechanisms, the intrusions are accurately predicted from the cyber-datasets. To assess the performance of this model, the Matlab simulation tool has been used, where the results are validated with the use of different parameters like precision, recall, f1-score, and etc. The estimated results indicate the average attack detection rate is improved up to 99% for all datasets, when compared to the other existing security models.

Flow Topology-Based Graph Convolutional Network for Intrusion Detection in Label-Limited IoT Networks

Given the distributed nature of the massively connected “Things” in IoT, IoT networks have been a primary target for cyberattacks. Although machine learning based network intrusion detection systems (NIDS) can effectively detect abnormal network traffic behaviors, most existing approaches are based on a large amount of labeled traffic flow data, which hinders their implementation in the highly dynamic IoT networks with limited labeling. In this paper, we develop a novel Flow Topology based Graph Convolutional Network (FT-GCN) approach for label-limited IoT network intrusion detection. Our main idea is to leverage the underlying traffic flow patterns, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i.e.$ </tex-math></inline-formula> , the flow topological structure, to unlock the full potential of the traffic flow data with limited labeling, where the FT-GCN will be deployed at the edge servers in IoT networks to detect intrusions via software defined network technologies. Specifically, FT-GCN first takes the time correlation of traffic flows into account to construct an interval-constrained traffic graph (ICTG). Besides, a Node-Level Spatial (NLS) attention mechanism is designed to further enhance the key statistical features of traffic flows in ICTG. Finally, the combined representation of statistical flow features and flow topological structure are learned by the cost-effective Topology Adaptive Graph Convolutional Networks (TAGCN) for intrusion identification in IoT networks. Extensive experiments are conducted on three real-world datasets, which demonstrate the effectiveness of the proposed FT-GCN compared to state-of-the-art approaches.

Research on Intrusion Identification of Hazardous Construction Areas Based on Machine Vision

The complex environment of power construction sites is prone to frequent accidents and many dangerous areas. Using advanced artificial intelligence technology to monitor these dangerous areas in real-time is the primary measure to improve construction safety. Given the problems of poor real-time monitoring and the low accuracy of traditional methods, this paper proposes a real-time monitoring method for dangerous areas based on YOLOv5 deep learning, and through the trained YOLOv5, the construction site is monitored in real-time. Timely warnings are issued for those entering dangerous area to avoid accidents. Verified by the actual data, the method is timely and effective with high identification accuracy.

Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.

Detection of Intrusion behavior in cloud applications using Pearson's chi-squared distribution and decision tree classifiers

• The main objective is to identify unauthorised fog nodes in cloud applications. • The optimal feature selection for security is perfomed by distribution algorithm. • The Decsion tree classifiers are used for intruded fog nodes isolation The information transmission in machine learning is a challengable task which must be performed productively in cloud based fog applications. There is a few methodologies has been pronounced for the issue of information transmission in cloudbased machines, which utilizes a few techniques and components in the choice of portable sensor hubs which has information put away. However, not just the decision could work on the presentation of information transmission, and there are such countless elements which influence information transmission in a roundabout way. The proposed technique keeps up with the rundown of versatile fog nodes and the follow about their information accessibility, unwavering quality, and the last time window information assortment performed number of supporting hub accessible in wakeup mode. In light of all the above factors the strategy registers the proficient information transmission for every one of the area considered, and at every locale, the technique computes the information accessibility measure for various information hubs to choose them for information assortment. At first utilized Pearson's Chi-Squared Distribution for choosing the ideal highlights from dataset. Then, at that point, human-in the loop experiment is proposed for arranging the hubs in an organization as typical or strange which brings about further developed intrusion discovery rate and time. The recreation of human-in-the loop experiment is directed on parameters, such as, intrusion detection rate, Data transmission proportion, intrusion identification time and prediction rate. The evaluated result shows that the proposed work performs better compared to different strategies.

An intrusion identification and prevention for cloud computing: From the perspective of deep learning

As the Internet industry has connected the globe using advancements in computer network from LAN to Cloud Infrastructure, Fashions and Developments in Internet of Things (IoT), Cloud Infrastructures, E-Commerce, Banking and Healthcare are allowing unimaginable access to devastating attacks.Intrusion detection is a critical component of information security, and the essential technique is the ability to properly recognise diverse network assaults. In this article, the methodology of an intrusion detection scheme utilising deep learning technique which is based on Fuzzy Min Max Neural Networks-Based Intrusion Detection System (FMMNN-IDS) has been proposed. The fuzzy min-max learning algorithm, an expansion-contraction method that can learn nonlinear class boundaries in a single pass through the data and gives the capacity to incorporate new and improve current classes without retraining, is used to identify the min-max points.Furthermore, the article investigated the model's performance in terms of binary and multiclass classification, as well as the count of learning rate and neurons affect the model's performance. The performance of FMMNN-IDS is compared with state-of-art approaches determined in the literatures. The FMMNN-IDS model increases accuracy rate of intrusion detection, achieves better intrusion detection rate and reduced error rate.