Visible to the public Biblio

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Lin, Y., Liu, H., Xie, G., Zhang, Y..  2018.  Time Series Forecasting by Evolving Deep Belief Network with Negative Correlation Search. 2018 Chinese Automation Congress (CAC). :3839-3843.

The recently developed deep belief network (DBN) has been shown to be an effective methodology for solving time series forecasting problems. However, the performance of DBN is seriously depended on the reasonable setting of hyperparameters. At present, random search, grid search and Bayesian optimization are the most common methods of hyperparameters optimization. As an alternative, a state-of-the-art derivative-free optimizer-negative correlation search (NCS) is adopted in this paper to decide the sizes of DBN and learning rates during the training processes. A comparative analysis is performed between the proposed method and other popular techniques in the time series forecasting experiment based on two types of time series datasets. Experiment results statistically affirm the efficiency of the proposed model to obtain better prediction results compared with conventional neural network models.

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He, F., Zhang, Y., Liu, H., Zhou, W..  2018.  SCPN-Based Game Model for Security Situational Awareness in the Intenet of Things. 2018 IEEE Conference on Communications and Network Security (CNS). :1-5.
Internet of Things (IoT) is characterized by various of heterogeneous devices that facing numerous threats, which makes modeling security situation of IoT still a certain challenge. This paper defines a Stochastic Colored Petri Net (SCPN) for IoT-based smart environment and then proposes a Game model for security situational awareness. All possible attack paths are computed by the SCPN, and antagonistic behavior of both attackers and defenders are taken into consideration dynamically according to Game Theory (GT). Experiments on two typical attack scenarios in smart home environment demonstrate the effectiveness of the proposed model. The proposed model can form a macroscopic trend curve of the security situation. Analysis of the results shows the capabilities of the proposed model in finding vulnerable devices and potential attack paths, and even facilitating the choice of defense strategy. To the best of our knowledge, this is the first attempt to use Game Theory in the IoT-based SCPN to establish a security situational awareness model for a complex smart environment.
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Li, J., Liu, H., Wu, J., Zhu, J., Huifeng, Y., Rui, X..  2019.  Research on Nonlinear Frequency Hopping Communication Under Big Data. 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA). :349—354.

Aiming at the problems of poor stability and low accuracy of current communication data informatization processing methods, this paper proposes a research on nonlinear frequency hopping communication data informatization under the framework of big data security evaluation. By adding a frequency hopping mediation module to the frequency hopping communication safety evaluation framework, the communication interference information is discretely processed, and the data parameters of the nonlinear frequency hopping communication data are corrected and converted by combining a fast clustering analysis algorithm, so that the informatization processing of the nonlinear frequency hopping communication data under the big data safety evaluation framework is completed. Finally, experiments prove that the research on data informatization of nonlinear frequency hopping communication under the framework of big data security evaluation could effectively improve the accuracy and stability.

Ming, Z., Zheng-jiang, W., Liu, H..  2017.  Random Projection Data Perturbation Based Privacy Protection in WSNs. 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :493–498.

Wireless sensor networks are responsible for sensing, gathering and processing the information of the objects in the network coverage area. Basic data fusion technology generally does not provide data privacy protection mechanism, and the privacy protection mechanism in health care, military reconnaissance, smart home and other areas of the application is usually indispensable. In this paper, we consider the privacy, confidentiality, and the accuracy of fusion results, and propose a data fusion algorithm for privacy preserving. This algorithm relies on the characteristics of data fusion, and uses the method of pre-distribution random number in the node to get the privacy protection requirements of the original data. Theoretical analysis shows that the malicious attacker attempts to steal the difficulty of node privacy in PPND algorithm. At the same time in the TOSSIM simulation results also show that, compared with TAG, SMART algorithm, PPND algorithm in the data traffic, the convergence accuracy of the good performance.

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Hu, J., Shi, W., Liu, H., Yan, J., Tian, Y., Wu, Z..  2017.  Preserving Friendly-Correlations in Uncertain Graphs Using Differential Privacy. 2017 International Conference on Networking and Network Applications (NaNA). :24–29.

It is a challenging problem to preserve the friendly-correlations between individuals when publishing social-network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the correlations between individuals is an associated probability. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper we first introduced a general model for constructing uncertain graphs. Then, we proposed an algorithm under the model which is based on differential privacy and made an analysis of algorithm's privacy. Our algorithm provides rigorous guarantees of privacy and against the background knowledge attack. Finally, the algorithm we proposed satisfied differential privacy and showed feasibility in the experiments. And then, we compare our algorithm with (k, ε)-obfuscation algorithm in terms of data utility, the importance of nodes for network in our algorithm is similar to (k, ε)-obfuscation algorithm.

