mohammad davarpour

The Internet of Things (IoT) refers to the connection of various devices to each other via the internet. Conceptually, the IoT can be defined as a dynamic, self-configuring network infrastructure based on standards and participatory communication protocols. The main goal of the IoT is to lead towards a better and safer community. However, one of the fundamental challenges in developing the IoT is the issue of security, and intrusion detection systems are one of the main methods to create security in the IoT. On the other hand, Convolutional Neural Network (CNN), with its specific features, is one of the best methods for analyzing network data. This network is a type of deep neural network composed of multiple layers that can ultimately reduce the dimensions of features. Additionally, the cuckoo algorithm has parameters required for configuration in the initial search, which are very few and can naturally and efficiently cope with multi-state problems. In this paper, a new method for intrusion detection in the IoT using CNN and feature selection by the cuckoo algorithm is presented. Simulation results indicate the satisfactory performance of the proposed method.

The Internet of Things (IoT) is an emerging field of study and operation. IoT creates a structure for internet-connected devices. IoT-enabled systems are susceptible to various security and privacy attacks due to their inherently open nature. In IoT-enabled systems, multimedia information moves from one end to another, and computational complexity with constrained environments such as ad-hoc networks, mobile networks, etc., is crucial. Wireless Sensor Networks (WSNs) consist of a large number of sensor nodes, each with very limited computational power and storage capacity. Sensor nodes are typically installed for monitoring activities in unsupervised locations, controlled by one or more gateway nodes. Maintaining the confidentiality of information communication is one of the main challenges, and preserving the privacy of multimedia data from unauthorized access by attackers is a major issue for active WSNs with IoT. In this paper, to leverage the benefits of the Kerberos encryption protocol and reduce overhead for IoT-based wireless sensor networks, users are authenticated using the Kerberos protocol, and then sensor data is encrypted using elliptic curve encryption protocol. The proposed method enhances the security of wireless sensor network by combining Kerberos encryption and elliptic curve encryption techniques.

Wireless sensor networks consist of a large number of sensor nodes spread in an environment. If the target-related data is collected semantically in the sensor network, it would be possible to be processed and reasoned intelligently. On the other hand, one of the most important challenges to do with all cases related to sensor networks is the limitation of energy consumption. Data aggregation technique is a way to reduce the energy consumed in wireless sensor networks. Although the techniques based on data aggregation extends the network lifetime through reducing the number of sent packets, they can decrease the accuracy and reliability of the received data. In this paper, Firstly, one strategy for semantic data collection and aggregation of information related to the targets located in sensor network environment are presented. Secondly, through semantic modelling of trust, a method is presented in aggregation technique in order to identify highly trusted sensors and increase their use in collection and aggregation of data about the sensed targets. Therefore, the system accuracy and trust would be generally improved. Modelling and simulation results show the desirable performance of the presented method compared to the previous works.

Virtual assets have emerged as a novel form of digital property that can be owned, traded, and secured using blockchain technology. These assets can represent real-world assets and can be traded on blockchain-based platforms. The Internet of Things (IoT) has also emerged as a significant technological advancement, enabling the interconnection of devices and systems, facilitating the exchange of data and information. The combination of virtual assets and IoT can create new opportunities for asset tracking, inventory management, and supply chain optimization. This paper presents an overview of the integration of virtual assets and IoT, exploring the challenges and opportunities associated with their integration. Additionally, the paper highlights ten categories of incorporation of virtual assets and IoT, including asset tracking, smart contracts, and supply chain management. The presented cases demonstrate the potential of integrating virtual assets and IoT in various industries and applications, showcasing their ability to enhance security, efficiency, and transparency.