Internet Of Things Datasets Research Articles

Beyond Performance Metrics: The Critical Role of Resource-Based Evaluation in Assessing IoT Attack Detectors

The proliferation of threats within the Internet of Things (IoT) environment is intensifying, largely due to the inherent limitations of this technology. The panoply of anti-threats based on artificial intelligence suffer from the complete embedment of models in limited resources. Tiny Machine Learning (TinyML) is presented as an opportunity in optimizing and selecting machine learning algorithms specifically tailored for intrusion detection systems (IDS) on limited-resource devices. This article addresses the challenges that must be overcome to enable the deployment of machine learning models on devices with constrained resources. In particular, it introduces additional indicators that could influence the algorithmic design of IoT models. Utilizing the PyCaret tool on the TON_IoT dataset, which encompasses nine distinct attacks, we developed and evaluated our approach for selecting the optimal algorithm from fourteen supervised learning models. The proposed tool, beyond the traditional six performance metrics, emphasizes resource consumption metrics, including memory, processor usage, battery life, and execution time – key considerations for TinyML in model refinement and selection. This study has identified less resource-intensive models suitable for developers in the design of IDS for IoT systems. We believe this research offers a foundational framework for the development of lightweight and efficient IoT vulnerability detection solutions.

Extraction of value and impact from IoT big data sets: A qualitative exploration of current methodologies and future directions

Integrating the Internet of Things (IoT) with big data analytics has created transformative opportunities across various domains, including smart cities, healthcare, and industrial automation. However, the challenges of extracting value from the vast and heterogeneous IoT data sets are significant. This study aims to explore methods for maximizing value extraction from IoT-generated big data and evaluate their impact on decision-making processes. A systematic literature review was conducted, and an exploratory qualitative methodology was employed to assess existing frameworks and propose improvements. The results highlight the importance of edge computing, machine learning algorithms, and data processing architectures in managing IoT data effectively. Additionally, the study identifies gaps in current research and suggests future directions to enhance the practical application of IoT big data analytics.

ClusFC‐IoT: A clustering‐based approach for data reduction in fog‐cloud‐enabled IoT

SummaryThe Internet of Things (IoT) is an ever‐expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog‐cloud‐enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC‐IoT, a novel two‐phase clustering‐based approach designed to optimize the management of IoT‐generated data. In the first phase, which is performed in the fog layer, we used the K‐means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K‐means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC‐IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC‐IoT very suitable for resource‐constrained IoT environments.

A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks

The exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times.

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things

ABSTRACT Nowadays, the network intrusion and cyberattack have emerged as the two main issues with Internet of Things (IoT) applications. The existing methods for preventing and detecting intrusions are limited in many ways, making it impossible to accurately identify any kind of attack occurring within network traffic. A number of machine learning-based methods that attains poor performance in the multiple class categorization accuracy are provided by the researchers. This research presents Data-Driven Intrusion Detection System in Internet of Things utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) to overcome these issues. The input data is taken from TON_IoT Dataset. The data balancing of the training dataset is enhanced using Class decomposition and synthetic minority class oversampling method (CDSMOTE). Then, the data is pre-processed using Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) of noise removal and data enhancement. The preprocessed output is given into feature extraction to extract features by using Dual-Tree Biquaternion Wavelet Transform (DTBWT). The extracted features are fed into Bayesian Regularization-Back Propagation Neural Network (BRBPNN) which detects the intrusion as Ransomware, Password attack, Scanning, Denial of Service (DoS), Distributed Denial of Service (DDoS), Data injection, Backdoor, Cross-Site Scripting (XSS), and Man-In-The-Middle (MITM). In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. The proposed DIDS-BRBPNN-BBWOA-IoT method is implemented using Python. The performance of the DIDS-BRBPNN-BBWOA-IoT approach is examined using performance metrics like accuracy, precision, recall, f1-score, specificity, error rate; computation time, and ROC. The proposed SAPVAEGAN-LCC-IR approach attains 18.44%, 26% ,and 29% greater accuracy; 26.55%, 24.12%, and 27.22% greater recall compared with existing MIDS-MIoT, AID-SDN-IoT, and IID-LW-IoT techniques.

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.

Enhancing IoT (Internet of Things) feature selection: A two-stage approach via an improved whale optimization algorithm

Feature selection is a critical task for optimizing system performance and reducing computational overhead in the context of Internet of Things (IoT) applications. This paper presents a two-stage feature selection approach specifically designed for IoT scenarios. In the first stage, a variety of feature dimensionality reduction techniques are employed to significantly reduce the dimensionality of the original feature set by more than 50%. This process results in the creation of a highly effective feature subset, which serves as a solid foundation for subsequent feature selection in the second stage. In the second stage, feature selection is performed on the feature subset by an evolutionary algorithm to obtain high accuracy. Notably, we propose an improved whale optimization algorithm (WOA-HA), which incorporates several improvement factors such as a chaotic Hénon map mechanism (HMM), adaptive coefficient vector (ACV), and a binary operator. To assess the effectiveness of our approach, we compare the performance of WOA-HA with other evolutionary algorithms in terms of feature selection outcomes. Through extensive experiments, our proposed approach achieves an average accuracy of up to 95.5% on Aalto IoT dataset and 98.8% on RT-IoT 2022 dataset, respectively. Meanwhile, the average number of selected features reduced by about 82.5% on Aalto IoT dataset and about 62.3% in the RT-IoT 2022 dataset, respectively. Our proposed approach consistently outperforms other methods and achieves the best performance on most datasets with higher accuracy and fewer features.

