Visible to the public Biblio

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Ma, C., Wang, L., Gai, C., Yang, D., Zhang, P., Zhang, H., Li, C..  2020.  Frequency Security Assessment for Receiving-end System Based on Deep Learning Method. 2020 IEEE/IAS Industrial and Commercial Power System Asia (I CPS Asia). :831–836.
For hours-ahead assessment of power systems with a high penetration level of renewable generation, a large number of uncertain scenarios should be checked to ensure the frequency security of the system after the severe power disturbance following HVDC blocking. In this situation, the full time-domain simulation is unsuitable as a result of the heavy calculation burden. To fulfill the quick assessment of the frequency security, the online frequency security assessment framework based on deep learning is proposed in this paper. The Deep Belief Network (DBN) method is used to establish the framework. The sample generation method is researched to generate representative samples for the purposed of higher assessment accuracy. A large-scale AC-DC interconnected power grid is adopted to verify the validity of the proposed assessment method.
Wang, L..  2020.  Trusted Connect Technology of Bioinformatics Authentication Cloud Platform Based on Point Set Topology Transformation Theory. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :151—154.
The bioinformatics features are collected by pattern recognition technology, and the digital coding and format conversion of the feature data are realized by using the theory of topological group transformation. Authentication and Signature based on Zero Knowledge Proof Technology can be used as the trusted credentials of cloud platform and cannot be forged, thus realizing trusted and secure access.
Wang, L., Guo, D..  2020.  Secure Communication Based on Reliability-Based Hybrid ARQ and LDPC Codes. 2020 Prognostics and Health Management Conference (PHM-Besançon). :304—308.
This paper designs a re-transmission strategy to intensify the security of communication over the additive white Gaussian noise (AWGN) wire-tap channel. In this scheme, irregular low-density parity-check (LDPC) codes work with reliability-based hybrid automatic repeat-request (RB-HARQ). For irregular LDPC codes, the variable nodes have different degrees, which means miscellaneous protection for the nodes. In RB-HARQ protocol, the legitimate receiver calls for re-transmissions including the most unreliable bits at decoder's outputting. The bits' reliability can be evaluated by the average magnitude of a posteriori probability log-likelihood ratios (APP LLRs). Specifically, this scheme utilizes the bit-error rate (BER) to assess the secrecy performance. Besides, the paper gives close analyses of BER through theoretical arguments and simulations. Results of numerical example demonstrate that RB-HARQ protocol with irregular LDPC codes can hugely reinforce the security performance of the communication system.
Wang, L., Liu, Y..  2020.  A DDoS Attack Detection Method Based on Information Entropy and Deep Learning in SDN. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:1084—1088.
Software Defined Networking (SDN) decouples the control plane and the data plane and solves the difficulty of new services deployment. However, the threat of a single point of failure is also introduced at the same time. The attacker can launch DDoS attacks towards the controller through switches. In this paper, a DDoS attack detection method based on information entropy and deep learning is proposed. Firstly, suspicious traffic can be inspected through information entropy detection by the controller. Then, fine-grained packet-based detection is executed by the convolutional neural network (CNN) model to distinguish between normal traffic and attack traffic. Finally, the controller performs the defense strategy to intercept the attack. The experiments indicate that the accuracy of this method reaches 98.98%, which has the potential to detect DDoS attack traffic effectively in the SDN environment.
Xie, J., Chen, Y., Wang, L., Wang, Z..  2020.  A Network Covert Timing Channel Detection Method Based on Chaos Theory and Threshold Secret Sharing. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2380—2384.

Network covert timing channel(NCTC) is a process of transmitting hidden information by means of inter-packet delay (IPD) of legitimate network traffic. Their ability to evade traditional security policies makes NCTCs a grave security concern. However, a robust method that can be used to detect a large number of NCTCs is missing. In this paper, a NCTC detection method based on chaos theory and threshold secret sharing is proposed. Our method uses chaos theory to reconstruct a high-dimensional phase space from one-dimensional time series and extract the unique and stable channel traits. Then, a channel identifier is constructed using the secret reconstruction strategy from threshold secret sharing to realize the mapping of the channel features to channel identifiers. Experimental results show that the approach can detect varieties of NCTCs with a guaranteed true positive rate and greatly improve the versatility and robustness.

Li, H., Xie, R., Kong, X., Wang, L., Li, B..  2020.  An Analysis of Utility for API Recommendation: Do the Matched Results Have the Same Efforts? 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). :479—488.

The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.

Wang, L., Wang, D., Gao, J., Huo, C., Bai, H., Yuan, J..  2019.  Research on Multi-Source Data Security Protection of Smart Grid Based on Quantum Key Combination. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). :449–453.

