Jinyuan Liang

University of British Columbia

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Zhijie Liu

University of Science and Technology Beijing

Thomas Pasquier

University of British Columbia

Qiujian Lv

Institute of Information Engineering

Degang Sun

Fudan University

Xueyuan Han

Wake Forest University

Yafeng Wu

Nanjing Medical University

Zijun Cheng

Institute of Information Engineering

Dan Feng

Wuhan University of Science and Technology

Erdal Kayacan

Paderborn University

Francesco Raimondo

University of Bristol

Cooperative Institutions

University of Bristol

University of Science and Technology Beijing

Chinese Academy of Sciences

Toshiba (United Kingdom)

Huazhong University of Science and Technology

University of Chinese Academy of Sciences

Institute of Information Engineering

Wuhan National Laboratory for Optoelectronics

Beijing University of Posts and Telecommunications

Aristotle University of Thessaloniki

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ANN-Based vibration control of an aerial refueling hose system with input nonlinearity and prescribed output constraint

Journal of the Franklin Institute (2022)

Zhijie Liu Jinyuan Liang Shuang Zhang Erdal Kayacan

Control-Lyapunov function

10.1016/j.jfranklin.2022.01.006

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Citations (4)

Vibration suppression and fault‐tolerant control of an aerial refueling hose with multiple actuators

Asian Journal of Control (2022)

Congcong Song Xiaobao Yan Qin Wang Jinyuan Liang Zhiji Han

Abstract In this paper, we propose an adaptive fault‐tolerant boundary vibration control approach for the flexible aerial refueling hose with variable length, variable speed, and multiple actuators. A distributed parameter system (DPS) is utilized to represent the dynamic behavior of the flexible refueling hose more precisely and accurately. Based on the established DPS model, we present a boundary vibration controller to suppress the vibration of the flexible refueling hose. In the controller, fault‐tolerant control with multiple actuators is considered to tackle the failure issues, and both multiplicative and additive failures are discussed in the fault situation. Then, by the Lyapunov direct method, we prove that the stability of the closed‐loop system is guaranteed under the proposed control approach. Numerical examples are presented to support the theoretical derivation.

Lyapunov stability

10.1002/asjc.2767

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Citations (2)

Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

2022 IEEE Symposium on Security and Privacy (SP) (2024)

Zijun Cheng Qiujian Lv Jinyuan Liang Yan Wang Degang Sun

Kairos

10.1109/sp54263.2024.00005

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Citations (8)

Vibration Control of a Probe-and-Drogue Refueling Hose System with Prescribed Bound

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) (2020)

Jinyuan Liang Zhijie Liu Xiuyu He Wei He

We develop a boundary control strategy for vibration suppression mission of a probe-and-drogue refueling hose (PDRH) system with prescribed bound in this study. Based on the distributed parameter system theory, the PDRH is modeled by partial differential equations (PDEs) because of its characteristic of infinite dimension. No violation of the time-varying boundary constraint during operation is warranted by the barrier Lyapunov function. Signals of closed-loop system are demonstrated to be uniformly bounded via Lyapunov's direct method. By choosing parameters appropriately, system state is proven to converge to a small neighborhood of zero. The feasibility of the proposed control law is verified by simulation results.

Distributed parameter system

10.1109/yac51587.2020.9337576

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Provenance Based Intrusion Detection via Measuring Provenance Sequence Similarity

Lin Wu Xie Yl Yafeng Wu Jinyuan Liang Xuan Li

Malicious intrusion presents normalization and diversification in the current environment. Attackers often evade detection by changing the execution order of programs or adding many confusing operations. Aiming at the problem that the existing methods cannot accurately detect abnormal variant sequences, we propose PSSID, an intrusion detection method based on provenance sequence similarity. Firstly, the system data is transformed into a provenance graph, and each path in the provenance graph is regarded as a provenance sequence. Then the provenance sequence and the rule database sequence are compared to find the maximum common sequence length. Finally, according to the distribution of sequence lengths, obtain the two sequences' similarity value to judge whether it is abnormal.

Sequence (biology)

Similarity (geometry)

Normalization

10.1109/icbctis55569.2022.00052

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Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

arXiv (Cornell University) (2023)

Zijun Cheng Qiujian Lv Jinyuan Liang Yan Wang Degang Sun

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.

Kairos

10.48550/arxiv.2308.05034

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Citations (1)

Adaptive Fault-Tolerant Control of a Probe-and-Drogue Refueling Hose Under Varying Length and Constrained Output

IEEE Transactions on Control Systems Technology (2021)

Zhijie Liu Jinyuan Liang Zhijia Zhao Mehmet Önder Efe Keum‐Shik Hong

This brief deals with an adaptive barrier-based fault-tolerant control of a probe-and-drogue refueling hose (PDRH) under varying length, actuator fault, and constrained output. First, we consider the actuator working normally, and a model-based control law with barrier function is developed to stabilize the elastic vibration and ensure the endpoint vibration without violating the restriction of the flexible hose. Second, an adaptive fault-tolerant control scheme is proposed to cope with partial effectiveness loss of the actuator. Subsequently, the direct Lyapunov method is adopted to derive the uniformly bounded stability in the closed-loop system. Finally, simulation results demonstrate the validity of the obtained schemes.

Lyapunov stability

10.1109/tcst.2021.3079275

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Citations (21)

Resource-Interaction Graph: Efficient Graph Representation for Anomaly Detection

arXiv (Cornell University) (2022)

James Pope Jinyuan Liang Vijay Kumar Francesco Raimondo Xinyi Sun

Security research has concentrated on converting operating system audit logs into suitable graphs, such as provenance graphs, for analysis. However, provenance graphs can grow very large requiring significant computational resources beyond what is necessary for many security tasks and are not feasible for resource constrained environments, such as edge devices. To address this problem, we present the \textit{resource-interaction graph} that is built directly from the audit log. We show that the resource-interaction graph's storage requirements are significantly lower than provenance graphs using an open-source data set with two container escape attacks captured from an edge device. We use a graph autoencoder and graph clustering technique to evaluate the representation for an anomaly detection task. Both approaches are unsupervised and are thus suitable for detecting zero-day attacks. The approaches can achieve f1 scores typically over 80\% and in some cases over 90\% for the selected data set and attacks.

Autoencoder

10.48550/arxiv.2212.08525

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Citations (1)

Angus: efficient active learning strategies for provenance based intrusion detection

Cybersecurity (2025)

Lin Wu Yulai Xie Jin Li Dan Feng Jinyuan Liang

Abstract As modern attack methods become more concealed and complex, obtaining many labeled samples in big data streams is difficult. Active learning has long been used to achieve better intrusion detection performance by using only a small number of training samples. Intrusion behaviors can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. It is a challenge to develop active learning strategies that consider defining and selecting the most valuable provenance and ensure that the strategy for querying provenance is efficient. We present Angus, an active learning framework for provenance-based intrusion detection. We propose two novel active learning strategies: the most similar graph query strategy and the maximum difference query strategy. They either select samples to update the training set according to similarities of provenance graphs or preferentially select samples with low redundancy and large differences from the current training set. Besides, we also improve the above query strategies by using the parallel query to reduce detection time overheads. The experiments on various real-world applications demonstrate their performance and efficiency.

10.1186/s42400-024-00311-y

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