KEYWORDS: Data modeling, Internet of things, Computer intrusion detection, Education and training, Gallium nitride, Neural networks, Deep learning, Machine learning, Evolutionary algorithms, Data processing
In recent years, the frequency and complexity of IoT network attacks have significantly increased. NIDS, strategically located in IoT network nodes, is an essential tool for monitoring traffic and detecting and mitigating network-based attacks. However, with the significant increase in computer network attacks, many datasets used for training suffer from imbalanced data problems. Therefore, to address the traffic characteristics of IoT networks and the issue of imbalanced data, this paper proposes an intrusion detection method that combines graph neural networks(E-GraphSAGE) and generative adversarial networks. Based on experiments using datasets NF-BoT-IoT, we found that training ML classifiers on datasets balanced with synthetic samples generated by WGAN-gp increased their prediction accuracy to 93.7% .
KEYWORDS: Robots, Network security, Information security, Control systems, Power grids, Clouds, Matrices, Defense and security, Telecommunications, Monte Carlo methods
The information network of the power grid enterprise is the same as the information system of the common Internet, and its application layer has a lot of general IT software. In order to mine all kinds of software vulnerabilities timely and ensure the normal operation of the software system, the most obvious disadvantage of this method is the low efficiency of implementation, which requires manual dynamic testing after the end of software development. It also needs to track the location of vulnerabilities according to the test results. In this paper, the cooperative control of heterogeneous wireless networked robots based on parallel control is proposed, and the configuration of defense resources is studied from the perspective of protection. Firstly, based on the network security robot, the game model of both sides of the power grid attack and defense under coordinated attack is established, and the optimal defense resource allocation strategy is analyzed and solved. Then, for heterogeneous wireless networks, a step-by-step solution is proposed based on parallel control optimization, and the allocation method of defense resources is formulated. Finally, the proposed method is verified on the simulation test system and compared with AUKF and IMM-UKF. The experimental results verify that the proposed method performs well in error control when the execution state changes, and achieves high accuracy, good stability and strong security prediction ability in general. It can ensure the safety of power grid and promote the healthy development of the national power industry.
KEYWORDS: Computer security, Network security, Data transmission, Databases, Reliability, Information security, Data communications, Internet technology, Internet, Head
In recent years, privacy data has been stolen frequently, and the security of privacy data has been concerned by society. In the big data environment, privacy is facing unprecedented challenges, and some traditional privacy protection technologies are facing failure, so how to choose a reasonable privacy protection technology is a challenging task. To solve the problem of a large amount of data theft in the application of traditional methods in the privacy data security protection, this paper proposes research on privacy data security based on multi-party computation. The hash function is used to encrypt the private data, and the data is stored in the block chain in the form of a private data encryption file. Based on the theory of multi-party computation, a secure multi-party technology protocol is designed, and the protocol is used to verify the identity of the participants in the private data transmission to realize the security protection of private data. The experimental results show that the amount of privacy data stolen by the application design method is less than that of the traditional method, which has important application value for the security protection of privacy data.
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