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Z
Tang, R., Yang, Z., Li, Z., Meng, W., Wang, H., Li, Q., Sun, Y., Pei, D., Wei, T., Xu, Y. et al..  2020.  ZeroWall: Detecting Zero-Day Web Attacks through Encoder-Decoder Recurrent Neural Networks. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2479—2488.

Zero-day Web attacks are arguably the most serious threats to Web security, but are very challenging to detect because they are not seen or known previously and thus cannot be detected by widely-deployed signature-based Web Application Firewalls (WAFs). This paper proposes ZeroWall, an unsupervised approach, which works with an existing WAF in pipeline, to effectively detecting zero-day Web attacks. Using historical Web requests allowed by an existing signature-based WAF, a vast majority of which are assumed to be benign, ZeroWall trains a self-translation machine using an encoder-decoder recurrent neural network to capture the syntax and semantic patterns of benign requests. In real-time detection, a zero-day attack request (which the WAF fails to detect), not understood well by self-translation machine, cannot be translated back to its original request by the machine, thus is declared as an attack. In our evaluation using 8 real-world traces of 1.4 billion Web requests, ZeroWall successfully detects real zero-day attacks missed by existing WAFs and achieves high F1-scores over 0.98, which significantly outperforms all baseline approaches.

R
Li, Q., Xu, B., Li, S., Liu, Y., Cui, D..  2017.  Reconstruction of measurements in state estimation strategy against cyber attacks for cyber physical systems. 2017 36th Chinese Control Conference (CCC). :7571–7576.

To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-Singular Value Decomposition (K-SVD). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.

P
Wang, R., He, J., Liu, C., Li, Q., Tsai, W., Deng, E..  2018.  A Privacy-Aware PKI System Based on Permissioned Blockchains. 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). :928–931.

Public key infrastructure (PKI) is the foundation and core of network security construction. Blockchain (BC) has many technical characteristics, such as decentralization, impossibility of being tampered with and forged, which makes it have incomparable advantages in ensuring information credibility, security, traceability and other aspects of traditional technology. In this paper, a method of constructing PKI certificate system based on permissioned BC is proposed. The problems of multi-CA mutual trust, poor certificate configuration efficiency and single point failure in digital certificate system are solved by using the characteristics of BC distribution and non-tampering. At the same time, in order to solve the problem of identity privacy on BC, this paper proposes a privacy-aware PKI system based on permissioned BCs. This system is an anonymous digital certificate publishing scheme., which achieves the separation of user registration and authorization, and has the characteristics of anonymity and conditional traceability, so as to realize to protect user's identity privacy. The system meets the requirements of certificate security and anonymity, reduces the cost of CA construction, operation and maintenance in traditional PKI technology, and improves the efficiency of certificate application and configuration.

L
Pan, T., Xu, C., Lv, J., Shi, Q., Li, Q., Jia, C., Huang, T., Lin, X..  2019.  LD-ICN: Towards Latency Deterministic Information-Centric Networking. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :973–980.
Deterministic latency is the key challenge that must be addressed in numerous 5G applications such as AR/VR. However, it is difficult to make customized end-to-end resource reservation across multiple ISPs using IP-based QoS mechanisms. Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to its in-network caching and native multicast capabilities, and the deterministic latency can promisingly be guaranteed by caching the relevant content objects in appropriate locations. Existing proposals formulate the ICN cache placement problem into numerous theoretical models. However, the underlying mechanisms to support such cache coordination are not discussed in detail. Especially, how to efficiently make cache reservation, how to avoid route oscillation when content cache is updated and how to conduct the real-time latency measurement? In this work, we propose Latency Deterministic Information-Centric Networking (LD-ICN). LD-ICN relies on source routing-based latency telemetry and leverages an on-path caching technique to avoid frequent route oscillation while still achieve the optimal cache placement under the SDN architecture. Extensive evaluation shows that under LD-ICN, 90.04% of the content requests are satisfied within the hard latency requirements.
F
Lin, J., Li, Q., Yang, J..  2017.  Frequency diverse array beamforming for physical-layer security with directionally-aligned legitimate user and eavesdropper. 2017 25th European Signal Processing Conference (EUSIPCO). :2166–2170.
The conventional physical-layer (PHY) security approaches, e.g., transmit beamforming and artificial noise (AN)-based design, may fail when the channels of legitimate user (LU) and eavesdropper (Eve) are close correlated. Due to the highly directional transmission feature of millimeter-wave (mmWave), this may occur in mmWave transmissions as the transmitter, Eve and LU are aligned in the same direction exactly. To handle the PHY security problem with directionally-aligned LU and Eve, we propose a novel frequency diverse array (FDA) beamforming approach to differentiating the LU and Eve. By intentionally introducing some frequency offsets across the antennas, the FDA beamforming generates an angle-range dependent beampattern. As a consequence, it can degrade the Eve's reception and thus achieve PHY security. In this paper, we maximize the secrecy rate by jointly optimizing the frequency offsets and the beamformer. This secrecy rate maximization (SRM) problem is hard to solve due to the tightly coupled variables. Nevertheless, we show that it can be reformulated into a form depending only on the frequency offsets. Building upon this reformulation, we identify some cases where the SRM problem can be optimally solved in closed form. Numerical results demonstrate the efficacy of FDA beamforming in achieving PHY security, even for aligned LU and Eve.
D
Li, Y., Chen, J., Li, Q., Liu, A..  2020.  Differential Privacy Algorithm Based on Personalized Anonymity. 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). :260—267.

The existing anonymized differential privacy model adopts a unified anonymity method, ignoring the difference of personal privacy, which may lead to the problem of excessive or insufficient protection of the original data [1]. Therefore, this paper proposes a personalized k-anonymity model for tuples (PKA) and proposes a differential privacy data publishing algorithm (DPPA) based on personalized anonymity, firstly based on the tuple personality factor set by the user in the original data set. The values are classified and the corresponding privacy protection relevance is calculated. Then according to the tuple personality factor classification value, the data set is clustered by clustering method with different anonymity, and the quasi-identifier attribute of each cluster is aggregated and noise-added to realize anonymized differential privacy; finally merge the subset to get the data set that meets the release requirements. In this paper, the correctness of the algorithm is analyzed theoretically, and the feasibility and effectiveness of the proposed algorithm are verified by comparison with similar algorithms.

C
Sun, J., Sun, K., Li, Q..  2017.  CyberMoat: Camouflaging Critical Server Infrastructures with Large Scale Decoy Farms. 2017 IEEE Conference on Communications and Network Security (CNS). :1–9.

Traditional deception-based cyber defenses often undertake reactive strategies that utilize decoy systems or services for attack detection and information gathering. Unfortunately, the effectiveness of these defense mechanisms has been largely constrained by the low decoy fidelity, the poor scalability of decoy platform, and the static decoy configurations, which allow the attackers to identify and bypass the deployed decoys. In this paper, we develop a decoy-enhanced defense framework that can proactively protect critical servers against targeted remote attacks through deception. To achieve both high fidelity and good scalability, our system follows a hybrid architecture that separates lightweight yet versatile front-end proxies from back-end high-fidelity decoy servers. Moreover, our system can further invalidate the attackers' reconnaissance through dynamic proxy address shuffling. To guarantee service availability, we develop a transparent connection translation strategy to maintain existing connections during shuffling. Our evaluation on a prototype implementation demonstrates the effectiveness of our approach in defeating attacker reconnaissance and shows that it only introduces small performance overhead.