Secure Internet Of Things Systems Research Articles

Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.

With the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024. From an initial pool of 4388 studies, selected articles underwent thorough quality assessment with weighted critical appraisal questions and a defined inclusion threshold. This study represents the first comprehensive systematic review to investigate the potential of microservices in IoT, with a particular focus on security aspects. The review explores the merits of MSA, highlighting twelve benefits, eight key challenges, and eight security risks. Additionally, the eight best practices for implementing MSA in IoT systems are extracted. The findings underscore MSA's utility in fortifying IoT security while also acknowledging complexities and potential vulnerabilities. Moreover, the study calls attention to the importance of incorporating complementary technologies including blockchain and machine learning to address identified gaps effectively. Finally, we propose a taxonomic classification for Microservice-based IoT security patterns, facilitating the categorization and organization of security measures in this context. Such a review can help researchers and practitioners identify existing gaps, highlight potential research directions, and provide guidelines for designing secure and efficient microservice-based IoT systems.

IHBOT: An Intelligent and Hybrid Model for Investigation and Classification of IoT Botnet

The Internet of Things (IoT) is revolutionizing the technological market with exponential growth year wise. This revolution of IoT applications has also brought hackers and malware to gain remote access to IoT devices. The security of IoT systems has become more critical for consumers and businesses because of their inherent heterogenous design and open interfaces. Since the release of Mirai in 2016, IoT malware has gained an exponential growth rate. As IoT system and their infrastructure have become critical resources that triggers IoT malware injected by various shareholders in different settings. The enormous applications cause flooding of insecure packets and commands that fueled threats for IoT applications. IoT botnet is one of the most critical malwares that keeps evolving with the network traffic and may harm the privacy of IoT devices. In this work, we presented several sets of malware analysis mechanisms to understand the behavior of IoT malware. We devise an intelligent and hybrid model (IHBOT) that integrates the malware analysis and distinct machine learning algorithms for the identification and classification of the different IoT malware family based on network traffic. The clustering mechanism is also integrated with the proposed model for the identification of malware families based on similarity index. We have also applied YARA rules for the mitigation of IoT botnet traffic.

An efficient intrusion detection system for IoT security using CNN decision forest.

The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.

IoTSLE: Securing IoT systems in low-light environments through finite automata, deep learning and DNA computing based image steganographic model

The Internet of Things (IoT) is a vast network of interconnected devices and systems, including wearables, smart home appliances, industrial machinery, and vehicles, equipped with sensors and connectivity. The data collected by the IoT devices are transmitted over a network, for processing and analyzing that data, so that appropriate actions can be initiated. Security of IoT systems is a major concern, as IoT devices collect and transmit crucial information. But images captured in low-light environments pose a challenge for IoT security by limiting the ability to accurately identify objects and people, increasing the risk of spoofing, and hindering forensic analysis. This paper unfolds a novel framework for IoT security using Steganography in Low-light Environment (IoTSLE) by image enhancement and data concealment. In proposed IoTSLE, initially, the low-light images, captured by the IoT devices in a low-light environment, are enhanced by band learning with recursion and band recomposition. After that, the secret information is concealed within the enhanced image. This concealment is supervised by using a specially designed finite automata for genome sequence encoding and 2-2-2 embedding. The proposed steganography technique is capable of hiding secret information within a 512 × 512 RGB image with the payload of 2097152 bits. The experiments like, PSNR, SSIM, Q-Index, BER, NCC, and NAE etc. are conducted to analyze the imperceptibility and security of IoTSLE. The proposed IoTSLE is useful for various IoT systems in different private and government fields like, defense agencies, digital forensics, agriculture, healthcare industry, cybersecurity firms, smart home, smart city etc.

