The development of smart devices in many aspects of our daily lives is accompanied by the increasing use of appropriate mechanisms to counter them against various attacks and applications in the Internet of Things environment. In this context, it is emerging as one of the most successful and suitable techniques for use in various aspects of IoT security. The aim of this is to systematically review and analyze research studies on research eyes conducted in different Internet of Things security scenarios. The reviewed researches are classified according to different perspectives in a coherent and structured classification to identify the gap in this research area. This research has been published on articles related to the keywords "concept learning", "security" and "Internet of Things" in the four main databases IEEEXplore, ScienceDirect, SpringerLink, and ACM Digital Library. In the end, 90 articles have been selected and reviewed. These studies are conducted according to three main research questions, i.e. the security aspects involved, the network architectures used, and the datasets used in IoT security. The final discussion explores the research gaps and acknowledges the outstanding flaws and vulnerabilities in the IoT security scenario.

Nowadays, The Combination of Information Technology and Communication, artificial intelligence, Internet of Things (IoT), blockchain, and other new technologies has led to the emergence of the Fourth Industrial Revolution, which has brought about profound changes in human life. The Internet of Things has evolved from equipping devices around humans with sensors, actuators, advanced chips, and intelligent algorithms that connect to the Internet and communicate with other devices or humans independently. Due to independence of smart devices in the IoT, security is one of the most important issues in the IoT debate. IoT security mechanisms should not only address traditional security attacks, but should also provide secure connections for both machine-to-human and machine-to-machine communication. Authentication of Devices is one of the most important issues in IoT Security. Therefore, in this paper, a location-aware mutual authentication protocol is proposed for IoT systems. In this protocol, entities can authenticate each other and attain a shared secret session key. In the production of this key, the information of the devices, their location and the type of devices in terms of being fixed or mobile have been used. ،Then, this key is used to authenticate devices before sending information over wireless channels. To prove the correctness and lightness of the proposed protocol, Experiments were performed and the results show that adding the location parameter to the authentication protocol has increased the security strength of the protocol and communication and computing costs have not increased significantly.

The Internet of Things is an emerging technology that integrates the Internet and physical intelligent objects, objects that cover a wide range of areas such as smart homes and cities, industrial and military processes, human health, business and agriculture. belongs to. IoT technology deepens the presence of Internet-connected devices in our day-to-day operations, bringing many benefits to the quality of life, as well as security-related challenges. Accordingly, IoT security solutions should be developed; Like other networks, IoT intrusion detection systems are the most important security too In the present study, a method is proposed to detect IoT intrusion using game theory. In the proposed method, the attacker security attack game and the behavior of the intrusion detection system in a two-player, non-participatory game are analyzed dynamically and with complete information, and the equilibrium solutions are obtained for specific sub-games. Analysis of best response parameters using game theory and Nash equilibrium definitions indicates the need to use cloud-fog-based IoT intrusion detection systems by providing optimal strategies and reporting maximum attacks by the smart node network. The proposed model.

Smart applications with interconnected intelligent devices for sharing services arise serious security problems to the stability of this IoT complex and heterogeneous environment. Unless security considerations are analyzed and implemented properly in real time then IoT cannot be perceived as a pervasive network for the possible stakeholders. Current state of the art has analyzed trust-based security solutions as additional feature to application layer of the system which can identify and filter out the malicious nodes. In this paper we are proposing holistic trust management with edge computing mechanism to create trustworthy zones comprising different clusters, where Gateway on behalf of clusters will initiate migration of their nodes if falls below the defined Zone trust threshold level. The created zones are self-resilient against any malicious attacks and saves lots processing usage time and energy to address the security issues. By analyzing our proposed algorithm with other contemporary approaches to handle IoT security issues using trust mechanism, this approach is more precise in terms of protecting system against incurring malicious behavior, and also prolong the application operation duration by reducing communication and processing overhead.

The Internet of Things(IoT) is a new concept on the IT industry that develops a new future of internet through combining users ,computational systems and all daily things. The problem is that the actual development of these services requires enhanced levels of security and confidentiality. The data encryption is most common method to ensure the confidentiality of information during data transmission. In this article first the challenges facing IoT security, including the vast amount of information and communication between systems hardware, along with security challenge are addressed. Then reviewing and explaining the common AES128 encryption method used in IoT protocols the present dissertation performs the process of upgrading the method to the highest level of security of this algorithm, i.e. AES256, along with implementing and simulating its performance on a data packet. The results of this upgrade and production of the desired outputs were used to analysis and compare the proposed method by using MATLAB software.

Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Smart home is considered one of the rather prominent applications of the Internet of Things (IoT), integrating high-levels of efficiency, home security, energy & cost saving to everyone’s life. In spite of all the benefits this technology provides, privacy and security are highly concerning issues that require more considerations. IoT-A reference architecture was established with the purpose of evaluating current sources and protocols, ensuring the compliance of things and protocols, and providing a comprehensive solution for different applications of IoT. This study was performed with the purpose of providing a general framework for improving security at all levels of design, implementation, and application of equipment and protocols using the IoT-A reference architecture by addressing the challenge of security in the Internet of Things and smart homes. This paper employs the term Security Framework to refer to a method for applying all technologies, procedures, software, and other components to provide security in smart homes. This research seeks to outline all the reference architecture's vulnerabilities and threats, following which an improved model for the reference architecture is proposed to meet all security requirements. Considering the theoretical evaluations performed in this study, the proposed framework, which was created by adding two components of threat and vulnerability management and field management while making some alterations to the licensing component, satisfies to an acceptable level the security requirements of the smart home and enhances the privacy of the IoT-based smart home.

A vision of the future Internet has been introduced in which various computing devices will be connected to each other to form a network called the Internet of Things. The Internet of Things facilitates human life by providing many applications and smart devices that can be controlled remotely. IoT security is a challenging task due to the inherent characteristics of IoT, especially the heterogeneity of nodes in terms of resources. Trust management, by calculating and analyzing the trust between nodes, enables the node to make an appropriate and reliable decision in communication between nodes. The goal of trust management schemes in a distributed system is to predict the future behavior of nodes based on their previous behavior. In this paper, an event-based distributed trust management method is proposed that calculates the trust between objects using weighted summation. In this method, nodes can evaluate the behavior of other nodes. According to the performed simulation, the proposed method is faster in comparison with the DDTMS method and identifies malicious nodes in fewer transactions and is also more resistant to on-off and bad-mouthing attacks.

With the advancement of human society and information and communication technology, the Internet of Things has penetrated into various aspects of the daily lives of people and industries. Emerging blockchain technology has become a viable solution to IoT security due to its inherent characteristics such as distribution, security, immutability, and traceability. However, the integration of IoT and blockchain has challenges such as latency, throughput, scalability, and device power limitation. Recent research has focused on the role of artificial intelligence methods in improving IoT performance in blockchain. According to the studies, there are few effects on improving the performance of IoT devices with limited power, so in this study, a conceptual model for improving blockchain performance in IoT devices with limited power by deep reinforcement learning is proposed. According to studies, there is little research on improving the performance of IoT devices with limited power, so in this study, a conceptual model to improve blockchain performance in IoT devices with limited power by deep reinforcement learning is proposed. In this model, Internet devices with limited power can delegate their extraction task to the mobile edge computing layer. The presented model has six layers of perception, data, network, consensus, mobile edge computing and application, which are explained in detail. In this model, to improve the throughput and select the mining method, a recommender located in the mobile edge computation layer is used. recommender systems are adjusted by adjusting the size and time of building blocks to improve the throughput and also tries to minimize the delay and energy consumption of the mining operation by selecting suitable method. To achieve good performance in reinforcement learning, the use of Q learning and long- short term memory is suggested. the use of deep reinforcement learning is to set the block size by considering the transmission delay in order to increase throughput as well as mining with respect to the minimum delays and energy consumption in the proposed conceptual model can improve the performance of blockchain in the IoT.

The main purpose of this study is to use Falcon's artificial neural networks and fuzzy inference system to designing an IOT-based business model in telemedicine at the National Institute of Medical Research Development of Iran. In fact, the research fatigue of NIMAD decision makers and physicians can be explained by the combining different ways of designing an IOT-based business model in telemedicine. In addition the need to use the T-MEDICINE+IOT.F-ANFIS system to increase confidence and reliability in Decision making, as well as the issue of the need for multiple expertise through the simultaneous using multi-disciplinary knowledge of different areas to solve the problem of internet of things-based business model. In this study, the Matlab R2017b programming environment was applied to design an Expert system based on Falcon neural networks. Finally, using the T-MEDICINE+IOT.F-ANFIS system outputs, the status of "IOT-based business model in telemedicine" can be analyzed based on variables such as following items: Using IOT in telemedicine; the ability to managing devices; The IOT networks position; IOT security; the degree of familiarity with the analyzing IOT applications. In fact, the final difference between the analyzing the outputs of the Expert system based on Falcon neuro-fuzzy system of this study, and the average views of Telemedicine physicians and IOT-based business model specialists, It is not significant and it equals to 0.065.

Today, the Internet of Things is expanding due to a wide range of applications and services. The variety of devices connected to the Internet has made discussing security in these networks a challenging issue. Security includes various aspects such as botnets. Botnets are a collection of devices such as smartphones, computers, and other devices infected by a program. This program, which is a herder bot, performs many harmful operations and leads to various anomalies in the network. Identifying botnets is one of the main challenges in IoT security due to their unique complexity. In this article, we have reviewed the botnet detection methods in IoT. Since there are different botnet detection methods in IoT, we need to do detailed research on different botnet detection methods and their strengths and weaknesses. In a way that shows the evolution of these malwares. Concepts such as life cycle, command and control models, communication protocols, botnet protocols, and botnet detection methods are described in this research. In the following, the advantages and disadvantages of botnet detection methods are discussed and these methods are compared.