ACM Transactions on Internet Technology最新文献

The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally results in the propagation of erroneous information, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of Coronavirus Disease (COVID-19), a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an “infodemic” by the World Health Organization (WHO), posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation. The research uses multiple deep learning model frameworks to detect misinformation in Chinese and English, and compare them based on different text feature selections. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The long and short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the bidirectional long and short-term memory (BiLSTM) model were selected for fake news detection. BiLSTM produces the best detection result, with detection accuracy reaching 94% for short-sentence English texts, and 99% for long-sentence English texts, while the accuracy for Chinese texts was 82%.

Cyber-manufacturing Systems (CMS) have been growing in popularity. Transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Thus, it is likely that this new manufacturing model will become increasingly popular shortly. By building new technologies on top of existing CMS, these systems ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim. There are still some challenges to overcome, such as ensuring data reliability, but these can be overcome with further research and development. In summary, the use of CMS can revolutionize the manufacturing industry. This paper comprehensively analyses these systems and their potential applications and implications. The article gives an overview of the field and then explores the various aspects of CMS in greater detail. A taxonomy of the most common and current approaches to cyber-manufacturing systems is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, the paper identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, the paper identifies several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. The paper provides a comprehensive overview of current technology and valuable insights into the potential impact of the technology on society and industry.

Darknets are probes listening to traffic reaching IP addresses that host no services. Traffic reaching a darknet results from the actions of internet scanners, botnets and possibly misconfigured hosts. Such peculiar nature of the darknet traffic makes darknets a valuable instrument to discover malicious online activities, e.g., identifying coordinated actions performed by bots or scanners. However, the massive amount of packets and sources that darknets observe makes it hard to extract meaningful insights, calling for scalable tools to automatically identify and group sources that share similar behaviour.

We here present i-DarkVec, a methodology to learn meaningful representations of Darknet traffic. i-DarkVec leverages Natural Language Processing techniques (e.g., Word2Vec) to capture the co-occurrence patterns that emerge when scanners or bots launch coordinated actions. As in NLP problems, the embeddings learned with i-DarkVec enable several new machine learning tasks on the darknet traffic, such as identifying clusters of senders engaged in similar activities.

We extensively test i-DarkVec and explore its design space in a case study using real darknets. We show that with a proper definition of services, the learned embeddings can be used to (i) solve the classification problem to associate unknown sources’ IP addresses to the correct classes of coordinated actors, and (ii) automatically identify clusters of previously unknown sources performing similar attacks and scans, easing the security analyst’s job. i-DarkVec leverages a novel incremental embedding learning approach that is scalable and robust to traffic changes, making it applicable to dynamic and large-scale scenarios.

Cyber-Manufacturing combines industrial big data with intelligent analysis to find and understand the intangible problems in decision-making, which requires a systematic method to deal with rich signal data. With the development of spectral detection and photoelectric imaging technology, spectral blind deconvolution has achieved remarkable results. However, spectral processing is limited by one-dimensional signal, there is no available structural information with little training samples. Moreover, in most practical applications, it is feasible to collect unpaired noise and clean spectrum. This training method of unpaired learning is practical and valuable. Therefore, a two-stage deconvolution scheme combining self supervised learning and feature extraction is proposed in this paper, which generates two complementary paired sets through self supervised learning to extract the final deconvolution network. In addition, a new deconvolution network is designed for feature extraction. The spectrum is pre-trained through spectral feature extraction and noise estimation network to improve the training efficiency and meet the assumed noise characteristics. Experimental results show that this method is effective in dealing with different types of synthetic noise.