Abstract
With the wide application of network technology, the Internet of Things (IoT) systems are facing the increasingly serious situation of network threats; the network threat situation assessment becomes an important approach to solve these problems. Aiming at the traditional methods based on data category tag that has high modeling cost and low efficiency in the network threat situation assessment, this paper proposes a network threat situation assessment model based on unsupervised learning for IoT. Firstly, we combine the encoder of variational autoencoder (VAE) and the discriminator of generative adversarial networks (GAN) to form the V-G network. Then, we obtain the reconstruction error of each layer network by training the network collection layer of the V-G network with normal network traffic. Besides, we conduct the reconstruction error learning by the 3-layer variational autoencoder of the output layer and calculate the abnormal threshold of the training. Moreover, we carry out the group threat testing with the test dataset containing abnormal network traffic and calculate the threat probability of each test group. Finally, we obtain the threat situation value (TSV) according to the threat probability and the threat impact. The simulation results show that, compared with the other methods, this proposed method can evaluate the overall situation of network security threat more intuitively and has a stronger characterization ability for network threats.
Highlights
In recent years, the application of various emerging network technologies such as big data, blockchain, artificial intelligence, and other technologies in the field of Internet of ings (IoT) has brought about more and more convenience to people in many fields
Because of the connection with the Internet, the IoT devices are vulnerable to more network threats [1], which will result in malicious attacks on physical devices
Because the IoT devices and applications are connected to the Internet, they are vulnerable to a variety of network attacks, which leads to important information leakage and even allows attackers to obtain permission to operate these devices. e authors of [3, 4] applied encryption algorithm in oblivious RAM to ensure the information security of storage devices. e IoT devices that are attacked by the network may have the management rights of the database stolen
Summary
The application of various emerging network technologies such as big data, blockchain, artificial intelligence, and other technologies in the field of Internet of ings (IoT) has brought about more and more convenience to people in many fields. Because of the connection with the Internet, the IoT devices are vulnerable to more network threats [1], which will result in malicious attacks on physical devices. Because the IoT devices and applications are connected to the Internet, they are vulnerable to a variety of network attacks, which leads to important information leakage and even allows attackers to obtain permission to operate these devices. E authors of [3, 4] applied encryption algorithm in oblivious RAM to ensure the information security of storage devices. To ensure the privacy and security of the database, the authors of [5, 6] proposed encryption algorithms to prevent the leakage of important information. In the face of a large number of complex network attacks, it is necessary to ensure network information security from a more comprehensive perspective
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