Application of Big Data and Quantum Computing in the Secure Federated Internet of Things

Federated Internet of Things (IoT) presents both unprecedented opportunities and challenges in security and data management. This study explores the integration of big data analytics and Quantum Computing as potential solutions to address security concerns within the Federated IoT ecosystem. The study examines the implications of leveraging big data analytics to process and analyze the massive volume of data generated by IoT devices. Advanced analytics techniques, including machine learning and anomaly detection algorithms, are employed to enhance the detection and mitigation of security threats such as unauthorized access, data breaches and malicious attacks. Furthermore, the study investigates the role of Quantum Computing infrastructure in providing scalable and reliable resources for securely storing, processing and transmitting IoT data. By offloading computational tasks to quantum-based platforms, the aim is to alleviate the burden on edge devices while ensuring robust security measures are in place to safeguard sensitive information. A comprehensive review of existing literature and case studies identifies key challenges and opportunities in implementing big data and Quantum Computing solutions within the Federated IoT environment. The study also proposes potential frameworks and methodologies for integrating these technologies effectively, considering factors such as data privacy, scalability and interoperability. Overall, this research aims to advance secure IoT systems by leveraging big data analytics and cloud computing. By addressing security concerns proactively and adopting innovative approaches, the goal is to create a more resilient and trustworthy Federated IoT ecosystem, benefiting society at large.

Deep Learning for Anomaly Detection in IoT Systems: Techniques, Applications, and Future Directions

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling real-time data collection and processing, leading to unprecedented levels of efficiency and convenience. However, this increased connectivity and data flow also introduce significant security risks, particularly in the realm of anomaly detection. This study delves into the use of deep learning techniques for identifying abnormalities in IoT systems, providing a thorough analysis of state-of-the-art methods, assessing their effectiveness across different IoT scenarios, and exploring future research directions. We review current deep learning-based anomaly detection techniques, examine their applications in real-world settings, and discuss potential improvements and innovations. By synthesizing the latest research and developments, this paper aims to offer a comprehensive understanding of how deep learning can bolster the security and performance of IoT systems. Our findings highlight the importance of robust anomaly detection mechanisms in safeguarding IoT networks and underscore the need for continued advancements in this area. Through this study, we seek to contribute valuable insights into the application of deep learning for enhancing IoT system security and reliability, ultimately supporting the sustainable growth and integration of IoT technologies across various domains.

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. Quantum computing can enhance encryption algorithms to make IoT data more secure for intelligent IoT applications. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

A Comprehensive Machine Learning Framework for Robust Security Management in Cloud-based Internet of Things Systems

The purpose of this paper is to explore the role of Machine Learning (ML) in fortifying the security of cloudbased Internet of Things (IoT) systems, using a comprehensive security management approach. The methodological approach involved comparing different ML techniques such as Decision Trees, Random Forest, Support Vector Machines, and Convolutional Neural Networks. Their effectiveness was evaluated based on the accuracy of threat detection in cloud-based IoT systems. The findings revealed that Convolutional Neural Networks demonstrated the highest accuracy rate (98%) in threat detection, thereby significantly enhancing the security of IoT systems. It also identified improvements in threat detection, prevention, response, and system recovery across all ML techniques. Research limitations were primarily the rapidly evolving nature of both ML and IoT technologies, necessitating continual reassessments. The scope was also limited to cloud-based IoT systems, leaving room for further research on other types of IoT systems. The practical implications included improved system security, which could lead to increased trust and wider adoption of IoT technology in various sectors, from healthcare to home security. The social implications entail a safer digital environment, contributing to data privacy and reducing the risk of cyber threats for individuals and communities. The originality of this paper lies in its comprehensive approach to IoT security management using ML, providing valuable insights into the effectiveness of different ML techniques in enhancing threat detection accuracy.

Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks

The Internet of Things (IoT) has revolutionized the way devices interact and share data over wireless sensor networks (WSN), enabling seamless connectivity and automation. However, the proliferation of IoT devices has raised serious security and privacy risks concerns due to their inherent vulnerabilities. This paper proposes a model for security intrusion monitoring by analyzing the existing literature and providing insights into the design, implementation, and effective deployment of the proposed model to detect intrusion in IoT using sniffing tools for network traffic analysis in real-time within WSN. The model passively monitors network traffic and identifies anomalous patterns, unauthorized access attempts, and abnormal device behavior. The review findings highlight the significance of the proposed model in enhancing the security of IoT systems. By detecting anomalous behavior and potential security breaches. The model enables timely response and mitigation actions to ensure the confidentiality, integrity and availability (CIA) of IoT devices data. The model includes consideration of network architecture, deployment of intrusion detection algorithms, and establishment of response mechanisms. It identifies various types of security threats, such as unauthorized access attempts, Denial-of-service, Distributed DoS, Brute-force, Heartbleed, Botnet, Inside Infiltration and device tampering, thereby providing response mechanisms that include generating alerts, isolating compromised devices, or blocking suspicious network traffic. The model incorporates a feedback loop to continuously update the detection mechanisms and adapt to evolving security threats in real-time. Series of experiments and simulations to be conducted using various IoT devices and network scenarios to evaluate model effectiveness. The model to comprise of wireless Router, MatLab for Deep Neural Network (DNN) training, Raspberry Pi, Wireshark setup and an array of Internet of Things (IoT) devices. The researcher to use dataset by extracting intrinsic, host-based and time-based attributes from Wireshark Sniffing tool. The datasets generated shall be piped by tshark to an output text file saved as a csv. Under-sampling technique to be used to address class imbalance of datasets. The model shall then be trained using the dataset to be able to detect intrusion in IoTs. The results is expected to demonstrate the model's ability to detect a wide range of security intrusions with high accuracy and minimal false positives. In conclusion, the model offers a proactive approach to safeguard IoT deployment. By leveraging sniffing tools and advanced analysis techniques, the model enhances the detection and response capabilities, enabling efficient protection against emerging threats in IoT. However, challenges associated with the model are identified, including the complexity of network monitoring and potential privacy concerns.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

Systematic Literature Review on the Role of Big Data in IoT Security

The Internet of Things (IoT) is an interconnected system of physical objects that are embedded with different sensors (for receiving information), chips, software, and other techniques, which allow connecting and transferring of data to other devices via the Internet without human involvement. Since the number of IoT devices is increasing, large amounts of data are being generated from different sources in different formats. This information needs to be used effectively to gain useful insights for enhancing IoT security. Hence, big data techniques are proposed for managing the data to overcome different issues of IoT. Despite the outstanding achievements in IoT security, a systematic literature review (SLR) on implementing big data for securing IoT is lacking. The number of existing related SLRs is limited. Hence, this paper provides a systematic literature review on the use of big data for securing IoT devices. It summarizes the relevant literature produced during the last six years and provides meaningful insights gained by these existing studies. Moreover, it provides a discussion on the sources of IoT big data, the techniques and approaches of big data for securing IoT systems. Current challenges and future research directions are discussed.

Ensuring the security of the Internet of Things: A deep dive into current network weaknesses and approaches for strengthening

As the Internet of Things (IoT) rapidly takes center stage in today's digital revolution, its integration across various sectors is becoming remarkably ubiquitous. Nevertheless, with the accelerated adoption of IoT, a surge of security concerns has concurrently surfaced. Acknowledging this paradigm, this article delves deep into the underlying architecture of IoT systems, spotlighting their constituent elements and the complex interplay between them. Through meticulous literature reviews and scrutiny of real-world incidents, the most prevalent security vulnerabilities inherent to these systems are pinpointed. For each identified vulnerability, a suite of protective countermeasures is meticulously crafted, underscoring the imperative to preemptively tackle these security gaps. It's paramount to recognize that IoT system security isn't merely a technical requisite but a cornerstone ensuring the system's integrity and resilience. As businesses and individuals increasingly rely on IoT for their day-to-day operations, prioritizing security isn't just a luxury; it's an absolute necessity. Developers and users alike must remain vigilant, consistently updating and fortifying their systems. In doing so, the full potential of IoT can be harnessed while minimizing risks, ensuring that these systems remain both robust and trustworthy in an ever-evolving digital landscape.

