Automated vulnerability detection is one of the critical issues in the realm of software security. Existing solutions to this problem are mostly based on features that are defined by human experts and directly lead to missed potential vulnerability. Deep learning is an effective method for automating the extraction of vulnerability characteristics. Our paper proposes intelligent and automated vulnerability detection while using deep representation learning and heterogeneous ensemble learning. Firstly, we transform sample data from source code by removing segments that are unrelated to the vulnerability in order to reduce code analysis and improve detection efficiency in our experiments. Secondly, we represent the sample data as real vectors by pre-training on the corpus and maintaining its semantic information. Thirdly, the vectors are fed to a deep learning model to obtain the features of vulnerability. Lastly, we train a heterogeneous ensemble classifier. We analyze the effectiveness and resource consumption of different network models, pre-training methods, classifiers, and vulnerabilities separately in order to evaluate the detection method. We also compare our approach with some well-known vulnerability detection commercial tools and academic methods. The experimental results show that our proposed method provides improvements in false positive rate, false negative rate, precision, recall, and F1 score.

Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.

Online social networks provide valuable information for researchers. However, users' privacy will be exposed when the social network data is published. Social relationships are connected with our social identification which represent users' sensitive message. In addition, there are some nodes in the social network that are closely connected to others, called hub nodes. These hub nodes play an important role in the social network. Many proposed methods only protect the privacy of social relationships, the hub nodes with a large amount of neighbors are exposed to the adversary. Therefore the adversary can easily find out the identity of these nodes, and then inferring the probability of the existence of a link, although the link is perturbed. To tackle this problem, we proposed a random walk based algorithm which can protect both hub nodes and social relationships. Through a random degree perturbation algorithm, the performance of link privacy preserving method can be improved. We conducted comprehensive experiments over the real-world social network dataset to evaluate the anonymity level and utility of the transformed graph. The experimental results show that the proposed mechanism can achieve higher link privacy and has better utility, outperforming the state of the art approaches.

Predicting network abnormal events and behavior can enhance security situation awareness and the ability to infer attack intentions. Most of the existing abnormal event prediction methods usually rely on the temporal relationship features between events and the spatial relationship features between hosts. However, the existing spatio-temporal anomaly event prediction methods do not fully consider the spatial relationship between events and the cross-domain environment of the behavior, resulting in poor performance in practical applications. In addition, the existing methods are mostly based on Euclidean space and hyperbolic space in terms of feature space relationship representation and do not fully consider the complexity of the relationship structure of anomalous events. In this paper, we propose a cross-domain spatio-temporal abnormal events prediction method, referred to as CDSTAEP. This method divides the local event sequence based on the temporal behavior sequence of entities and realizes the graphical representation of the multi-domain event correlation relationship. In the mixed-curvature space, we realize the representation learning of the correlation relationship of complex events and combine the event mixed-curvature vector representation and attention-based long short-term memory (LSTM-ATT) to capture the spatial and temporal correlation characteristics of cross-domain events, and finally realize the prediction. In this paper the proposed CDSTAEP is verified with the live network data set collected by a national key research and development plan. The results demonstrate that CDSTAEP can retain more spatial relationship features between events, the area under roc curve (AUC) score is better than the result of single-space representation and is 4.53% and 6.699% higher than the baseline models such as LSTM and LSTM-ATT.

Cross-domain identity association of network entities is a significant research challenge and a vital issue of practical value in relationship discovery and service recommendation between things in the Internet of things, cyberspace resources surveying mapping, threat tracking, and intelligent recommendation. This task usually adds additional difficulty to the research in practical applications due to the need to link across multiple platforms. The existing entity identity association methods in cross-domain networks mainly use the attribute information, generated content, and network structure information of network user entities but do not fully use the inherent strong positioning characteristics of active nodes in the network. In this article, we analyzed the structural characteristics of existing relational networks. We found that the hub node has the role of identity association positioning, and the importance of identity association reflected by different nodes is different. Moreover, we creatively designed a network representation learning method. We proposed a supervised learning identity association model combined with a representation learning method. Experiments on the public data set show that using the identity association method proposed in this article, the ranking accuracy of user entity association similarity is about 30% and 25% higher than the existing two typical methods.