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Yunkai Zou

Bio: Yunkai Zou is an academic researcher from Civil Aviation University of China. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

Papers
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Journal ArticleDOI
TL;DR: A long short-term memory (LSTM)-based ADS-B spoofing attack detection method from the perspective of data that can respond well to the security threats suffered by theADS-B system.
Abstract: The open and shared nature of the Automatic Dependent Surveillance Broadcast (ADS-B) protocol makes its messages extremely vulnerable to various security threats, such as jamming, modification, and injection. This paper proposes a long short-term memory (LSTM)-based ADS-B spoofing attack detection method from the perspective of data. First, the message sequence is preprocessed in the form of a sliding window, and then, an LSTM network is used to perform prediction training on the windows. Finally, the residual set of predicted values and true values is calculated to set a threshold. As a result, we can detect a spoofing attack and further identify which feature was attacked. Experiments show that this method can effectively detect 10 different kinds of simulated manipulated ADS-B messages without further increasing the complexity of airborne applications. Therefore, the method can respond well to the security threats suffered by the ADS-B system.

16 citations

Proceedings ArticleDOI
01 May 2022
TL;DR: This work proposes four theoretic models for characterizing the attacker $\mathcal{A}$’s best distinguishing strategies, and develops the corresponding honeyword-generation method for each type of attackers, by using various representative probabilistic password guessing models.
Abstract: Honeywords are decoy passwords associated with each user account to timely detect password leakage. The key issue lies in how to generate honeywords that are hard to be differentiated from real passwords. This security mechanism was first introduced by Juels and Rivest at CCS’13, and has been covered by hundreds of media and adopted in dozens of research domains. Existing research deals with honeywords primarily in an ad hoc manner, and it is challenging to develop a secure honeyword-generation method and well evaluate (attack) it. In this work, we tackle this problem in a principled approach. We first propose four theoretic models for characterizing the attacker $\mathcal{A}$’s best distinguishing strategies, with each model based on a different combination of information available to $\mathcal{A}$ (e.g., public datasets, the victim’s personal information and registration order). These theories guide us to design effective experiments with real-world password datasets to evaluate the goodness (flatness) of a given honeyword-generation method.Armed with the four best attacking theories, we develop the corresponding honeyword-generation method for each type of attackers, by using various representative probabilistic password guessing models. Through a series of exploratory investigations, we show the use of these password models is not straightforward, but requires creative and significant efforts. Both empirical experiments and user-study results demonstrate that our methods significantly outperform prior art. Besides, we manage to resolve several previously unexplored challenges that arise in the practical deployment of a honeyword method. We believe this work pushes the honeyword research towards statistical rigor.

11 citations

Journal ArticleDOI
TL;DR: In this article , an anomaly detection method for industrial control systems (ICS) based on Long Short Term Memory (LSTM) has been proposed, which outperforms the accuracy of traditional ones.
Abstract: Industrial control systems (ICS) are no longer restricted to industrial production. They are also at the heart of safety critical systems and carry out key information that require strong need in terms of availability and integrity. Furthermore, they are gradually connected with the Internet. In the context of Air Traffic Management, safety critical data are generally time series which contain periodic events. Anomalies can hardly be detected as we only have a little knowledge of the traffic characteristic and the kind of anomalies we might encounter. Consequently, detecting them is challenging as it requires high detection accuracy currently unfeasible with traditional methods based on anomaly signatures or predictions. To cope with this issue, we introduce an anomaly detection method for ICS based on Long Short Term Memory (LSTM) that outperforms the accuracy of traditional ones. We experiment and develop our method with one major dataset containing French civil radar aviation data. We then evaluate our scheme with different datasets containing ICS monitoring data (publicly available predictable time series data) and show that our autoencoder can detect anomalies from predictable times series and present a higher detection rate on average than traditional detection methods.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: A comprehensive review along with a taxonomy of the most recent existing autonomic and elastic RM techniques in a cloud environment is presented to ensure the Quality-of-Service (QoS) of cloud-based applications, satisfy the cloud user requirements, and avoid a Service-Level Agreement (SLA) violations.
Abstract: Resource management (RM) is a challenging task in a cloud computing environment where a large number of virtualized, heterogeneous, and distributed resources are hosted in the datacentres. The uncertainty, heterogeneity, and the dynamic nature of such resources affect the efficiency of provisioning, allocation, scheduling, and monitoring tasks of RM. The most existing RM techniques and strategies have insufficiency in handling such cloud resources dynamic behaviour. To resolve these limitations, there is a need for the design and development of intelligent and efficient autonomic RM techniques to ensure the Quality-of-Service (QoS) of cloud-based applications, satisfy the cloud user requirements, and avoid a Service-Level Agreement (SLA) violations. This paper presents a comprehensive review along with a taxonomy of the most recent existing autonomic and elastic RM techniques in a cloud environment. The taxonomy classifies the existing autonomic and elastic RM techniques into different categories based on their design, objective, function, and applications. Moreover, a comparison and qualitative analysis is provided to illustrate their strengths and weaknesses. Finally, the open issues and challenges are highlighted to help researchers in finding significant future research options.