A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare

Strong cybersecurity solutions are becoming more and more important as Internet of Things (IoT) technology integration in healthcare settings develops. This study offers a method for feature extraction, selection, and attack classification by fusing the discriminative capacity of feedforward neural networks (FNNs) with the adaptability of fuzzy logic systems. In delicate healthcare database of IoT wearable devices, to reduce false alarm and guaranteeing intrusion detection dependability are the main priorities. The suggested method uses a feature extraction, selection technique, training and testing based on FNN, which allows the model to adjust to the dynamic and varied character of medical data. During the assessment stage, a dataset including a range of healthcare IoT scenarios, including different kinds of attacks, is used to train and evaluate the model, the ToN_IoT dataset was used. Fuzzy logic improves the system's resilience in identifying pertinent features by managing uncertainties and imprecise input. Fuzzy logic is one of the best technique for handling uncertainty, its linguistic representation and rule reasoning helps in better identification and classification. The findings indicate a noteworthy decrease in the frequency of false alarms when juxtaposed with conventional intrusion detection systems. Results obtained from the model are 99.2, 98.8, 99.5, 99.1 & 0.008 for accuracy, precision, recall, F1-Score and False alarm respectively. Promising outcomes in protecting IoT healthcare environments are demonstrated by the suggested system, opening the door to better patient data privacy and system resilience against cyberattacks.

COMPARATIVE ANALYSIS OF RANDOM FOREST AND ADABOOST LEARNING MODELS FOR THE CLASSIFICATION OF ATTACKS IN INTERNET OF THINGS

Attacks are actions that attempt to break one of the following properties of the computer system: confidentiality, integrity, and availability. The immense increment in the amount of internet applications and the appearance of modern networks has created the need for improved security mechanisms. Internet of Things (IoT) is a system that uses the Internet to facilitate communication between sensors and devices. Several approaches have been used to build attacks detection system in the past. This study built two ensemble models for the classification of attacks using Random Forest and Adaboost algorithms respectively. Feature importance was used for selecting promising attributes from the IoT intrusion dataset. Thereafter, the results of the classification models were evaluated and compared. The models were evaluated based on when feature selection technique was applied and without respectively. For Random Forest-based classification model with feature selection, 99.0% ,0.95,0.88,0.82, were obtained for accuracy, recall, f1-score, and precision respectively while without feature selection 69.0%,0.86,0.76,0.64 were obtained respectively. For Adaboost-based classification model with feature selection 99.0%.0.69,0.61,0.66 were obtained for accuracy, recall, f1-score and precision respectively. Without feature selection the Adaboost model recorded 58.0%,0.58,0.48,0.50 respectively. The results showed that both models achieved high rates with feature selection technique used, with Random Forest performing slightly better, both learning models showed promised performances in classifying attacks in IoT environments. This study concluded that the use of the chosen feature selection method helped improve the performances of the two ensembles in the classification of attacks in the IoT dataset.

Ensemble learning approach to enhancing binary classification in Intrusion Detection System for Internet of Things

The Internet of Things (IoT) has experienced significant growth and plays a crucial role in daily activities. However, along with its development, IoT is very vulnerable to attacks and raises concerns for users. The Intrusion Detection System (IDS) operates efficiently to detect and identify suspicious activities within the network. The primary source of attacks originates from external sources, specifi-cally from the internet attempting to transmit data to the host network. IDS can identify unknown attacks from network traffic and has become one of the most effective network security. Classification is used to distinguish between normal class and attacks in binary classification problem. As a result, there is a rise in the false positive rates and a decrease in the detection accuracy during the model's training. Based on the test results using the ensemble technique with the ensemble learning XGBoost and LightGBM algorithm, it can be concluded that both binary classification problems can be solved. The results using these ensemble learning algorithms on the ToN IoT Dataset, where binary classification has been performed by combining multiple devices into one, have demonstrated improved accuracy. Moreover, this ensemble approach ensures a more even distribution of accuracy across each device, surpassing the findings of previous research.

A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection

Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.

A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems

To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.

MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

The ever-expanding Internet of Things (IoT) landscape faces significant security challenges due to limitations in traffic monitoring, device heterogeneity, and weak security practices, leaving networks vulnerable to multi-target and coordinated attacks like large-scale botnets and Distributed Denial-of-Service (DDoS). Existing intrusion detection systems can be computationally expensive and resource-intensive. This can be problematic for resource-constrained IoT devices with limited processing power, memory, and battery life. This paper proposes MULTI-BLOCK, a novel, multi-module framework that leverages machine learning, stateful P4 processing, and a Software-Defined Networking (SDN)-based multi-controller architecture. MULTI-BLOCK tackles critical management tasks within IoT networks, including synchronized communication, traffic monitoring, intrusion detection, and attack mitigation. This comprehensive framework comprises four modules. The first, the proposed pyramidal conceptually decentralized multi-controller structure (PCDMCS), introduces Decentralized Control Interfaces (DCIs) for real-time threat identification through a Decentralized Warning Conduit (DWC). The second module provides comprehensive network monitoring using P4-enabled 24-state tables. The third module leverages 30 new P4-extracted/calculated features and the Enhanced Weighted Ensemble Algorithm (EWEA) for enhanced anomaly detection. The final module presents a novel mitigation approach with 22 steps distributed across multiple controllers for scalability. Extensive evaluation using established IoT datasets (X-IIoTID, TON_IoT, and Edge-IIoTset) and diverse test scenarios (including single-victim and multi-victim attacks) demonstrates MULTI-BLOCK's exceptional performance, achieving high accuracy (99.75 %), precision (99.32 %), F1-score (99.53 %), recall (99.67 %), specificity (99.60 %), low false positive rates (FPR) (0.40 %), and low Average Detection Time (ADT) (2.11 ms). By offering a robust and adaptable solution against evolving threats, MULTI-BLOCK represents a significant advancement in IoT network security.

Towards intrusion detection in IoT using Few-shot learning

The Internet of Things (IoT) is an emerging technology that covers various domains and has become an essential part of the upcoming technological revolution. IoT applications include healthcare, smart-cities, smart-cars, industries, quality of life, and several other fields. IoT typically consists of lightweight sensor devices that facilitate procedures such as automation, real-time trackable data collection, and data-driven decisions. However, securing IoT networks is an accessible research area for several reasons. The main security challenges are limited resources that are incapable of dealing with complex and advanced security tools; and lack of required data for training the security systems like Intrusion detection systems as a result of their heterogeneous nature. This research proposed a Few-shot learning IoT intrusion detection system model based on a Siamese network to overcome the above limitation. The model aims to classify and distinguish normal and attacked traffic. The experiment utilized an IoT dataset in different scenarios to analyze and validate the behavior with three categories with different numbers of data in each. The performance result achieves more than 99% accuracy and shows an efficient detection ability using only less than 1% of the dataset.

Clustering on heterogeneous IoT information network based on meta path.

As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

An Approach for Detecting Security Attacks using Machine Learning in IoT Environment

The strong security measures are becoming even more necessary to protect these networked systems as a result of the proliferation of Internet of Things (IoT) devices. The dynamic and varied nature of IoT networks frequently makes it impossible for traditional security solutions to be effective. The method suggested in this research uses machine learning to identify security attacks in IoT contexts. The suggested method makes use of the capabilities of machine learning algorithms to examine the enormous amounts of data produced by IoT devices. The system can develop the ability to recognise possible security threats and take immediate action by training models on labelled datasets that include both normal and attack patterns. In this paper multiple ML models for security threat detection in IoT environments in this study. Our evaluation uses both binary and multiclass classification models in an effort to accurately reflect the variety of assaults that can be found in IoT environments. The proposed method provide a new specialised IoT dataset that was created especially to reflect the characteristics of actual IoT environments in order to assure the validity of our assessment. By completing this extensive analysis, we hope to shed light on how well AI-based methods for security attack detection in IoT contexts work. The results of this study can help researchers and professionals decide which ML models and feature engineering techniques are best for IoT security. At the end of the day, we want to help with the creation of reliable security systems that safeguard IoT devices and shield user data from harmful attacks.

Anomaly detection in IoT-based healthcare: machine learning for enhanced security

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.

Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning

Internet of Things (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine learning approaches have been developed for network intrusion detection in IoT. These often use feature reduction techniques like feature selection or extraction before feeding data to models. This helps make detection efficient for real-time needs. This paper thoroughly compares feature extraction and selection for IoT network intrusion detection in machine learning-based attack classification framework. It looks at performance metrics like accuracy, f1-score, and runtime, etc. on the heterogenous IoT dataset named Network TON-IoT using binary and multiclass classification. Overall, feature extraction gives better detection performance than feature selection as the number of features is small. Moreover, extraction shows less feature reduction compared with that of selection, and is less sensitive to changes in the number of features. However, feature selection achieves less model training and inference time compared with its counterpart. Also, more space to improve the accuracy for selection than extraction when the number of features changes. This holds for both binary and multiclass classification. The study provides guidelines for selecting appropriate intrusion detection methods for particular scenarios. Before, the TON-IoT heterogeneous IoT dataset comparison and recommendations were overlooked. Overall, the research presents a thorough comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks.

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models

The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.

Trustworthy-Based Authentication Model with Intrusion Detection for IoT-Enabled Networks with Deep Learning Algorithm

In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications.