Internet Of Things Intrusion Research Articles

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

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.

HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection

The continuous strengthening of the security posture of Internet of Things (IoT) ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. Using Machine Learning (ML) capabilities to defend against IoT cyber attacks has many potential benefits. However, the currently proposed frameworks do not consider data privacy, secure architectures, and scalable deployments of IoT ecosystems. This paper proposes a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. We highlight and demonstrate the importance of sharing cyber threat intelligence among inter-organisational IoT networks to improve the model’s detection capabilities. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The transactions (model updates) and processes will run on a secure blockchain, and the smart contract will verify the conformance of executed tasks. We have tested our solution and demonstrated its feasibility by implementing it and evaluating the intrusion detection performance using a key IoT data set. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.

A feature selection method based on auto-encoder for internet of things intrusion detection

<p><span>The evolution in gadgets where various devices have become connected to the internet such as sensors, cameras, smartphones, and others, has led to the emergence of internet-of-things (IoT). As any network, security is the main issue facing IoT. Several studies addressed the intrusion detection task in IoT. The majority of these studies utilized different statistical and bio-inspired feature selection techniques. Deep learning is a family of techniques that demonstrated remarkable performance in the field of classification. The emergence of deep learning techniques has led to configure new neural network architectures that is designed for the feature selection task. This study proposes a deep learning architecture known as auto-encoder (AE) for the task of feature selection in IoT intrusion detection. A benchmark dataset for IoT intrusions has been considered in the experiments. The proposed AE has been carried out for the feature selection task along with a simple neural network (NN) architecture for the classification task. Experimental results showed that the proposed AE showed an accuracy of 99.97% with a false alarm rate (FAR) of 1.0. The comparison against the state of the art proves the efficacy of AE.</span></p>

A Review on Deep Learning Techniques for IoT Data

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method

The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86) using the intersection and union, respectively, on IoTID20 and resulted 15 and 25 relevant features (out of 41) using the intersection and union, respectively, on NSL-KDD. We have further compared our approach with other state-of-the-art studies. The comparison reveals that our model is superior and competent, scoring a very high 99.98% classification accuracy.

On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples

Internet of things (IoT) security is a prerequisite for the rapid development of the IoT to enhance human well-being. Machine learning-based intrusion detection systems (IDS) have good protection capabilities. However, it is difficult to identify attack information in massive amounts of data, which leads to inefficient model detection when faced with insufficient samples for certain types of attacks. In this regard, this paper fuses deep learning methods and statistical ideas to address the problem of minority samples attack detection, and proposes an intrusion detection method for the IoT based on Improved Conditional Variational Autoencoder (ICVAE) and Borderline Synthetic Minority Oversampling Technique (BSM), called ICVAE-BSM. By introducing an auxiliary network into the Conditional Variational Autoencoder (CVAE) to adjust the output probability distribution of the encoder, learning the posterior distribution of different classes of samples, so that the distributions of samples of the same class are concentrated, and the distributions of different classes of samples are scattered in the latent space; then based on BSM, adaptively synthesize the edge latent variables in the latent space of ICVAE, and feed the new synthetic edge latent variables to the ICVAE’s decoder to generate representative new samples to balance the data set. The output of the encoder is connected to the Softmax classifier at last, and the original samples are mixed with the generated samples to fine-tune it to enhance its generalization ability for intrusion detection of minority samples. We use the NSL-KDD data set, CIC-IDS2017 data set and CSE-CIC-IDS2018 data set to simulate and evaluate the model, the experimental results show that the proposed method can more effectively improve the accuracy of IoT attack detection under the condition of unbalanced samples.

FGMD: A robust detector against adversarial attacks in the IoT network

Since network intrusion detectors for the Internet of Things (IoT) increasingly rely on machine learning models, attacks against these detectors are also escalating. Machine learning-based detectors are vulnerable to adversarial attacks. Existing solutions for adversarial attacks against IoT networks are rare and have certain limitations. To address this problem, we first design an adversarial samples generation algorithm to generate adversarial samples and evaluate the performance of the IoT network intrusion detector. Then we propose a novel framework FGMD (Feature Grouping and Multi-model fusion Detector) that can defend against adversarial attacks through feature grouping and multi-model fusion. We evaluate our framework on two public datasets and compare it with existing works. The experimental results demonstrate the effectiveness of the FGMD against adversarial attacks. Meanwhile, the performance of the FGMD does not degrade in the absence of adversarial samples. To the best of our knowledge, in field of IoT intrusion detection, the detection of adversarial attacks against machine learning-based detectors has not been widely studied yet. Our proposal represents a valuable contribution to the IoT network intrusion detection.

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection.

The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.

Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and attacks that cause security and privacy issues, existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, the IoT intrusion threat has been analyzed using the sparse convolute network to contest the threats and attacks. The web is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly, Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. Effective utilization of training patterns in the network successfully classifies the standard and threat patterns. Then, the effectiveness of the system is evaluated using experimental results and discussion. Network intrusion detection systems are superior to other types of traditional network defense in providing network security. The research applied an IGA-BP network to combat the growing challenge of Internet security in the big data era, using an autoencoder network model and an improved genetic algorithm to detect intrusions. MATLAB built it, which ensures a 98.98% detection rate and 99.29% accuracy with minimal processing complexity, and the performance ratio is 90.26%. A meta-heuristic optimizer was used in the future to increase the system’s ability to forecast attacks.

Feature Entropy Estimation (FEE) for Malicious IoT Traffic and Detection Using Machine Learning

Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for IoT security. Tracking and blocking unwanted traffic flows in the IoT network is required to design a framework for the identification of attacks more accurately, quickly, and with less complexity. Many machine learning (ML) algorithms proved their efficiency to detect intrusion in IoT networks. But this ML algorithm suffers many misclassification problems due to inappropriate and irrelevant feature size. In this paper, an in-depth study is presented to address such issues. We have presented lightweight low-cost feature selection IoT intrusion detection techniques with low complexity and high accuracy due to their low computational time. A novel feature selection technique was proposed with the integration of rank-based chi-square, Pearson correlation, and score correlation to extract relevant features out of all available features from the dataset. Then, feature entropy estimation was applied to validate the relationship among all extracted features to identify malicious traffic in IoT networks. Finally, an extreme gradient ensemble boosting approach was used to classify the features in relevant attack types. The simulation is performed on three datasets, i.e., NSL-KDD, USNW-NB15, and CCIDS2017, and results are presented on different test sets. It was observed that on the NSL-KDD dataset, accuracy was approx. 97.48%. Similarly, the accuracy of USNW-NB15 and CCIDS2017 was approx. 99.96% and 99.93%, respectively. Along with that, state-of-the-art comparison is also presented with existing techniques.

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System.

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

Performance evaluation of the SRE and SBPG components of the IoT hardware platform security advisor framework

The applications of Internet of Things (IoT) and associated technologies have been spreading rapidly across a wide range of domains, including environmental monitoring, home automation, and supply chain, having a significant bearing on the social and economic well-being of humans as well as enhancing environmental sustainability. In recent years, however, there have been several data breaches and other security and privacy incidents involving IoT devices, which have attracted significant attention from the research community in both academia and industry. This has resulted in a surge of proposals put forward by many researchers, including IoT blockchain-based security solutions, IoT intrusion detection systems, IoT authentication systems, and IoT security analytics. While these proposals are aimed at addressing various IoT security and privacy-related issues, many of these solutions arguably seem not to focus on helping designers and developers with little or no security expertise in start-up companies to produce secure IoT systems. To this end, the IoT Hardware Platform Security Advisor (IoT-HarPSecA) framework was proposed to foster the design and implementation of secure IoT systems. In this paper, we present the performance and usability evaluation of the Security Requirements Elicitation (SRE) and Security Best Practice Guidelines (SBPG) component tools of IoT-HarPSecA, which are two of the three component tools of the security framework. Results show that the two components of the IoT-HarPSecA framework can facilitate the development of secure IoT systems and that the SRE and SBPG tools are easy to use.

Exact greedy algorithm based split finding approach for intrusion detection in fog-enabled IoT environment

The essential role of intrusion detection is to manage the critical infrastructure to detect malicious activity competently concerning the Internet of Things (IoT). The IoT network is used to communicate and control information among various components composing a critical system. The essential inclination of infrastructure swerves to confront security issues and challenges as network systems are examined to expose cyber-related threats. Additionally, accessing real-time information from the cloud leads to a massive problem of latency. Fog computing is a novel archetype to encompass the cloud to the network edge with practical computation and critical infrastructure. The fog layer makes the device vulnerable to numerous attacks because of rapid access to resources. The practical way of addressing these issues is to detect intrusions effectively and trace the path leading to the attack source. The intent of this paper is to present a security mechanism and assure truthful operation of IoT networks with the intrusion detection system. A network intrusion detection system is proposed based on the conception of the Exact Greedy Boosting ensemble method for device implementation in the fog node because of protecting critical infrastructure from timely and accurate detection of malicious activities. The proposed model explores the traffic flow monitoring in novel IoT Intrusion Dataset 2020(IoTID20) network traffic by identifying and classifying the type of attack based on anomalies from normal behavior. Further, the paper estimates the complete experimentation performance and evaluations with competitive machine learning algorithms. The experimental observation of the simulation work is evident in the proposed model's efficiency and robustness in categorizing the attacks.

A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks.

WITHDRAWN: Improved security using machine learning for IoT intrusion detection system

Abstract Machine Learning supports numerous fields for better service on its outcomes. Internet of Things plays vital role in the digital era to collect and transfer data over the network without any human interaction to connect the world in a single system. In the aspect of security and privacy patterns, machine learning has the capability to make optimistic interference on mischievous network connection of IoT (Internet of Things) system. In this paper to focus on security challenges for the intrusion detection in the IoT system. To analysis various attacks for intrusion detection system on types, effects and surface attack over IoT network. And main focus on machine learning classification algorithm implemented on network of IoT system to improve the efficiency on identifying the attacks.

