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BGP is the de facto inter-domain routing protocol to ensure global connectivity of the Internet. However, various reasons, such as deliberate attacks or misconfigurations, could cause BGP routing anomalies. Traditional methods for BGP routing anomaly detection require significant manual investigation of routes by network operators. Although machine learning has been applied to automate the process, prior arts typically impose significant training overhead (such as large-scale data labeling and feature crafting), and only produce uninterpretable results. To address these limitations, this paper presents a routing anomaly detection system centering around a novel network representation learning model named BEAM. The core design of BEAM is to accurately learn the unique properties (defined as \emph{routing role}) of each Autonomous System (AS) in the Internet by incorporating BGP semantics. As a result, routing anomaly detection, given BEAM, is reduced to a matter of discovering unexpected routing role churns upon observing new route announcements. We implement a prototype of our routing anomaly detection system and extensively evaluate its performance. The experimental results, based on 18 real-world RouteViews datasets containing over 11 billion route announcement records, demonstrate that our system can detect all previously-confirmed routing anomalies, while only introducing at most five false alarms every 180 million route announcements. We also deploy our system at a large ISP to perform real-world detection for one month. During the course of deployment, our system detects 497 true anomalies in the wild with an average of only 1.65 false alarms per day., Comment: To be published in USENIX Security 2024

Machine learning (ML) is promising in accurately detecting malicious flows in encrypted network traffic; however, it is challenging to collect a training dataset that contains a sufficient amount of encrypted malicious data with correct labels. When ML models are trained with low-quality training data, they suffer degraded performance. In this paper, we aim at addressing a real-world low-quality training dataset problem, namely, detecting encrypted malicious traffic generated by continuously evolving malware. We develop RAPIER that fully utilizes different distributions of normal and malicious traffic data in the feature space, where normal data is tightly distributed in a certain area and the malicious data is scattered over the entire feature space to augment training data for model training. RAPIER includes two pre-processing modules to convert traffic into feature vectors and correct label noises. We evaluate our system on two public datasets and one combined dataset. With 1000 samples and 45% noises from each dataset, our system achieves the F1 scores of 0.770, 0.776, and 0.855, respectively, achieving average improvements of 352.6%, 284.3%, and 214.9% over the existing methods, respectively. Furthermore, We evaluate RAPIER with a real-world dataset obtained from a security enterprise. RAPIER effectively achieves encrypted malicious traffic detection with the best F1 score of 0.773 and improves the F1 score of existing methods by an average of 272.5%.

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this paper, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of our system components and the integrated system. IEEE

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this paper, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of our system components and the integrated system. IEEE

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this paper, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of our system components and the integrated system. IEEE

A high-energy liquid-jet hammer with specially designed backward stroke end buffer structure was investigated computationally. Computational Fluid Dynamics (CFD) with the technique of dynamic and sliding meshes method was employed in this study. Results indicated that each of the geometric parameter of the buffer structure had a significant effect on the backward impacting energy of the impact body and brought a maximum of 49.8 % of backward impacting energy reduction. Experimental tests based on the non-contact measuring method were conducted to verify the simulation results, by which the accuracy and reliability of this CFD simulation method was proved. In addition, the high-energy liquid-jet hammer worked well with the optimal parameters of the buffer structure in bench testing and reached high penetration rate in a drilled borehole.