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

A survey of security challenges, attacks in IoT

A network of linked devices that can gather, process, and exchange data is known as the Internet of Things (IoT). It delivers several benefits such as convenience, efficiency and automation. However, IoT also poses significant security concerns that necessity to be describe like the heterogeneity and diversity of IoT devices with different hardware, software and communication protocols. Now a days the large scale and complexity of IoT systems, which may involve multiple stakeholders, domains and layers. The limited resources and capabilities of IoT devices, which may affect their ability to perform security functions such as encryption, authentication and update. The vital and evolving features of IoT environments, which may introduce new threats and vulnerabilities over time. These challenges require novel and holistic approaches to ensure that privacy and security of IoT systems and its users. With the develop of security standards and best practices for IoT devices and systems it should include Implementation of security by design and security by default principles for IoT development and deployment by adopting risk-based and adaptive security mechanisms for IoT management, operation and enhanced security awareness and education among IoT users and stakeholders.

Improving Network Security using Tunicate Swarm Algorithm with Stacked Deep Learning Model on IoT Environment

The Internet of Things (IoT) represents important security vulnerabilities, increasing difficulties in cyberattacks. Attackers employ these vulnerabilities to establish distributed denial-of-service (DDoS) attacks, compromising availability and causing financial losses to digital platforms. Newly, numerous Machine Learning (ML) and Deep Learning (DL) approaches have been presented for the identification of botnet attacks in IoT networks. By analyzing the patterns of communication and behavior of IoT devices, DL algorithms will be differentiated between malicious and normal activity, therefore supporting the earlier detection and avoidance of botnet attacks. This is essential to protect the integrity and security of IoT systems that can be increasingly vulnerable to botnet-driven attacks because of their limited security measures and often large-scale applications. In this aspect, this study designs an innovative tunicate swarm algorithm with stacked deep learning for botnet detection (TSASDL-BD) technique for IoT platforms. The purpose of the TSASDL-BD technique is to recognize the botnets and achieve maximum network security. In the TSASDL-BD technique, the TSA is applied for the effectual feature selection process, which aids in reducing the dimensionality problem. For botnet detection, the TSASDL-BD technique makes use of the stacked long short-term memory gated recurrent unit (SLSTM-GRU) model. Finally, the artificial humming algorithm (AHA) can be used for the optimal selection of the hyperparameter values of the SLSTM+GRU system. The outcome analysis of the TSASDL-BD method on the benchmark database takes place. The extensive outcomes stated that the TSASDL-BD approach gains maximum detection results over other algorithms with respect of different measures

A novel approach for integrating cryptography and blockchain into IoT system

The quick advancement of Internet of Things (IoT) emphasizes the significance of cryptography and blockchain ensuring the security of sensitive data and connected devices. Blockchain technology and encryption play key roles in ensuring the security of the expansive IoT network. Blockchain offers decentralized trust, immutability, and transparency to IoT networks and transactions, while encryption serves to protect IoT data from unauthorized access. It is a novel approach for integrating cryptography and blockchain into IoT System, cryptography and blockchain stand out as robust technologies that enhance the security of IoT systems. The implementation of an integrated architecture, along with a strategic integration approach, further strengthens the security measures. This methodology proves valuable for managing and validating digital transactions on decentralized, immutable networks. This work also explores the potential significance of integrating cryptography and blockchain into IoT System, this functions and applications in enhancing IoT security. This methodology introduces encryption techniques tailored for resource-constrained IoT devices, which are essential for ensuring end-to-end security.

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized internet-based technologies and have led to an increase in information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities involved in connected and shared environments. Networks are guarded by Intrusion Detection Systems (IDS) against various cyber threats such as malware, viruses, and unauthorized access. IDS have recently adopted Machine Learning (ML) and Deep Learning (DL) techniques to identify and classify security risks. However, the effective utilization of these technologies depends on the availability, quality, and characteristics of the data used to train models. Moreover, data lack, data leak, and dimensionality (DLLD) are common problems in data science and ML. This paper surveys existing research and suggests solutions for overcoming DLLD-related issues to improve IDS model.

