Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.

Location-based mobile marketing recommendation has become one of the hot spots in e-commerce. The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of users and shopping location information in the recommendation. This paper focuses on location feedback data of user and proposes a location-based mobile marketing recommendation model by convolutional neural network (LBCNN). First, the users’ location-based behaviors are divided into different time windows. For each window, the extractor achieves users’ timing preference characteristics from different dimensions. Next, we use the convolutional model in the convolutional neural network model to train a classifier. The experimental results show that the model proposed in this paper is better than the traditional recommendation models in the terms of accuracy rate and recall rate, both of which increase nearly 10%.

Federated learning (FL) is an emerging paradigm that allows participants to collaboratively train deep learning tasks while protecting the privacy of their local data. However, the absence of central server control in distributed environments exposes a vulnerability to data poisoning attacks, where adversaries manipulate the behavior of compromised clients by poisoning local data. In particular, data poisoning attacks against FL can have a drastic impact when the participant’s local data is non-independent and identically distributed (non-IID). Most existing defense strategies have demonstrated promising results in mitigating FL poisoning attacks, however, fail to maintain their effectiveness with non-IID data. In this work, we propose an effective defense framework, FL data augmentation (FLDA), which defends against data poisoning attacks through local data mixup on the clients. In addition, to mitigate the non-IID effect by exploiting the limited local data, we propose a gradient detection strategy to reduce the proportion of malicious clients and raise benign clients. Experimental results on datasets show that FLDA can effectively reduce the poisoning success rate and improve the global model training accuracy under poisoning attacks for non-IID data. Furthermore, FLDA can increase the FL accuracy by more than 12% after detecting malicious clients.

Human-centric computing is becoming an important part of data-driven artificial intelligence (AI) and the importance of data mining under Human-centric computing is getting more and more attention. The rapid development of machine learning has gradually increased its ability to mine data. In this paper, privacy protection is combined with machine learning, in which a logistic regression is adopted for local differential privacy protection to achieves classification task utilizing noise addition and feature selection. The design idea is mainly divided into three parts: noise addition, feature selection and logistic regression. In the part of noise addition, the way of adding noise using Laplace mechanism to original data achieves the purpose of disturbing data. The part of feature selection is to highlight the impact of noised data on the classifier. The part of logistic regression is to use logistic regression to implement classification task. The experimental results show that an accuracy of 85.7% can be achieved for the privacy data by choosing appropriate regularization coefficients.