Data heterogeneity is one of the key challenges in federated learning, and many efforts have been devoted to tackling this problem. However, distributed concept drift with data heterogeneity, where clients may additionally experience different concept drifts, is a largely unexplored area. In this work, we focus on real drift, where the conditional distribution $P(\mathcal{Y}|\mathcal{X})$ changes. We first study how distributed concept drift affects the model training and find that local classifier plays a critical role in drift adaptation. Moreover, to address data heterogeneity, we study the feature alignment under distributed concept drift, and find two factors that are crucial for feature alignment: the conditional distribution $P(\mathcal{Y}|\mathcal{X})$ and the degree of data heterogeneity. Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment. To enhance collaboration under distributed concept drift, FedCCFA clusters local classifiers at class-level and generates clustered feature anchors according to the clustering results. Assisted by these anchors, FedCCFA adaptively aligns clients' feature spaces based on the entropy of label distribution $P(\mathcal{Y})$, alleviating the inconsistency in feature space. Our results demonstrate that FedCCFA significantly outperforms existing methods under various concept drift settings. Code is available at https://github.com/Chen-Junbao/FedCCFA.

With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the “Why? → What? → How?” research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews.

It is a wide-spread way to detect malware by analyzing its behavioral characteristics based on API call sequences. However, previous studies usually just focus on its static or dynamic API call sequence, while neglecting the correlation between them. Our experimental results show that there exists an underlying relation between the dynamic and static API call sequences of malware. The relation can be described as “the syntax is different, but the semantics is similar”. Based on this discovery, this paper first attempts to explore the difference and relation between the static and dynamic API sequences of malicious programs. We correlate and fuse their dynamic and static API sequences into one hybrid sequence based on semantics mapping and then construct the hybrid feature vector space. Furthermore, we mine and define the malicious behavior types of the programs, and provide explainable results for malware detection. Our study has addressed the shortcoming of the previous approaches that they usually pay attention to detection but neglect explanation. By correlation and fusion of the static and dynamic API sequences, we establish an explainable malware detection framework, called MalDAE. The evaluation results show that the detection and classification accuracy of MalDAE can reach up to 97.89% and 94.39% respectively outperforming the previous similar studies by comprehensive comparison. In addition, MalDAE gives an understandable explanation for common types of malware and provides predictive support for understanding and resisting malware.