While the anonymous communication system Tor can protect user privacy, website fingerprinting (WF) attackers can still identify the websites that users access over encrypted network connections by analyzing the metadata generated during network communication. Despite the emergence of new WF attack techniques in recent years, most research in this area has focused on pure traffic traces generated from single-tab browsing behavior. However, multi-tab browsing behavior significantly degrades the performance of WF classification models based on the single-tab assumption. As a result, some research has shifted its focus to multi-tab WF attacks, although most of these works have limited utilization of the mixed information contained in multi-tab traces. In this paper, we propose an end-to-end multi-tab WF attack model, called Transformer-based model for Multi-tab Website Fingerprinting attack (TMWF). Inspired by object detection algorithms in computer vision, we treat multi-tab WF recognition as a problem of predicting ordered sets with a maximum length. By adding enough single-tab queries to the detection model and letting each query extract WF features from different positions in the multi-tab traces, our model's Transformer architecture capitalizes more fully on trace features. Paired with our new proposed model training approach, we accomplish adaptive recognition of multi-tab traces with varying numbers of web pages. This approach successfully eliminates a strong and unrealistic assumption in the field of multi-tab WF attacks - that the number of tabs contained in a sample belongs to the attacker's prior knowledge. Experimental results in various scenarios demonstrate that the performance of TMWF is significantly better than existing multi-tab WF attack models. To evaluate model performance in more authentic scenarios, we present a dataset of multi-tab trace data collected from real open-world environments.

In recent years, the security of cyberphysical system (CPS) has been focused on increasingly. The most common example of CPS is industrial control system (ICS), which is prevalent in almost every critical infrastructure, such as electricity, oil and gas, water, chemical processing, and healthcare. So ICS security has become a top priority in the security field. Based on a general description of the wireless sensor network (WSN), which is an important element of CPS, this paper first gives a comprehensive and deep understanding of CPS. Secondly, it provides a comprehensive description of the current situation of ICS security in the U.S. and the corresponding approaches the U.S. government and some industries have taken, including management, technology, standards and regulations, and researches of national laboratories. Thirdly, the paper shows the research on ICS in Europe, focusing on the most important report issued by ENISA. Then, compared with developed countries, it presents the grim situation of ICS security and describes the efforts of ICS security management in China.