Runtime log-based anomaly detection is one of several key building blocks in ensuring system security, as well as post-incident forensic investigations. However, existing log-based anomaly detection approaches that are implemented on large-scale Internet of Things (IoT) systems generally upload local data from edge devices to a centralized (cloud) server for processing and analysis. Such a workflow incurs significant communication and computation overheads, with potential privacy implications. Hence, in this paper, we propose a customizable and communication-efficient federated anomaly detection scheme (hereafter referred to as FedLog), designed to facilitate the identification of abnormal log patterns in large-scale IoT systems. Specifically, we first craft a Temporal Convolutional Network-Attention Mechanism-based Convolutional Neural Network (TCN-ACNN) model, to effectively extract fine-grained features from system logs. Second, we develop a new federated learning framework to support IoT devices in establishing a comprehensive anomaly detection model in a collaborative and privacy-preserving manner. Third, a lottery ticket hypothesis based masking strategy is designed to achieve customizable and communication-efficient federated learning in handling non-Independent and Identically Distributed (non-IID) log datasets. We then evaluate the performance of our proposed scheme with those of DeepLog (published in CCS, 2017) and Loganomaly (published in IJCAI, 2019) in both centralized learning and federated learning settings, using two publicly available and widely used real-world datasets (i.e., HDFS and BGL). The findings demonstrate the utility of the proposed FedLog scheme, in terms of log-based anomaly detection.

Encrypting network traffic is an effective means of safeguarding user privacy and sensitive information. However, it also introduces potential vulnerabilities that can be exploited by network attackers, posing significant security risks to the Internet. In response to the challenge of low accuracy in existing methods for classifying encrypted malicious traffic, we propose a novel approach named MTSecurity, which leverages Transformer and Graph Neural Network technologies. This method automatically extracts raw byte features and graph-based traffic interaction features from encrypted malicious flows, combining them to substantially enhance the classification accuracy of encrypted malicious traffic. Furthermore, we introduce a graph structure called the Malicious Traffic Interaction Graph (MTIG) for representing encrypted malicious traffic. MTIG is based on the client-server interaction process and incorporates multidimensional traffic features. Experimental results demonstrate that the proposed MTSecurity model consistently performs well across different datasets, surpassing state-of-the-art methods. It achieves an accuracy of 0.9946 and an F1 score of 0.9940 on the MCFP dataset, and an accuracy of 0.9948 with an F1 score of 0.9934 on the USTC-TFC dataset.

Tumor-associated macrophages (TAMs) promote tumor proliferation, invasion, angiogenesis, stemness, therapeutic resistance, and immune tolerance in a protein-dependent manner. Therefore, the traditional target paradigms are often insufficient to exterminate tumor cells. These pro-tumoral functions are mediated by the subsets of macrophages that exhibit canonical protein markers, while simultaneously having unique transcriptional features, which makes the proteins expressed on TAMs promising targets during anti-tumor therapy. Herein, TAM-associated protein-dependent target strategies were developed with the aim of either reducing the numbers of TAMs or inhibiting the pro-tumoral functions of TAMs. Furthermore, the recent advances in TAMs associated with tumor metabolism and immunity were extensively exploited to repolarize these TAMs to become anti-tumor elements and reverse the immunosuppressive tumor microenvironment. In this review, we systematically summarize these current studies to fully illustrate the TAM-associated protein targets and their inhibitors, and we highlight the potential clinical applications of targeting the crosstalk among TAMs, tumor cells, and immune cells in anti-tumor therapy.

Identification of bird species from audio recordings has been a major area of interest within the field of ecological surveillance and biodiversity conservation. Previous studies have successfully identified bird species from given recordings. However, most of these studies are only adaptive to low‐noise acoustic environments and the cases where each recording contains only one bird's sound simultaneously. In reality, bird audios recorded in the wild often contain overlapping signals, such as bird dawn chorus, which makes audio feature extraction and accurate classification extremely difficult. This study is the first to focus on applying a blind source separation method to identify all foreground bird species contained in overlapping vocalization recordings. The proposed IVA‐Xception model is based on independent vector analysis and convolutional neural network. Experiments on 2020 Bird Sound Recognition in Complex Acoustic Environments competition (BirdCLEF2020) dataset show that this model could achieve a higher macro F1‐score and average accuracy compared with state‐of‐the‐art methods.

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average.