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There is a new cache pollution attack in the information-centric network (ICN), which fills the router cache by sending a large number of requests for nonpopular content. This attack will severely reduce the router cache hit rate. Therefore, the detection of cache pollution attacks is also an urgent problem in the current information center network. In the existing research on the problem of cache pollution detection, most of the methods of manually setting the threshold are used for cache pollution detection. The accuracy of the detection result depends on the threshold setting, and the adaptability to different network environments is weak. In order to improve the accuracy of cache pollution detection and adaptability to different network environments, this paper proposes a detection algorithm based on gradient boost decision tree (GBDT), which can obtain cache pollution detection through model learning. Method. In feature selection, the algorithm uses two features based on node status and path information as model input, which improves the accuracy of the method. This paper proves the improvement of the detection accuracy of this method through comparative experiments.
Dapeng Man; Yongjia Mu; Jiafei Guo; Wu Yang; Jiguang Lv; Wei Wang. Cache Pollution Detection Method Based on GBDT in Information-Centric Network. Security and Communication Networks 2021, 2021, 1 -10.
AMA StyleDapeng Man, Yongjia Mu, Jiafei Guo, Wu Yang, Jiguang Lv, Wei Wang. Cache Pollution Detection Method Based on GBDT in Information-Centric Network. Security and Communication Networks. 2021; 2021 ():1-10.
Chicago/Turabian StyleDapeng Man; Yongjia Mu; Jiafei Guo; Wu Yang; Jiguang Lv; Wei Wang. 2021. "Cache Pollution Detection Method Based on GBDT in Information-Centric Network." Security and Communication Networks 2021, no. : 1-10.
Online social networks provide convenient conditions for the spread of rumors, and false rumors bring great harm to social life. Rumor dissemination is a process, and effective identification of rumors in the early stage of their appearance will reduce the negative impact of false rumors. This paper proposes a novel early rumor detection (ERD) model based on reinforcement learning. In the rumor detection part, a dual-engine rumor detection model based on deep learning is proposed to realize the differential feature extraction of original tweets and their replies. A double self-attention (DSA) mechanism is proposed, which can eliminate data redundancy in sentences and words at the same time. In the reinforcement learning part, an ERD model based on Deep Recurrent Q-Learning Network (DRQN) is proposed, which uses LSTM to learn the state sequence features, and the optimization strategy of the reward function is to take into account the timeliness and accuracy of rumor detection. Experiments show that, compared with existing methods, the ERD model proposed in this paper has a greater improvement in the timeliness and detection rate of rumor detection.
Wei Wang; Yuchen Qiu; Shichang Xuan; Wu Yang. Early Rumor Detection Based on Deep Recurrent Q-Learning. Security and Communication Networks 2021, 2021, 1 -13.
AMA StyleWei Wang, Yuchen Qiu, Shichang Xuan, Wu Yang. Early Rumor Detection Based on Deep Recurrent Q-Learning. Security and Communication Networks. 2021; 2021 ():1-13.
Chicago/Turabian StyleWei Wang; Yuchen Qiu; Shichang Xuan; Wu Yang. 2021. "Early Rumor Detection Based on Deep Recurrent Q-Learning." Security and Communication Networks 2021, no. : 1-13.
Network community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including “location label countercurrent” and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the “location label countercurrent” and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.
Huan Ma; Wei Wang. A Label Propagation Based User Locations Prediction Algorithm in Social Network. Communications in Computer and Information Science 2020, 165 -174.
AMA StyleHuan Ma, Wei Wang. A Label Propagation Based User Locations Prediction Algorithm in Social Network. Communications in Computer and Information Science. 2020; ():165-174.
Chicago/Turabian StyleHuan Ma; Wei Wang. 2020. "A Label Propagation Based User Locations Prediction Algorithm in Social Network." Communications in Computer and Information Science , no. : 165-174.