Augmented whale feature selection for IoT attacks: Structure, analysis and applications

Smart connected appliances expand the boundaries of the conventional Internet into the new Internet of Things (IoT). IoT started to hold a significant role in our life, and in several fields as in transportation, industry, smart homes, and cities. However, one of the critical issues is how to protect IoT environments and prevent intrusions. Attacks detection systems aim to identify malicious patterns and threats that cannot be detected by traditional security countermeasures. In literature, feature selection or dimensionality reduction has been profoundly studied and applied to the design of intrusion detection systems. In this paper, we present a novel wrapper feature selection approach based on augmented Whale Optimization Algorithm (WOA), which adopted in the context of IoT attacks detection and handles the high dimensionality of the problem. In our approach, we introduce the use of both V-shaped and S-shaped transfer functions into WOA and compare the superior variant with other well-known evolutionary optimizers. The experiments are conducted using N-BaIoT dataset; wherein, five datasets were sampled from the original dataset. The dataset represents real IoT traffic, which is drawn from the UCI repository. The experimental results show that WOA based on V-shaped transfer function combined with elitist tournament binarization method is superior over S-shaped transfer function and outperforms other well-regarded evolutionary optimizers based on the obtained average accuracy, fitness, number of features, running time and convergence curves. Hence, we can conclude that the proposed approach can be deployed in IoT intrusion detection systems.

Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks

Open wireless sensor networks (WSNs) in Internet of things (IoT) has led to many zero-day security attacks. Since intrusion detection is a key security solution, this paper presents a lightweight machine learning-based intrusion detection technique with high performance for resource limited IoT wireless networks namely, IoT intrusion detection system (IoTIDS). IoTIDS is based on hybridization of genetic algorithm (GA) and grey wolf optimizer (GWO), termed as GA–GWO. The main aim of the hybrid algorithm for the IoTIDS is to reduce the dimensionality of the huge wireless network traffic through intelligent selecting the most informative traffic features. By hybridizing, we try to eliminate their weaknesses through GA and GWO strengths. The effectiveness of the GA–GWO on IoTIDS is evaluated using AWID (aegean wi-fi intrusion dataset) as a new real-world wireless intrusion dataset, after preprocessing it under different scenarios. The experimental results proved that the proposed GA–GWO individually not only improved the performance of the IoTIDS in terms of computational costs, but it also enabled the IoTIDS to detect ? with high accuracy and low false alarm rate. Furthermore, GA–GWO in comparison to the original GA and GWO and other recent existing methods like FS, weight, and parameter optimization of SVM based on the GA (FWP-SVM-GA) and binary GWO (BGWO) has proven to be more effective.

DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System

Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.

Deep recurrent neural network for IoT intrusion detection system

As a results of the large scale development of the Internet of Things (IoT), cloud computing capabilities including networking, data storage, management, and analytics are brought very close to the edge of networks forming Fog computing and enhancing transferring and processing of tremendous amount of data. As the Internet becomes more deeply integrated into our business operations through IoT platform, the desire for reliable and efficient connections increases as well. Fog and Cloud security is a topical issue associated with every data storage, managing or processing paradigm. Attacks once occurred, have ineradicable and disastrous effects on the development of IoT, Fog, Cloud computing. Therefore, many security systems/models have been proposed and/or implemented for the sake of Fog security. Intrusion detection systems are one of the premier choices especially ones that designed using artificial intelligence. In our paper, we presented an artificially full-automated intrusion detection system for Fog security against cyber-attacks. The proposed model uses multi-layered of recurrent neural networks designed to be implemented for Fog computing security that is very close to the end-users and IoT devices. We demonstrated our proposed model using a balanced version of the challenging dataset: NSL-KDD. The performance of our model was measured using a variety of typical metrics, and we add two additional metrics: Mathew correlation and Cohen's Kappa coefficients for deeper insight. where the experimental results and simulations proved the stability and robustness of the proposed model in terms of a variety of performance metrics.

AI-based Intrusion Detection System for Internet of Things (IoT) Networks

The rise of the Internet of Things has brought about various advantages, such as providing us with more efficient and effortless activities. Unfortunately, the lack of security solutions has also led to the development of new threats. One of these is the exploitation of vulnerabilities in the networks of IoT devices. In order to effectively address the security threats that can arise in the networks of IoT devices, there needs to be an effective intrusion detection system (IDS). In the field of security, the use of artificial intelligence (AI) powered IDS has shown promising promise. Through deep learning and machine learning techniques, these systems can learn and adapt quickly to new threats. This paper presents an evaluation of the performance of an AI-based security system on a large dataset. The research begins with a literature review of the previous studies related to the security of IoT devices and intrusion detection. We then develop a methodology that includes the data collected for evaluation and training, an AI model architecture for intrusion detection, and the evaluation metrics. The paper presents the results of the study and discusses the performance of the AI-based IDS compared to the existing solutions for addressing security threats in Internet of Things networks. It also explores the potential of this technology for future research. The findings of this study contribute to the growing body of research on the security of IoT networks and intrusion detection. It shows that an AI-based IDS can perform better than the existing solutions in identifying and mitigating threats. The study's findings show the potential of deep learning and machine learning techniques to enhance the security of IoT networks. It also highlights the scope of this technology's application in other security domains.