Abstract
Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short‐term memory (LSTM), and a hybrid convolution neural network with the long short‐term memory (CNN‐LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN‐LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real‐world attacks and is capable of enhancing the security of the IoT environment.
Highlights
There are more than 25 billion devices connected to the Internet worldwide, three times as many human beings [1,2,3]. e Internet of ings (IoT) is based on interconnected smart devices, and different services are used to integrate them into a single network. is allows the smart devices to gather sensitive information and carry out important functions, and these devices connect and communicate with each other at high speeds and make decisions according to indicator information. e IoT environment uses cloud services as a backend for processing information and maintaining remote control
It was noted that 10 features were the most significant features that enhanced the classification algorithm to attain good results. ey used cross-validations 3, 5, and 10 to validate their results. us, we developed a system based on deep learning algorithms to improve the accuracy of detecting attacks. e particle swarm optimization method (PSO) method was Number of epochs
We presented the implementation and evaluation of a proposed framework to detect intrusions based on IoT infrastructure
Summary
There are more than 25 billion devices connected to the Internet worldwide, three times as many human beings [1,2,3]. e Internet of ings (IoT) is based on interconnected smart devices, and different services are used to integrate them into a single network. is allows the smart devices to gather sensitive information and carry out important functions, and these devices connect and communicate with each other at high speeds and make decisions according to indicator information. e IoT environment uses cloud services as a backend for processing information and maintaining remote control. E Internet of ings (IoT) is based on interconnected smart devices, and different services are used to integrate them into a single network. Client users use mobile applications or web services to access data and control the devices. Complexity systems, networks, and data from malicious attacks. It is known as information technology security [6,7,8,9]. Indications of intrusions incorporating abnormal outcomes while executing different client charges are exemplified by moderate system execution, and sudden system crashes and changes in parts of information structures are, bizarrely, moderate system implementations (e.g., opening records or accessing sites)
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