Zheng, T., Liu, H., Wang, Z., Yang, Q., Wang, H..  2020.  Physical-Layer Security with Finite Blocklength over Slow Fading Channels. 2020 International Conference on Computing, Networking and Communications (ICNC). :314–319.
This paper studies physical-layer security over slow fading channels, considering the impact of finite-blocklength secrecy coding. A comprehensive analysis and optimization framework is established to investigate the secrecy throughput (ST) of a legitimate user pair coexisting with an eavesdropper. Specifically, we devise both adaptive and non-adaptive optimization schemes to maximize the ST, where we derive optimal parameters including the transmission policy, blocklength, and code rates based on the instantaneous and statistical channel state information of the legitimate pair, respectively. Various important insights are provided. In particular, 1) increasing blocklength improves both reliability and secrecy with our transmission policy; 2) ST monotonically increases with blocklength; 3) ST initially increases and then decreases with secrecy rate, and there exists a critical secrecy rate that maximizes the ST. Numerical results are presented to verify theoretical findings.
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Sun, X., Liu, H., Zhang, M..  2016.  Multivariate symmetric cryptography with 2-dimesion chaotic disturbation. 2016 8th International Conference on Wireless Communications Signal Processing (WCSP). :1–4.

Multivariate public key cryptosystem acts as a signature system rather than encryption system due to the minus mode used in system. A multivariate encryption system with determinate equations in central map and chaotic shell protection for central map and affine map is proposed in this paper. The outputs of two-dimension chaotic system are discretized on a finite field to disturb the central map and affine map in multivariate cryptosystem. The determined equations meet the shortage of indeterminate equations in minus mode and make the general attack methods are out of tenable condition. The analysis shows the proposed multivariate symmetric encryption system based on chaotic shell is able to resist general attacks.

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Lu, L., Yu, J., Chen, Y., Liu, H., Zhu, Y., Liu, Y., Li, M..  2018.  LipPass: Lip Reading-based User Authentication on Smartphones Leveraging Acoustic Signals. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1466–1474.

To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.

Zhang, H., Liu, H., Deng, L., Wang, P., Rong, X., Li, Y., Li, B., Wang, H..  2018.  Leader Recognition and Tracking for Quadruped Robots. 2018 IEEE International Conference on Information and Automation (ICIA). :1438—1443.

To meet the high requirement of human-machine interaction, quadruped robots with human recognition and tracking capability are studied in this paper. We first introduce a marker recognition system which uses multi-thread laser scanner and retro-reflective markers to distinguish the robot's leader and other objects. When the robot follows leader autonomously, the variant A* algorithm which having obstacle grids extended virtually (EA*) is used to plan the path. But if robots need to track and follow the leader's path as closely as possible, it will trust that the path which leader have traveled is safe enough and uses the incremental form of EA* algorithm (IEA*) to reproduce the trajectory. The simulation and experiment results illustrate the feasibility and effectiveness of the proposed algorithms.

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Zheng, L., Jiang, J., Pan, W., Liu, H..  2020.  High-Performance and Range-Supported Packet Classification Algorithm for Network Security Systems in SDN. 2020 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.
Packet classification is a key function in network security systems in SDN, which detect potential threats by matching the packet header bits and a given rule set. It needs to support multi-dimensional fields, large rule sets, and high throughput. Bit Vector-based packet classification methods can support multi-field matching and achieve a very high throughput, However, the range matching is still challenging. To address issue, this paper proposes a Range Supported Bit Vector (RSBV) algorithm for processing the range fields. RSBV uses specially designed codes to store the pre-computed results in memory, and the result of range matching is derived through pipelined Boolean operations. Through a two-dimensional modular architecture, the RSBV can operate at a high clock frequency and line-rate processing can be guaranteed. Experimental results show that for a 1K and 512-bit OpenFlow rule set, the RSBV can sustain a throughput of 520 Million Packets Per Second.
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Liu, H., Ditzler, G..  2017.  A fast information-theoretic approximation of joint mutual information feature selection. 2017 International Joint Conference on Neural Networks (IJCNN). :4610–4617.

Feature selection is an important step in data analysis to address the curse of dimensionality. Such dimensionality reduction techniques are particularly important when if a classification is required and the model scales in polynomial time with the size of the feature (e.g., some applications include genomics, life sciences, cyber-security, etc.). Feature selection is the process of finding the minimum subset of features that allows for the maximum predictive power. Many of the state-of-the-art information-theoretic feature selection approaches use a greedy forward search; however, there are concerns with the search in regards to the efficiency and optimality. A unified framework was recently presented for information-theoretic feature selection that tied together many of the works in over the past twenty years. The work showed that joint mutual information maximization (JMI) is generally the best options; however, the complexity of greedy search for JMI scales quadratically and it is infeasible on high dimensional datasets. In this contribution, we propose a fast approximation of JMI based on information theory. Our approach takes advantage of decomposing the calculations within JMI to speed up a typical greedy search. We benchmarked the proposed approach against JMI on several UCI datasets, and we demonstrate that the proposed approach returns feature sets that are highly consistent with JMI, while decreasing the run time required to perform feature selection.