Smart Building Security using ESP32 based AES One Bio-key and Owner's Biometrics Encryption Technology

Internet of Things (IoT) connects smart devices to the internet, but it poses significant security challenges. ‘Security measures like access control, authentication, and encryption must be implemented during IoT system development to safeguard sensitive information and ensure secure transmission. Regular security updates, ongoing assessment, and improved measures are necessary to address evolving threats. Data security is crucial for overall IoT system security, given its unique features like perception, transmission, and processing. In this paper, it is suggested using encryption for data sent over Wi-Fi to improve Internet of Things (IoT) security. Advanced Encryption Standard (AES) commonly used encryption standard, and it is the encryption technique employed. AES algorithm uses one bio-key (variable key every minute) to maximize security and the capacity to fend off attacks. It is added the owner's biometric to the bio-key for authentication and security in order to authenticate the system. Our technology stands out with robust encryption and authentication against numerous assaults because the owner's biometrics are encrypted and it is added to the bio-key made up of the minute-by-minute changes to the year, month, day, hour, and minute. The data is encoded in ESP32 and sent to the local server and then stored in a Python language database using the HTTP protocol. The database is hosted on XAMPP, which is a web server package that allows easy database management.

AKILLI EV SİSTEMLERİNDE KUANTUM ANAHTAR DAĞITIMI

In recent years, with the increase in the widespread use of the internet, the usage areas and the number of users have increased rapidly. Technological advances and efforts to make life easier bring innovation and transformation in many sectors. One of these innovative approaches is the use of Internet of Things (IoT) in home systems. The concept of IoT refers to the ability of objects to communicate with each other over the internet. Smart home systems stand out as systems where different devices come together work in interaction and can be controlled remotely. While this communication and integration offers various advantages, it also brings security risks. Ensuring security in IoT systems basically includes objectives such as confidentiality, integrity, and authentication. In this study, how the key used in the encryption of data transmitted between the gateway and the cloud in smart home systems can be strengthened with the B92 Quantum Key Distribution protocol is implemented through a scenario. The effect of the Quantum Key Distribution protocol in providing privacy and its advantages against man-in-the-middle attacks are emphasized in this study.

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Security is an important field in the Internet of Things (IoT) systems. The IoT and security are topical domains. Because it was obtained 35,077 document results from the Scopus database. Hence, the AI (Artificial Intelligence) has proven its efficiency in several domains including security, digital marketing, healthcare, big data, industry, education, robotic, and entertainment. Thus, the contribution of AI to the security of IoT systems has become a huge breakthrough. This contribution adopts the artificial intelligence (AI) as a base solution for the IoT security systems. Two different subsets of AI algorithms were considered: Machine Learning (ML) and Deep Learning (DL) methods. Nevertheless, it is difficult to determine which AI method and IoT dataset are best (more suitable) for classifying and/or detecting intrusions and attacks in the IoT domain. The large number of existing publications on this phenomenon explains the need for the current state of research that covers publications on IoT security using AI methods. Thus, this study compares the results regarding AI algorithms that have been mentioned in the related works. The goal of this paper is to compare the performance assessment of the existing AI algorithms in order to choose the best algorithm as well as whether the chosen algorithm can be used for classifying or/and detecting intrusions and attacks in order to improve security in the IoT domain. This study compares these methods in term of accuracy rate. Evaluating the current state of IoT security, AI and IoT datasets is the main aim for considering our future work. After that, this paper proposes, as result, a new and general taxonomy of AI techniques for IoT security (classification and detection techniques). Finally, the obtained results from this assessment survey that was dedicated to research conducted between 2018 and 2023 were satisfactory. This paper provides a good reference for researchers and readers in the IoT domain.