Cyber attacks have become increasingly complicated, persistent, organized, and weaponized. Faces with this situation, drives a rising number of organizations across the world are showing a growing willingness to leverage the open exchange of cyber threat intelligence (CTI) for obtaining a full picture of the fast-evolving cyber threat situation and protecting themselves against cyber-attacks. However, modeling CTI is challenging due to the explicit and implicit relationships among CTI and the heterogeneity of cyber-threat infrastructure nodes involved in CTI. Owing to the limited labels of cyber threat infrastructure nodes involved in CTI, automatically identifying the threat type of infrastructure nodes for early warning is also challenging. To tackle these challenges, a practical system called HinCTI is developed for modeling cyber threat intelligence and identifying threat types. We first design a threat intelligence meta-schema to depict the semantic relatedness of infrastructure nodes. We then model cyber threat intelligence on heterogeneous information network (HIN), which can integrate various types of infrastructure nodes and rich relations among them. Following, we define a meta-path and meta-graph instances-based threat Infrastructure similarity (MIIS) measure between threat infrastructure nodes and present a MIIS measure-based heterogeneous graph convolutional network (GCN) approach to identify the threat types of infrastructure nodes involved in CTI. Moreover, through the hierarchical regularization strategy, our model can alleviate the problem of overfitting and achieve good results in the threat type identification of infrastructure nodes. To the best of our knowledge, this work is the first to model CTI on HIN for threat identification and propose a heterogeneous GCN-based approach for threat type identification of infrastructure nodes. With HinCTI , comprehensive experiments are conducted on real-world datasets, and experimental results demonstrate that our proposed approach can significantly improve the performance of threat type identification compared to the existing state-of-the-art baseline methods. Our work is beneficial to greatly relieve security analysts from heavy analysis work and efficiently protect organizations against cyber-attacks.

Facing multidemand tasks and massive heterogeneous resources in an IoT edge computing environment, it is a challenge to obtain reliable and quick response service and allocate application tasks to resource nodes that meet task requirements and user preference. Since IoT edge computing is facing different types of severe attacks, such as message attacks, swing attacks, collusion attack, node attacks, etc., providing a reliable service environment, trust evaluation between edge nodes is necessary. Existing trust computing schemes, however, suffer from a long response period and low malicious detection rate in a dynamic environment. To alleviate these issues, we propose a reliable and efficient task offloading strategy based on the multifeedback trust mechanism (TOSMFTM). First, a reliable and efficient architecture of TOSMFTM is established, which can effectively improve the ability of trust computing and task offloading. Second, according to the broker’s dynamic monitoring of data, a multifeedback trust aggregation model based on time attenuation and interaction frequency is proposed to provide a trusted running environment. Third, a trust weight $k$ -means (TWK-means) clustering algorithm is designed based on resource attributes to enhance the reliability of service, and quickly and accurately cluster out resource nodes required by the task. Finally, we construct a task offloading model based on trust clustering to ensure user experience quality and promote system efficiency. Different from existing task processing models, which only focus on task offloading, our method also carries out resource preprocessing, trust evaluation, and resource clustering before task processing. The experiment verifies the effectiveness and feasibility of our TOSMFTM scheme.

Social media have become increasingly popular for the sharing and spreading of usergenerated content due to their easy access, fast dissemination, and low cost. Meanwhile, social media also enable the wide propagation of cyber frauds, which leverage fake information sources to reach an ulterior goal. The prevalence of untrustworthy information sources on social media can have significant negative societal effects. In a trustworthy social media system, trust calculation technology has become a key demand for identification of information sources. Trust, as one of the most complex concepts in network communities, has multi-criteria properties. However, existing work only focuses on single trust factor, and does not fully consider the complexity of trust relationships in social computing. In this paper, a multi-criteria trustworthiness calculation mechanism called Info-Trust is proposed for information sources, in which identity-based trust, behavior-based trust, relation-based trust, and feedback-based trust factors are incorporated to present an accuracy-enhanced full view of trustworthiness evaluation of information sources. More importantly, the weights of these factors are dynamically assigned by the ordered weighted averaging and weighted moving average (OWA-WMA) combination algorithm. This mechanism surpasses the limitations of existing approaches in which weights are assigned subjectively. Experimental results based on real-world datasets from SinaWeibo demonstrate that the proposed mechanism achieves greater accuracy and adaptability in trustworthiness identification of network information.

In mobile ad hoc networks (MANETs), if mobile devices frequently leave or join the overlay of MANETs, the communication links between mobile devices or between mobile devices and cloud will lose or reestablish; for searching and routing the needed cloud services again, more energy will be consumed. 5G is the fifth generation mobile communication technology, and fog computing is defined as a distributed computing infrastructure that is able to handle billions of Internet-connected devices. Therefore, combining fog computing with 5G, we present a novel and effective dynamic cloudlet-assisted routing mechanism (DCRM) for MANETs to solve the energy-saving problem of link breakages. First, for every mobile device in MANETs, we build a temporary file to record its identity and route information in a certain time. Moreover, as a key promising technology of 5G, device-to-device is used as the communication way between mobile devices, because it can enhance the communication ability and the information sharing ability between mobile devices. Second, cloudlets can be considered as small data centers, and we set the sharing relation table and the cooperation mechanism between cloudlets. Then, relying on these, mobile devices can quickly route and search the requested services regardless of the frequent movement of mobile devices in MANETs. The experimental results show that DCRM can save more time and several times more energy, and display more advantages than the network model without the proposed mechanism in many ways, consequently enable cloud to provide services that are more realistic for the future mobile network applications.

Data aggregation is a critical operation in wireless sensor networks(WSNs). Many applications have strict requirements for the latency of data aggregation. This paper focuses on the latency problem of data aggregation. Two factors determine the latency of data aggregation. First, because of the existence of interference, efficient collision-free scheduling is crucial for reducing data aggregation latency. Second, the tree structure has an important impact on data aggregation latency. In this paper, we propose a novel approach called distributed and efficient data aggregation scheduling over multi-channel links (DEDAS-MC). DEDAS-MC minimizes the latency in routing the aggregated data to the sink over multi-channel links. In DEDAS-MC, we first present a scheduling algorithm to schedule sensors to avoid interference and minimize the latency of data aggregation on a given tree. Then, a distributed algorithm for constructing minimum-latency data aggregation trees is proposed by employing the Markov approximation method. In DEDAS-MC, the value of β is adaptive. The Markov approximation method based adaptive-β is more flexible and efficient than the single β approximation. Experiments show that DEDAS-MC outperforms the existing competing schemes.