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Zhang, H., Liu, H., Liang, J., Li, T., Geng, L., Liu, Y., Chen, S..  2020.  Defense Against Advanced Persistent Threats: Optimal Network Security Hardening Using Multi-stage Maze Network Game. 2020 IEEE Symposium on Computers and Communications (ISCC). :1—6.

Advanced Persistent Threat (APT) is a stealthy, continuous and sophisticated method of network attacks, which can cause serious privacy leakage and millions of dollars losses. In this paper, we introduce a new game-theoretic framework of the interaction between a defender who uses limited Security Resources(SRs) to harden network and an attacker who adopts a multi-stage plan to attack the network. The game model is derived from Stackelberg games called a Multi-stage Maze Network Game (M2NG) in which the characteristics of APT are fully considered. The possible plans of the attacker are compactly represented using attack graphs(AGs), but the compact representation of the attacker's strategies presents a computational challenge and reaching the Nash Equilibrium(NE) is NP-hard. We present a method that first translates AGs into Markov Decision Process(MDP) and then achieves the optimal SRs allocation using the policy hill-climbing(PHC) algorithm. Finally, we present an empirical evaluation of the model and analyze the scalability and sensitivity of the algorithm. Simulation results exhibit that our proposed reinforcement learning-based SRs allocation is feasible and efficient.

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Hu, X., Tang, W., Liu, H., Zhang, D., Lian, S., He, Y..  2017.  Construction of bulk power grid security defense system under the background of AC/DC hybrid EHV transmission system and new energy. IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. :5713–5719.

With the rapid development of bulk power grid under extra-high voltage (EHV) AC/DC hybrid power system and extensive access of distributed energy resources (DER), operation characteristics of power grid have become increasingly complicated. To cope with new severe challenges faced by safe operation of interconnected bulk power grids, an in-depth analysis of bulk power grid security defense system under the background of EHV and new energy resources was implemented from aspects of management and technology in this paper. Supported by big data and cloud computing, bulk power grid security defense system was divided into two parts: one is the prevention and control of operation risks. Power grid risks are eliminated and influence of random faults is reduced through measures such as network planning, power-cut scheme, risk pre-warning, equipment status monitoring, voltage control, frequency control and adjustment of operating mode. The other is the fault recovery control. By updating “three defense lines”, intelligent relay protection is used to deal with the challenges brought by EHV AC/DC hybrid grid and new energy resources. And then security defense system featured by passive defense is promoted to active type power grid security defense system.

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Chowdhuryy, M. H. Islam, Liu, H., Yao, F..  2020.  BranchSpec: Information Leakage Attacks Exploiting Speculative Branch Instruction Executions. 2020 IEEE 38th International Conference on Computer Design (ICCD). :529–536.
Recent studies on attacks exploiting processor hardware vulnerabilities have raised significant concern for information security. Particularly, transient execution attacks such as Spectre augment microarchitectural side channels with speculative executions that lead to exfiltration of secretive data not intended to be accessed. Many prior works have demonstrated the manipulation of branch predictors for triggering speculative executions, and thereafter leaking sensitive information through processor microarchitectural components. In this paper, we present a new class of microarchitectural attack, called BranchSpec, that performs information leakage by exploiting state changes of branch predictors in speculative path. Our key observation is that, branch instruction executions in speculative path alter the states of branch pattern history, which are not restored even after the speculatively executed branches are eventually squashed. Unfortunately, this enables adversaries to harness branch predictors as the transmitting medium in transient execution attacks. More importantly, as compared to existing speculative attacks (e.g., Spectre), BranchSpec can take advantage of much simpler code patterns in victim's code base, making the impact of such exploitation potentially even more severe. To demonstrate this security vulnerability, we have implemented two variants of BranchSpec attacks: a side channel where a malicious spy process infers cross-boundary secrets via victim's speculatively executed nested branches, and a covert channel that communicates secrets through intentionally perturbing the branch pattern history structure via speculative branch executions. Our evaluation on Intel Skylake- and Coffee Lake-based processors reveals that these information leakage attacks are highly accurate and successful. To the best of our knowledge, this is the first work to reveal the information leakage threat due to speculative state update in branch predictor. Our studies further broaden the attack surface of processor microarchitecture, and highlight the needs for branch prediction mechanisms that are secure in transient executions.
Nan, Z., Zhai, L., Zhai, L., Liu, H..  2018.  Botnet Homology Method Based on Symbolic Approximation Algorithm of Communication Characteristic Curve. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). :1-6.

