As a policy-based routing protocol, the primary purpose of Border Gateway Protocol (BGP) is to exchange routing reachability information to provide sufficient end-to-end Quality-of-Service (QoS). The constant increase of anomalous traffic of BGP affects the connectivity and reachability of routing information among different Autonomous Systems (ASs), which calls for building accurate alerting models to provide stable routing services in the Internet. The previous works classify anomalies without considering the characteristic of multiple time scales, which may lead to inaccurate classification. In this paper, we propose a novel Multi-Scale Long Short-Term Memory (MSLSTM) model to capture the anomalous behaviors from BGP traffic. In our model, a Discrete Wavelet Transform is used to obtain temporal information on multiple scales, and a hierarchical two-layer LSTM architecture is devised where the first layer learns the attentions of different time scales to generate an integrated historical representation, and the second layer captures the temporal dependency in the learned representation. To evaluate the feasibility in different alerting scenarios, we conduct comprehensive experiments based on several BGP data sets collected from real world applications. The results demonstrate that our model achieves a promising performance compared with the state-of-the-art approaches.

Workflow scheduling problem (WSP) is a well-known combinatorial optimization problem, which is defined to assign a series of interconnected tasks to the available resources to meet user defined Quality of Service (QoS). Traditional guided random search methods and heuristic based methods are either require expensive computational cost or heavily rely on human's empirical knowledge, which makes them inconvenient for practical applications. To address the above issues, this paper proposes a cooperative coevolution hyper-heuristic framework to solve WSP with an objective of minimizing the completed time of workflow. In particular, in the proposed framework, two hyper-heuristics, namely, the task selection rule (TSR) and the resource selection rule (RSR), are learned automatically by a cooperative coevolution genetic programming algorithm. The TSR is used to select a ready task for scheduling, while the RSR is used to allocate resources to perform the selected task.To validate the effectiveness of the proposed framework, randomly generated workflow instances and four real-world workflows are used as test cases. Compared with several state-of-the-art methods, the hyper-heuristics found by our proposed framework demonstrate superior performance on all the test cases in terms of metrics including the schedule length ratio, speedup and efficiency

Network representation learning (NRL) has far-reaching effects on data mining research, showing its importance in many real-world applications. NRL, also known as network embedding, aims at preserving graph structures in a low-dimensional space. These learned representations can be used for subsequent machine learning tasks, such as vertex classification, link prediction, and data visualization. Recently, graph convolutional network (GCN)-based models, e.g., GraphSAGE, have drawn a lot of attention for their success in inductive NRL. When conducting unsupervised learning on large-scale graphs, some of these models employ negative sampling (NS) for optimization, which encourages a target vertex to be close to its neighbors while being far from its negative samples. However, NS draws negative vertices through a random pattern or based on the degrees of vertices. Thus, the generated samples could be either highly relevant or completely unrelated to the target vertex. Moreover, as the training goes, the gradient of NS objective calculated with the inner product of the unrelated negative samples and the target vertex may become zero, which will lead to learning inferior representations. To address these problems, we propose an adversarial training method tailored for unsupervised inductive NRL on large networks. For efficiently keeping track of high-quality negative samples, we design a caching scheme with sampling and updating strategies that has a wide exploration of vertex proximity while considering training costs. Besides, the proposed method is adaptive to various existing GCN-based models without significantly complicating their optimization process. Extensive experiments show that our proposed method can achieve better performance compared with the state-of-the-art models.

Internet platforms provide new ways for people to share experiences, generating massive amounts of data related to various real-world concepts. In this paper, we present an event detection framework to discover real-world events from multiple data domains, including online news media and social media. As multi-domain data possess multiple data views that are heterogeneous, initial dictionaries consisting of labeled data samples are exploited to align the multi-view data. Furthermore, a shared multi-view data representation (SMDR) model is devised, which learns underlying and intrinsic structures shared among the data views by considering the structures underlying the data, data variations, and informativeness of dictionaries. SMDR incorporates various constraints in the objective function, including shared representation, low-rank, local invariance, reconstruction error, and dictionary independence constraints. Given the data representations achieved by SMDR, class-wise residual models are designed to discover the events underlying the data based on the reconstruction residuals. Extensive experiments conducted on two real-world event detection datasets, i.e., Multi-domain and Multi-modality Event Detection dataset, and MediaEval Social Event Detection 2014 dataset, indicating the effectiveness of the proposed approaches.

The development of the Industrial Internet will generate a large amount of valuable data, known as industrial big data (IBD). By mining and utilizing IBD, enterprises can improve production efficiency, reduce costs and risks, optimize management processes, and innovate services and business models. However, IBD comes from various institutions in all walks of life and has features such as multisource, heterogeneity, and multimodality. And data sharing and trading (DS&T) occur in the Industrial Internet environment without mutual trust. These characteristics pose new challenges to analytics methods and privacy and security protection technologies. Therefore, this article aims to provide references for privacy-preserving and secure industrial big data analytics (IBDA) from three perspectives: 1) research framework; 2) platform architecture; and 3) key technologies. First, we review the current state of research on theories and technologies related to IBDA. Then, we reveal three challenges to secure and efficient IBDA. We take the analytics and utilization of IBD as systematic engineering, propose the research framework for privacy-preserving and secure IBDA, and point out the specific content to be studied. Further, we design the architecture of the IBDA platform with the idea of layering, including a function model, security architecture, and system architecture. Finally, detailed research proposals and potential technologies for IBD analytics and utilization are presented from three aspects: 1) data fusion and analytics; 2) data privacy and security protection; and 3) blockchain.

Precise destination prediction from partial trajectories have a huge potential impact on intelligent location-based approaches. Traditional prediction approaches, which treat trajectories as one-dimensional sequences and process them in a single scale, fail to capture diverse and rich two-dimensional patterns of trajectories in different spatial scales. Meanwhile, most models treat each portion of a trajectory equally in terms of contributing to final destination prediction. This is in conflict with our observation that there exists some albeit small local areas playing much more important roles for destination prediction than the others. To address these problems, we propose a novel prediction algorithm T-CONV, which models trajectories as two-dimensional images, and then feed them into a convolutional neural network (CNN) architecture to extract multi-scale patterns for precise destination prediction. Furthermore, we propose a method to extract regions with different relevance for final output of T-CONV, and further explore the local patterns of important regions by integrating multi-scope local-enhancement areas based on attention mechanism. The comprehensive experiments based on two large-scale real taxi trajectory datasets show that T-CONV can achieve higher accuracy than the state-of-the-art methods, demonstrating the strength of the multi-scale and multi-scope feature extraction mechanisms in trajectory mining.