11 citations

Journal ArticleDOI
TL;DR: The state of the art of IoMT systems is examined and their crucial role in supporting anticipatory learning is discussed and the guidelines and directions for future research on this emerging topic are highlighted.
Abstract: The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings on multiple spatio-temporal scales, interact with each other across a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently, most of the geospatial applications of IoMT systems are developed for abnormal detection and control monitoring. However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities. This paper examines the state of the art of IoMT systems and discusses their crucial role in supporting anticipatory learning. The maximum potential of IoMT systems in future smart cities can be fully exploited in terms of proactive decision making and decision delivery via an anticipatory action/feedback loop. We also examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to GIS. The holistic overview provided in this paper highlights the guidelines and directions for future research on this emerging topic.

5 citations

Journal ArticleDOI
TL;DR: Although spoofing in aviation is only a potential threat, its technical feasibility is realistic and its potential is considerable; it becomes more flexible and cheaper due to very rapid advancement of SDR technologies.
Abstract: Introduction/purpose: The paper provides a review of recent research in the field of GPS and ADS-B spoofing. Systems that rely on satellite positioning technology can be targeted by spoofing in order to generate incorrect positioning/timing, which is accomplished by inserting false signals into the \"victim's\" receiver. Attackers try to insert false positioning information into systems that, for example, provide navigation of airplanes or drones for the purpose of hijacking or distracting security/safety in airspace surveillance. New concepts of navigation and ATC will thus be necessary. Methods: Using a scientific approach, the paper gives an evaluation of GPS and ADS-B spoofing/antispoofing and how spoofing affects the cyber security of aviation systems. Results: Based on the methodological analysis used, the importance of studying spoofing/anti-spoofing in aviation is shown. Conclusion: Although spoofing in aviation is only a potential threat, its technical feasibility is realistic and its potential is considerable; it becomes more flexible and cheaper due to very rapid advancement of SDR technologies. The real risk, in the time to come, are potential spoofing attacks that could occur from the air, using drones. However, aircraft systems are not exposed to spoofing without any defense; receivers can detect it by applying various anti-spufing techniques. Also, pilots are able to detect and solve problems at every stage of the flight. However, due to a possibility of more sophisticated spoofing attacks, international organizations such as ICAO are proactively working to increase GPS and ADS-B systems robustness on spoofing.

4 citations

Proceedings ArticleDOI
25 Nov 2020
TL;DR: In this article, a blockchain implementation within a Microservices framework for ADS-B data verification is proposed to enable data feeds coming from third-party receivers to be processed and correlated with that of the ATC ground station receivers.
Abstract: The use of Automatic Dependent Surveillance - Broadcast (ADS-B) for aircraft tracking and flight management operations is widely used today. However, ADS-B is prone to several cyber-security threats due to the lack of data authentication and encryption. Recently, Blockchain has emerged as new paradigm that can provide promising solutions in decentralized systems. Furthermore, software containers and Microservices facilitate the scaling of Blockchain implementations within cloud computing environment. When fused together, these technologies could help improve Air Traffic Control (ATC) processing of ADS-B data. In this paper, a Blockchain implementation within a Microservices framework for ADS-B data verification is proposed. The aim of this work is to enable data feeds coming from third-party receivers to be processed and correlated with that of the ATC ground station receivers. The proposed framework could mitigate ADS- B security issues of message spoofing and anomalous traffic data. and hence minimize the cost of ATC infrastructure by throughout third-party support.

3 citations

Journal ArticleDOI
TL;DR: The traditional filtering algorithm is improved and built and the channel optimization system of the ADS-B aviation target surveillance radar is built based on the improved filtering algorithm.
Abstract: In order to improve the surveillance effect of the aviation target surveillance radar, this paper improves the traditional filtering algorithm and builds the channel optimization system of the ADS-B aviation target surveillance radar based on the improved filtering algorithm. Moreover, this paper uses algorithm improvement to ensure the positive definite or semipositive definiteness of the state covariance and uses the root mean square volume Kalman filter to avoid the filter divergence or tracking interruption caused by the nonpositive definiteness of the matrix; the filtering principle of the interactive multimodel is to use multiple filters for parallel processing and achieve the adaptive adjustment algorithm residual error by adjusting the one-step prediction covariance in the adjustment algorithm. In addition, this paper combines the actual needs to construct a system functional structure to optimize the channel of the ADS-B aviation target surveillance radar and uses software engineering methods to model and analyze the requirements. Finally, this paper designs experiments to verify system performance. The research results show that the performance of the system constructed in this paper meets actual needs.

3 citations