Based on the openness and accessibility of user data, personality recognition is widely used in personalized recommendation, intelligent medicine, natural language processing, and so on. Existing approaches usually adopt a single deep learning mechanism to extract personality information from user data, which leads to semantic loss to some extent. In addition, researchers encode scattered user posts in a sequential or hierarchical manner, ignoring the connection between posts and the unequal value of different posts to classification tasks. We propose a hierarchical hybrid model based on a self-attention mechanism, namely HMAttn-ECBiL, to fully excavate deep semantic information horizontally and vertically. Multiple modules composed of convolutional neural network and bi-directional long short-term memory encode different types of personality representations in a hierarchical and partitioned manner, which pays attention to the contribution of different words in posts and different posts to personality information and captures the dependencies between scattered posts. Moreover, the addition of a word embedding module effectively makes up for the original semantics filtered by a deep neural network. We verified the hybrid model on the MyPersonality dataset. The experimental results showed that the classification performance of the hybrid model exceeds the different model architectures and baseline models, and the average accuracy reached 72.01%.
Malicious social media bots are disseminators of malicious information on social networks and seriously affect information security and the network environment. Efficient and reliable classification of social media bots is crucial for detecting information manipulation in social networks. Aiming to correct the defects of high-cost labeling and unbalanced positive and negative samples in the existing methods of social media bot detection, and to reduce the training of abnormal samples in the model, we propose an anomaly detection framework based on a combination of a Variational AutoEncoder and an anomaly detection algorithm. The purpose is to use Variational AutoEncoder to automatically encode and decode sample features. The normal sample features are more similar to the initial features after decoding; however, there is a difference between the abnormal samples and the initial features. The decoding representation and the original features are combined, and then the anomaly detection method is used for detection. The results show that the area under the curve of the proposed model for identifying social media bots reaches 98% through the experiments on public datasets, which can effectively distinguish bots from common users and further verify the performance of the proposed model.
In order to improve user authentication accuracy based on keystroke dynamics and mouse dynamics in hybrid scenes and to consider the user operation changes in different scenes that aggravate user status changes and make it difficult to simulate user behaviors, we present a user authentication method entitled SIURUA. SIURUA uses scene-irrelated features and user-related features for user identification. First, features are extracted based on keystroke data and mouse movement data. Next, scene-irrelated features that have a low correlation with scenes are obtained. Finally, scene-irrelated features are fused with user-related features to ensure the integrity of the features. Experimental results show that the proposed method has the advantage of improving user authentication accuracy in hybrid scenes, with an accuracy of 84% obtained in the experiment.
Obscuring or otherwise minimizing the release of personality information from potential victims of social engineering attacks effectively interferes with an attacker’s personality analysis and reduces the success rate of social engineering attacks. We propose a text transformation method named PerTransGAN using generative adversarial networks (GANs) to protect the personality privacy hidden in text data. Making use of reinforcement learning, we use the output of the discriminator as a reward signal to guide the training of the generator. Moreover, the model extracts text features from the discriminator network as additional semantic guidance signals. And the loss function of the generator adds a penalty item to reduce the weight of words that contribute more to personality information in the real text so as to hide the user’s personality privacy. In addition, the semantic and personality modules are designed to calculate the semantic similarity and personality distribution distance between the real text and the generated text as a part of the objective function. Experiments show that the self-attention module and semantic module in the generator improved the content retention of the text by 0.11 compared with the baseline model and obtained the highest BLEU score. In addition, with the addition of penalty item and personality module, compared with the classification accuracy of the original data, the accuracy of the generated text in the personality classifier decreased by 20%. PerTransGAN model preserves users’ personality privacy as found in user data by transforming the text and preserving semantic similarity while blocking privacy theft by attackers.
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