With the continuous development of network technology, the diversity of network traffic constantly increased (intra-class diversity). Nevertheless, the boundary between malicious and benign actions became even ambiguous (inter-class similarity), causing lots of false detection and hindering the further optimization of the detection model. Focusing on challenges brought by intra-class diversity and inter-class similarity, we proposed a novel approach to enhance intrusion detection based on contrastive learning, which can make the right decision while disentangling samples from different classes. First, to bridge the gap when applying contrastive learning to intrusion detection data, we proposed a heuristic method to build contrastive tasks based on random masking of network packet sequences, which can reflect semantic relationships among samples. Then contrastive loss can be calculated to measure the inter-class and intra-class distances. Second, contrastive cross-entropy loss was proposed, which was a combination of contrastive loss and classification loss. Together with a dual branch deep structure, we can optimize the detection and sample distance requirements at the same time. Thirdly, experiments were conducted on diverse real-world and benchmark datasets using different model architectures under various parameter settings. Results on real-world dataset showed that our methods could stable experience a 5% increase in accuracy, and an 8% improvement in detection rate on those easily misdetected scenarios. To verify the method’s effectiveness on different traffic representations, we further conducted experiments on NSL-KDD and UNSW-NB15, which achieved a 7% accuracy improvement on NSL-KDD and a 6% accuracy improvement on UNSW-NB15. Extensive comparison with state of art intrusion detection models in recent five years showed that the proposed methods could effectively improve the accuracy of detection models.

As a lightweight, flexible, and high-performance operating system virtualization, containers are used to speed up the big data platform. However, due to the imperfection of the resource isolation mechanism and the property of shared kernel, the meltdown and spectre attacks can lead to information leakage of kernel space and coresident containers. In this article, a noise-resilient and real-time detection system, named ContainerGuard, is proposed to detect meltdown and spectre attacks in the container-based big data platform. ContainerGuard uses a nonintrusive manner to collect lifecycle multivariate time-series performance event data of processes in containers and then uses ensemble of variational autoencoders as generative neural networks to learn the robust representations of normal patterns. Therefore, ContainerGuard meets the urgent need for information protection in the container-based big data platform. Our evaluations using real-world datasets show that ContainerGuard achieves excellent detection performance and only introduces about 4.5% of running performance overhead to the platform.

Online users are typically active on multiple social media networks (SMNs), which constitute a multiplex social network. With improvements in cybersecurity awareness, users increasingly choose different usernames and provide different profiles on different SMNs. Thus, it is becoming increasingly challenging to determine whether given accounts on different SMNs belong to the same user; this can be expressed as an interlayer link prediction problem in a multiplex network. To address the challenge of predicting interlayer links, feature or structure information is leveraged. Existing methods that use network embedding techniques to address this problem focus on learning a mapping function to unify all nodes into a common latent representation space for prediction; positional relationships between unmatched nodes and their common matched neighbors (CMNs) are not utilized. Furthermore, the layers are often modeled as unweighted graphs, ignoring the strengths of the relationships between nodes. To address these limitations, we propose a framework based on multiple types of consistency between embedding vectors (MulCEVs). In MulCEV, the traditional embedding-based method is applied to obtain the degree of consistency between the vectors representing the unmatched nodes, and a proposed distance consistency index based on the positions of nodes in each latent space provides additional clues for prediction. By associating these two types of consistency, the effective information in the latent spaces is fully utilized. In addition, MulCEV models the layers as weighted graphs to obtain representation. In this way, the higher the strength of the relationship between nodes, the more similar their embedding vectors in the latent representation space will be. The results of our experiments on several real-world and synthetic datasets demonstrate that the proposed MulCEV framework markedly outperforms current embedding-based methods, especially when the number of training iterations is small.

The integration of the space–air–ground-integrated network and the Internet of Vehicles (IoV) enables the IoV to achieve full network coverage and better network performance. However, the large scale of the network and the complex cooperation mechanism make the credibility of the nodes in the network and the service delivery questioned. In this article, the hardware trusted module is used as the root of trust to build the trust chain and the trusted running environment and provide protection and trusted state attestation for services. In order to overcome the large-scale and high-concurrency performance bottlenecks in the remote verification of trusted states in the IoV, a novel batch remote approach for trusted states is proposed. The simulation results show that the proposed approach can effectively attest to the trusted state of each network node and virtual service in the IoV and enhance the trustworthiness of the network.

Many researchers from a variety of fields, including computer science, network science, and mathematics, have focused on how to contain the outbreaks of Internet misinformation that threaten social systems and undermine societal health. Most research on this topic treats the connections among individuals as static, but these connections change in time, and thus social networks are also temporal networks. Currently, there is no theoretical approach to the problem of containing misinformation outbreaks in temporal networks. We thus propose a misinformation spreading model for temporal networks and describe it using a new theoretical approach. We propose a heuristic-containing (HC) strategy based on optimizing the final outbreak size that outperforms simplified strategies such as those that are random-containing and targeted-containing. We verify the effectiveness of our HC strategy on both artificial and real-world networks by performing extensive numerical simulations and theoretical analyses. We find that the HC strategy dramatically increases the outbreak threshold and decreases the final outbreak threshold.

The development of information technology and mobile Internet has spawned the prosperity of online social networks. As the world’s largest microblogging platform, Twitter is popular among people all over the world. However, as the number of users on Twitter increases, rumors have become a serious problem. Therefore, rumor detection is necessary since it can prevent unverified information from causing public panic and disrupting social order. Cantonese is a widely used language in China. However, to the best of our knowledge, little research has been done on Cantonese rumor detection. In this paper, we propose a novel hybrid model XGA (namely XLNet-based Bidirectional Gated Recurrent Unit (BiGRU) network with Attention mechanism) for Cantonese rumor detection on Twitter. Specifically, we take advantage of both semantic and sentiment features for detection. First of all, XLNet is employed to produce text-based and sentiment-based embeddings at the character level. Then we perform joint learning of character and word embeddings to obtain the words’ external contexts and internal structures. In addition, we leverage BiGRU and the attention mechanism to obtain important semantic features and use the Cantonese rumor dataset we constructed to train our proposed model. The experimental results show that the XGA model outperforms the other popular models in Cantonese rumor detection. The research in this paper provides methods and ideas for future work in Cantonese rumor detection on other social networking platforms.