Power communication network is an important infrastructure of power system. For a large number of widely distributed business terminals and communication terminals. The data protection is related to the safe and stable operation of the whole power grid. How to solve the problem that lots of nodes need a large number of keys and avoid the situation that these nodes cannot exchange information safely because of the lack of keys. In order to solve the problem, this paper proposed a segmentation and combination technology based on quantum key to extend the limited key. The basic idea was to obtain a division scheme according to different conditions, and divide a key into several different sub-keys, and then combine these key segments to generate new keys and distribute them to different terminals in the system. Sufficient keys were beneficial to key updating, and could effectively enhance the ability of communication system to resist damage and intrusion. Through the analysis and calculation, the validity of this method in the use of limited quantum keys to achieve the business data secure transmission of a large number of terminal was further verified.

Lu, B., Qin, Z., Yang, M., Xia, X., Zhang, R., Wang, L..  2018.  Spoofing Attack Detection Using Physical Layer Information in Cross-Technology Communication. 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). :1-2.

Recent advances in Cross-Technology Communication (CTC) enable the coexistence and collaboration among heterogeneous wireless devices operating in the same ISM band (e.g., Wi-Fi, ZigBee, and Bluetooth in 2.4 GHz). However, state-of-the-art CTC schemes are vulnerable to spoofing attacks since there is no practice authentication mechanism yet. This paper proposes a scheme to enable the spoofing attack detection for CTC in heterogeneous wireless networks by using physical layer information. First, we propose a model to detect ZigBee packets and measure the corresponding Received Signal Strength (RSS) on Wi-Fi devices. Then, we design a collaborative mechanism between Wi-Fi and ZigBee devices to detect the spoofing attack. Finally, we implement and evaluate our methods through experiments on commercial off-the- shelf (COTS) Wi-Fi and ZigBee devices. Our results show that it is possible to measure the RSS of ZigBee packets on Wi-Fi device and detect spoofing attack with both a high detection rate and a low false positive rate in heterogeneous wireless networks.

Hu, D., Wang, L., Jiang, W., Zheng, S., Li, B..  2018.  A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks. IEEE Access. 6:38303-38314.

The security of image steganography is an important basis for evaluating steganography algorithms. Steganography has recently made great progress in the long-term confrontation with steganalysis. To improve the security of image steganography, steganography must have the ability to resist detection by steganalysis algorithms. Traditional embedding-based steganography embeds the secret information into the content of an image, which unavoidably leaves a trace of the modification that can be detected by increasingly advanced machine-learning-based steganalysis algorithms. The concept of steganography without embedding (SWE), which does not need to modify the data of the carrier image, appeared to overcome the detection of machine-learning-based steganalysis algorithms. In this paper, we propose a novel image SWE method based on deep convolutional generative adversarial networks. We map the secret information into a noise vector and use the trained generator neural network model to generate the carrier image based on the noise vector. No modification or embedding operations are required during the process of image generation, and the information contained in the image can be extracted successfully by another neural network, called the extractor, after training. The experimental results show that this method has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.

Lin, B., Chen, X., Wang, L..  2017.  A Cloud-Based Trust Evaluation Scheme Using a Vehicular Social Network Environment. 2017 24th Asia-Pacific Software Engineering Conference (APSEC). :120–129.

New generation communication technologies (e.g., 5G) enhance interactions in mobile and wireless communication networks between devices by supporting a large-scale data sharing. The vehicle is such kind of device that benefits from these technologies, so vehicles become a significant component of vehicular networks. Thus, as a classic application of Internet of Things (IoT), the vehicular network can provide more information services for its human users, which makes the vehicular network more socialized. A new concept is then formed, namely "Vehicular Social Networks (VSNs)", which bring both benefits of data sharing and challenges of security. Traditional public key infrastructures (PKI) can guarantee user identity authentication in the network; however, PKI cannot distinguish untrustworthy information from authorized users. For this reason, a trust evaluation mechanism is required to guarantee the trustworthiness of information by distinguishing malicious users from networks. Hence, this paper explores a trust evaluation algorithm for VSNs and proposes a cloud-based VSN architecture to implement the trust algorithm. Experiments are conducted to investigate the performance of trust algorithm in a vehicular network environment through building a three-layer VSN model. Simulation results reveal that the trust algorithm can be efficiently implemented by the proposed three-layer model.

Ma, H., Tao, O., Zhao, C., Li, P., Wang, L..  2017.  Impact of Replacement Policies on Static-Dynamic Query Results Cache in Web Search Engines. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). :137–139.

Caching query results is an efficient technique for Web search engines. A state-of-the-art approach named Static-Dynamic Cache (SDC) is widely used in practice. Replacement policy is the key factor on the performance of cache system, and has been widely studied such as LIRS, ARC, CLOCK, SKLRU and RANDOM in different research areas. In this paper, we discussed replacement policies for static-dynamic cache and conducted the experiments on real large scale query logs from two famous commercial Web search engine companies. The experimental results show that ARC replacement policy could work well with static-dynamic cache, especially for large scale query results cache.