With the arrival of the Internet of Things (IoT) era and the rise of Big Data, cloud computing, and similar technologies, data resources are becoming increasingly valuable. Organizations and users can perform all kinds of processing and analysis on the basis of massive IoT data, thus adding to their value. However, this is based on data-sharing transactions, and most existing work focuses on one aspect of data transactions, such as convenience, privacy protection, and auditing. In this paper, a data-sharing-transaction application based on blockchain technology is proposed, which comprehensively considers various types of performance, provides an efficient consistency mechanism, improves transaction verification, realizes high-performance concurrency, and has tamperproof functions. Experiments were designed to analyze the functions and storage of the proposed system.
Shichang Xuan; Yibo Zhang; Hao Tang; Ilyong Chung; Wei Wang; Wu Yang. Hierarchically Authorized Transactions for Massive Internet-of-Things Data Sharing Based on Multilayer Blockchain. Applied Sciences 2019, 9, 5159 .
AMA StyleShichang Xuan, Yibo Zhang, Hao Tang, Ilyong Chung, Wei Wang, Wu Yang. Hierarchically Authorized Transactions for Massive Internet-of-Things Data Sharing Based on Multilayer Blockchain. Applied Sciences. 2019; 9 (23):5159.
Chicago/Turabian StyleShichang Xuan; Yibo Zhang; Hao Tang; Ilyong Chung; Wei Wang; Wu Yang. 2019. "Hierarchically Authorized Transactions for Massive Internet-of-Things Data Sharing Based on Multilayer Blockchain." Applied Sciences 9, no. 23: 5159.
Recently, co-clustering algorithms are widely used in heterogeneous information networks mining, and the distance metric is still a challenging problem. Bregman divergence is used to measure the distance in traditional co-clustering algorithms, but the hierarchical structure and the feature of the entity itself are not considered. In this paper, an agglomerative hierarchical co-clustering algorithm based on Bregman divergence is proposed to learn hierarchical structure of multiple entities simultaneously. In the aggregation process, the cost of merging two co-clusters is measured by a monotonic Bregman function, integrating heterogeneous relations and features of entities. The robustness of algorithms based on different divergences is tested on synthetic data sets. Experiments on the DBLP data sets show that our algorithm improves the accuracy over existing co-clustering algorithms.
Guowei Shen; Wu Yang; Wei Wang; Miao Yu; Guozhong Dong. Agglomerative Hierarchical Co-clustering Based on Bregman Divergence. Advances in Intelligent Systems and Computing 2014, 389 -398.
AMA StyleGuowei Shen, Wu Yang, Wei Wang, Miao Yu, Guozhong Dong. Agglomerative Hierarchical Co-clustering Based on Bregman Divergence. Advances in Intelligent Systems and Computing. 2014; ():389-398.
Chicago/Turabian StyleGuowei Shen; Wu Yang; Wei Wang; Miao Yu; Guozhong Dong. 2014. "Agglomerative Hierarchical Co-clustering Based on Bregman Divergence." Advances in Intelligent Systems and Computing , no. : 389-398.
Along with the widely using of microblog, third party services such as follower markets sell bots to customers to build fake influence and reputation. However, the bots and the customers that have large numbers of followers usually post spam messages such as promoted messages, messages containing malicious links. In this paper, we propose an effective approach for bots detection based on interaction graph model and BP neural network. We build an interaction graph model based on user interaction and design robust interaction-based features. We conduct a comprehensive set of experiments to evaluate the proposed features using different machine learning classifiers. The results of our evaluation experiments show that BP neural network classifier using our proposed features can be effectively used to detect bots compared to other existing state-of-the-art approaches.
Wu Yang; Guozhong Dong; Wei Wang; Guowei Shen; Liangyi Gong; Miao Yu; Jiguang Lv; Yaxue Hu. Detecting Bots in Follower Markets. Communications in Computer and Information Science 2014, 525 -530.
AMA StyleWu Yang, Guozhong Dong, Wei Wang, Guowei Shen, Liangyi Gong, Miao Yu, Jiguang Lv, Yaxue Hu. Detecting Bots in Follower Markets. Communications in Computer and Information Science. 2014; ():525-530.
Chicago/Turabian StyleWu Yang; Guozhong Dong; Wei Wang; Guowei Shen; Liangyi Gong; Miao Yu; Jiguang Lv; Yaxue Hu. 2014. "Detecting Bots in Follower Markets." Communications in Computer and Information Science , no. : 525-530.