With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to system security. However, current detection methods exhibit limitations, leading to compromises in accuracy and efficiency. To cope with it, three key strategies are implemented in this paper: (i) Using tensors to model large-scale and heterogeneous data in complex networks; (ii) Proposing a denoising algorithm based on the improved and distributed tensor train (IDTT) decomposition, which optimizes the tensor train(TT) decomposition in terms of parallel computation and low-rank estimation; (iii) Combining (i), (ii) and Light Gradient Boosting Machine (LightGBM) classification model, an efficient DDoS attack detection framework is proposed. Datasets CIC-DDoS2019 and NSL-KDD are used to evaluate the framework, and results demonstrate that accuracy can reach 99.19% while having the characteristics of low storage consumption and well speedup ratio.

With the rapid advancement of 5G communication technology in the era of big data, massive terminal devices connected to the Internet have dramatically increased the scale of network, generating a large amount of high-dimensional and heterogeneous information. This not only enhances the difficulty of information processing in the network, but also poses a severe challenge to data storage and calculation, which has become a big data problem to be solved urgently. To cope with it, this paper proposes an efficient information processing framework and applies it to Distributed Denial of Service (DDoS) attack detection. Overall, three major highlights are made: (i) Tensor is used to represent multi-modal information in large-scale networks; (ii) A novel denoising algorithm based on tensor train(TT) decomposition is proposed, focused on optimizing both computation and correlation; (iii) A big data-driven information processing framework is developed, which includes information preprocessing, denoising and classification. Results in case study indicate that the framework can achieve an accuracy of 99.19%, all while maintaining the great storage advantage, well speedup ratio and strong computing capabilities under the same computational complexity. It can also be generalized to other network data processing scenarios.

With the bloom of intelligent devices over the world, a large scale of user’s trajectory data are collected. How to mine valuable rules from these data and provide services for the industrial community has become an urgent problem. In this article, we propose a multimodal prediction system to infer users’ mobility pattern embedded in heterogeneous data from cyber–physical–social space. According to users’ mobility pattern, the framework can provide smart services for the industrial community. The highlight is the retrospective multivariate multistep transition tensor (${\text{M}^2}{\text{T}^2}$) chain model, which decomposes a large scale of${\text{M}^2}{\text{T}^2}$into a series of small-scale transition tensors (subtransition tensors) with the tensor maximum likelihood estimation method. Then, that one can solve the stationary probability distribution with the small-scale subtransition tensors so as to highly reduce the computation and storage cost. At the same time, the tensor maximum likelihood estimation method avoids the overfitting of${\text{M}^2}{\text{T}^2}$, so the proposed model improves the performance of prediction systems. In the end, several experiments are constructed to evaluate the proposed model.

With the development of GPS technology, a new Mobile Internet of Things (M-IoT) is emerging, which perceives the city's rhythm and pulse day and night to collect a large scale of city data. It is urgent to innovate M-IoT service system for these large-scale and heterogeneous data. To cope with the problem, this article proposes a Mobile-IoT based multi-modal reinforcement learning service framework from data perspective, which has three highlights, i) Developing Action-aware High-order Transition Tensor (AHTT) to fuse the heterogeneous data from M-IoTs in a unified form. ii) Developing Multi-modal Markov Decision Process (MMDP) to model the multi-modal reinforcement learning for M-IoT service framework. iii) Developing Tensor Policy Iteration algorithm (TPIA) to solve the optimal tensor policy. Due to using tensor keeps the multi-modal relations of the context information in the process of solving the optimal policy. The proposed M-IoT service system provides more personalized service for taxi drivers. The experiment results shows that most taxi drivers earn more revenue according to the tensor policy.