In this paper, an interactive multitask learning method for Chinese text sentiment classification is proposed. Here, the classic BiLSTM + attention + CRF model is used to obtain full use of the interaction relationship between tasks, and it simultaneously solves the two tasks of emotional dictionary expansion and sentiment classification. The proposed method divides text sentiment classification and emotional dictionary expansion into primary task and subtask, and it adopts the Enhanced Language Representation with Informative Entities (ERNIE) model as the text representation learning model for the primary task. Then, through the maximum pooling layer and the fully connected layer, the text sentiment classification task is completed. Meanwhile, the classical BiLSTM + attention + CRF model is used to extract emotional words from the text in the subtask. In addition, the multitask information interaction mechanism is used, and the prediction information on the autonomous subtask is fed back into the potential representation of the two tasks. After iterative training, the performance of the two tasks is further optimized. Micro-blogs with COVID-19 are used here as the subject to form the experimental data set. The results demonstrate the superiority of the proposed method over other approaches, and they further verify the superiority of ERNIE over BERT, RoBERTa and XLNet for the sentiment classification of Chinese text.

With the rapid development of Cooperative Intelligent Transportation System (C-ITS), it becomes an urgent problem to effectively evaluate the data transmission efficiency of code dissemination protocols with network coding in the Dedicated Transportation Sensor Network (DTSN). First, this paper builds the overall structure of DTSN to remotely monitor the railway infrastructure in the C-ITS. Second, we propose a time model of page dissemination to reduce the deviation between the predictive data of existing models and the real-world data of code dissemination. Third, a Firefly Algorithm is designed to further improve the data transmission efficiency. The algorithm makes the feasibility rules to handle constraints, and it searches the optimal page granularity to minimize the time of page dissemination through iteration. Experiments show that the simulation results are consistent with the prediction results of proposed model, and code images can be distributed quickly and efficiently in the DTSN.

Network traffic classification technologies could be used by attackers to implement network monitoring and then launch traffic analysis attacks or website fingerprint attacks. In order to prevent such attacks, a novel way to generate adversarial samples of network traffic from the perspective of the defender is proposed. By adding perturbation to the normal network traffic, a kind of adversarial network traffic is formed, which will cause misclassification when the attackers are implementing network traffic classification with deep convolutional neural networks (CNN) as a classification model. The paper uses the concept of adversarial samples in image recognition for reference to the field of network traffic classification and chooses several different methods to generate adversarial samples of network traffic. The experiment, in which the LeNet‐5 CNN is selected as a classification model used by attackers and Vgg16 CNN is selected as the model to test the transferability of the adversarial network traffic generated, shows the effect of the adversarial network traffic samples.

Cloud data, the ownership of which is separated from their administration, usually contain users’ private information, especially in the fifth-generation mobile communication (5G) environment, because of collecting data from various smart mobile devices inevitably containing personal information. If it is not securely deleted in time or the result of data deletion cannot be verified after their expiration, this will lead to serious issues, such as unauthorized access and data privacy disclosure. Therefore, this affects the security of cloud data and hinders the development of cloud computing services seriously. In this paper, we propose a novel secure data deletion and verification (SDVC) scheme based on CP-ABE to achieve fine-grained secure data deletion and deletion verification for cloud data. Based on the idea of access policy in CP-ABE, we construct an attribute association tree to implement fast revoking attribute and reencrypting key to achieve fine-grained control of secure key deletion. Furthermore, we build a rule transposition algorithm to generate random data blocks and combine the overwriting technology with the Merkle hash tree to implement secure ciphertext deletion and generate a validator, which is then used to verify the result of data deletion. We prove the security of the SDVC scheme under the standard model and verify the correctness and effectiveness of the SDVC scheme through theoretical analysis and ample simulation experiment results.

Increasingly, more administrators (defenders) are using defense strategies with deception such as honeypots to improve the IoT network security in response to attacks. Using game theory, the signaling game is leveraged to describe the confrontation between attacks and defenses. However, the traditional approach focuses only on the defender; the analysis from the attacker side is ignored. Moreover, insufficient analysis has been conducted on the optimal defense strategy with deception when the model is established with the signaling game. In our work, the signaling game model is extended to a novel two-way signaling game model to describe the game from the perspectives of both the defender and the attacker. First, the improved model is formally defined, and an algorithm is proposed for identifying the refined Bayesian equilibrium. Then, according to the calculated benefits, optimal strategies choice for both the attacker and the defender in the game are analyzed. Last, a simulation is conducted to evaluate the performance of the proposed model and to demonstrate that the defense strategy with deception is optimal for the defender.

The Internet of Things (IoT) is an emerging technology that has attracted significant attention and triggered a technical revolution in recent years. Numerous IoT devices are directly connected to the physical world, such as security cameras and medical equipment, making IoT security a critical issue. Artificial intelligence (AI) based intrusion detection technology for IoT can rapidly detect network attacks and improve security performance. However, this technology is vulnerable to backdoor attacks. As an important form of adversarial machine learning (ML), backdoor attacks can allow malicious traffic to evade detection of the intrusion detection system, posing a significant threat to the IoT security. This study focuses on backdoor attack and defense methods for AI–based IoT intrusion detection system. Specifically, we first use different ML and deep learning (DL) classification models to classify IoT traffic data, thereby achieving intrusion detection within IoT. Additionally, we employ data poisoning techniques to implant backdoors into models, enabling backdoor attacks on classification models. For backdoor defense, we propose backdoor detection and mitigate methods: (1) The proposed backdoor detection method is achieved by leveraging the strong correlation between the backdoor trigger and the target classification; (2) we utilize the unlearning method to mitigate the backdoor effect, enhancing the robustness of classification networks. Extensive experiments were conducted on the CICIOT2023 dataset to evaluate the effectiveness of IoT intrusion detection, backdoor attack, and defense.