Muñoz, C., Wang, L., Solana, E., Crowcroft, J..  2017.  I(FIB)F: Iterated bloom filters for routing in named data networks. 2017 International Conference on Networked Systems (NetSys). :1–8.

Named Data Networks provide a clean-slate redesign of the Future Internet for efficient content distribution. Because Internet of Things are expected to compose a significant part of Future Internet, most content will be managed by constrained devices. Such devices are often equipped with limited CPU, memory, bandwidth, and energy supply. However, the current Named Data Networks design neglects the specific requirements of Internet of Things scenarios and many data structures need to be further optimized. The purpose of this research is to provide an efficient strategy to route in Named Data Networks by constructing a Forwarding Information Base using Iterated Bloom Filters defined as I(FIB)F. We propose the use of content names based on iterative hashes. This strategy leads to reduce the overhead of packets. Moreover, the memory and the complexity required in the forwarding strategy are lower than in current solutions. We compare our proposal with solutions based on hierarchical names and Standard Bloom Filters. We show how to further optimize I(FIB)F by exploiting the structure information contained in hierarchical content names. Finally, two strategies may be followed to reduce: (i) the overall memory for routing or (ii) the probability of false positives.

Chowdhury, M., Gawande, A., Wang, L..  2017.  Secure Information Sharing among Autonomous Vehicles in NDN. 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI). :15–26.

Autonomous vehicles must communicate with each other effectively and securely to make robust decisions. However, today's Internet falls short in supporting efficient data delivery and strong data security, especially in a mobile ad-hoc environment. Named Data Networking (NDN), a new data-centric Internet architecture, provides a better foundation for secure data sharing among autonomous vehicles. We examine two potential threats, false data dissemination and vehicle tracking, in an NDN-based autonomous vehicular network. To detect false data, we propose a four-level hierarchical trust model and the associated naming scheme for vehicular data authentication. Moreover, we address vehicle tracking concerns using a pseudonym scheme to anonymize vehicle names and certificate issuing proxies to further protect vehicle identity. Finally, we implemented and evaluated our AutoNDN application on Raspberry Pi-based mini cars in a wireless environment.

Zhang, Y., Wang, L., You, Y., Yi, L..  2017.  A Remote-Attestation-Based Extended Hash Algorithm for Privacy Protection. 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA). :254–257.

Compared to other remote attestation methods, the binary-based approach is the most direct and complete one, but privacy protection has become an important problem. In this paper, we presented an Extended Hash Algorithm (EHA) for privacy protection based on remote attestation method. Based on the traditional Merkle Hash Tree, EHA altered the algorithm of node connection. The new algorithm could ensure the same result in any measure order. The security key is added when the node connection calculation is performed, which ensures the security of the value calculated by the Merkle node. By the final analysis, we can see that the remote attestation using EHA has better privacy protection and execution performance compared to other methods.

Lee, S. H., Wang, L., Khisti, A., Womell, G. W..  2017.  Covert communication with noncausal channel-state information at the transmitter. 2017 IEEE International Symposium on Information Theory (ISIT). :2830–2834.

We consider the problem of covert communication over a state-dependent channel, where the transmitter has non-causal knowledge of the channel states. Here, “covert” means that the probability that a warden on the channel can detect the communication must be small. In contrast with traditional models without noncausal channel-state information at the transmitter, we show that covert communication can be possible with positive rate. We derive closed-form formulas for the maximum achievable covert communication rate (“covert capacity”) in this setting for discrete memoryless channels as well as additive white Gaussian noise channels. We also derive lower bounds on the rate of the secret key that is needed for the transmitter and the receiver to achieve the covert capacity.

Bulbul, R., Ten, C. W., Wang, L..  2015.  Prioritization of MTTC-based combinatorial evaluation for hypothesized substations outages. 2015 IEEE Power Energy Society General Meeting. :1–5.

Exhaustive enumeration of a S-select-k problem for hypothesized substations outages can be practically infeasible due to exponential growth of combinations as both S and k numbers increase. This enumeration of worst-case substations scenarios from the large set, however, can be improved based on the initial selection sets with the root nodes and segments. In this paper, the previous work of the reverse pyramid model (RPM) is enhanced with prioritization of root nodes and defined segmentations of substation list based on mean-time-to-compromise (MTTC) value that is associated with each substation. Root nodes are selected based on the threshold values of the substation ranking on MTTC values and are segmented accordingly from the root node set. Each segmentation is then being enumerated with S-select-k module to identify worst-case scenarios. The lowest threshold value on the list, e.g., a substation with no assignment of MTTC or extremely low number, is completely eliminated. Simulation shows that this approach demonstrates similar outcome of the risk indices among all randomly generated MTTC of the IEEE 30-bus system.