The IRC botnet is the earliest and most significant botnet group that has a significant impact. Its characteristic is to control multiple zombies hosts through the IRC protocol and constructing command control channels. Relevant research analyzes the large amount of network traffic generated by command interaction between the botnet client and the C&C server. Packet capture traffic monitoring on the network is currently a more effective detection method, but this information does not reflect the essential characteristics of the IRC botnet. The increase in the amount of erroneous judgments has often occurred. To identify whether the botnet control server is a homogenous botnet, dynamic network communication characteristic curves are extracted. For unequal time series, dynamic time warping distance clustering is used to identify the homologous botnets by category, and in order to improve detection. Speed, experiments will use SAX to reduce the dimension of the extracted curve, reducing the time cost without reducing the accuracy.

Alniamy, A. M., Liu, H..  2020.  Blockchain-Based Secure Collaboration Platform for Sharing and Accessing Scientific Research Data. 2020 3rd International Conference on Hot Information-Centric Networking (HotICN). :34—40.
Research teams or institutions in different countries need an effective and secure online platform for collaboration and data sharing. It is essential to build such a collaboration platform with strong data security and privacy. In this paper, we propose a platform for researchers to collaborate and share their data by leveraging attribute-based access control (ABAC) and blockchain technologies. ABAC provides an access control paradigm whereby access rights are granted to users through attribute-based policies, instead of user identities and roles. Hyperledger fabric permission blockchain is used to enable a decentralized secure data sharing environment and preserves user’s privacy. The proposed platform allows researchers to fully control their data, manage access to the data at a fine-grained level, keep file updates with proof of authorship, and ensure data integrity and privacy.
Liu, H., Wang, W., He, Z., Tong, Q., Wang, X., Yu, W., Lv, M..  2015.  Blind image quality evaluation metrics design for UAV photographic application. 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). :293–297.

A number of blind Image Quality Evaluation Metrics (IQEMs) for Unmanned Aerial Vehicle (UAV) photograph application are presented. Nowadays, the visible light camera is widely used for UAV photograph application because of its vivid imaging effect; however, the outdoor environment light will produce great negative influences on its imaging output unfortunately. In this paper, to conquer this problem above, we design and reuse a series of blind IQEMs to analyze the imaging quality of UAV application. The Human Visual System (HVS) based IQEMs, including the image brightness level, the image contrast level, the image noise level, the image edge blur level, the image texture intensity level, the image jitter level, and the image flicker level, are all considered in our application. Once these IQEMs are calculated, they can be utilized to provide a computational reference for the following image processing application, such as image understanding and recognition. Some preliminary experiments for image enhancement have proved the correctness and validity of our proposed technique.

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Liu, H., Zhou, Z., Zhang, M..  2020.  Application of Optimized Bidirectional Generative Adversarial Network in ICS Intrusion Detection. 2020 Chinese Control And Decision Conference (CCDC). :3009—3014.

Aiming at the problem that the traditional intrusion detection method can not effectively deal with the massive and high-dimensional network traffic data of industrial control system (ICS), an ICS intrusion detection strategy based on bidirectional generative adversarial network (BiGAN) is proposed in this paper. In order to improve the applicability of BiGAN model in ICS intrusion detection, the optimal model was obtained through the single variable principle and cross-validation. On this basis, the supervised control and data acquisition (SCADA) standard data set is used for comparative experiments to verify the performance of the optimized model on ICS intrusion detection. The results show that the ICS intrusion detection method based on optimized BiGAN has higher accuracy and shorter detection time than other methods.

Liu, H., Di, W..  2020.  Application of Differential Privacy in Location Trajectory Big Data. 2020 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :569—573.

With the development of mobile internet technology, GPS technology and social software have been widely used in people's lives. The problem of big data privacy protection related to location trajectory is becoming more and more serious. The traditional location trajectory privacy protection method requires certain background knowledge and it is difficult to adapt to massive mass. Privacy protection of data. differential privacy protection technology protects privacy by attacking data by randomly perturbing raw data. The method used in this paper is to first sample the position trajectory, form the irregular polygons of the high-frequency access points in the sampling points and position data, calculate the center of gravity of the polygon, and then use the differential privacy protection algorithm to add noise to the center of gravity of the polygon to form a new one. The center of gravity, and the new center of gravity are connected to form a new trajectory. The purpose of protecting the position trajectory is well achieved. It is proved that the differential privacy protection algorithm can effectively protect the position trajectory by adding noise.