Visible to the public Biblio

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2019-08-12
Liu, Y., Yang, Y., Shi, A., Jigang, P., Haowei, L..  2019.  Intelligent monitoring of indoor surveillance video based on deep learning. 2019 21st International Conference on Advanced Communication Technology (ICACT). :648–653.

With the rapid development of information technology, video surveillance system has become a key part in the security and protection system of modern cities. Especially in prisons, surveillance cameras could be found almost everywhere. However, with the continuous expansion of the surveillance network, surveillance cameras not only bring convenience, but also produce a massive amount of monitoring data, which poses huge challenges to storage, analytics and retrieval. The smart monitoring system equipped with intelligent video analytics technology can monitor as well as pre-alarm abnormal events or behaviours, which is a hot research direction in the field of surveillance. This paper combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image while simultaneously generating a high-quality segmentation mask for each instance. The experiment show that our network is simple to train and easy to generalize to other datasets, and the mask average precision is nearly up to 98.5% on our own datasets.

2019-04-01
Wang, M., Yang, Y., Zhu, M., Liu, J..  2018.  CAPTCHA Identification Based on Convolution Neural Network. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :364–368.
The CAPTCHA is an effective method commonly used in live interactive proofs on the Internet. The widely used CAPTCHAs are text-based schemes. In this paper, we document how we have broken such text-based scheme used by a website CAPTCHA. We use the sliding window to segment 1001 pieces of CAPTCHA to get 5900 images with single-character useful information, a total of 25 categories. In order to make the convolution neural network learn more image features, we augmented the data set to get 129924 pictures. The data set is trained and tested in AlexNet and GoogLeNet to get the accuracy of 87.45% and 98.92%, respectively. The experiment shows that the optimized network parameters can make the accuracy rate up to 92.7% in AlexNet and 98.96% in GoogLeNet.
2019-03-15
Ye, J., Yang, Y., Gong, Y., Hu, Y., Li, X..  2018.  Grey Zone in Pre-Silicon Hardware Trojan Detection. 2018 IEEE International Test Conference in Asia (ITC-Asia). :79-84.

Pre-Silicon hardware Trojan detection has been studied for years. The most popular benchmark circuits are from the Trust-Hub. Their common feature is that the probability of activating hardware Trojans is very low. This leads to a series of machine learning based hardware Trojan detection methods which try to find the nets with low signal probability of 0 or 1. On the other hand, it is considered that, if the probability of activating hardware Trojans is high, these hardware Trojans can be easily found through behaviour simulations or during functional test. This paper explores the "grey zone" between these two opposite scenarios: if the activation probability of a hardware Trojan is not low enough for machine learning to detect it and is not high enough for behaviour simulation or functional test to find it, it can escape from detection. Experiments show the existence of such hardware Trojans, and this paper suggests a new set of hardware Trojan benchmark circuits for future study.

2019-02-08
Zou, Z., Wang, D., Yang, H., Hou, Y., Yang, Y., Xu, W..  2018.  Research on Risk Assessment Technology of Industrial Control System Based on Attack Graph. 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). :2420-2423.

In order to evaluate the network security risks and implement effective defenses in industrial control system, a risk assessment method for industrial control systems based on attack graphs is proposed. Use the concept of network security elements to translate network attacks into network state migration problems and build an industrial control network attack graph model. In view of the current subjective evaluation of expert experience, the atomic attack probability assignment method and the CVSS evaluation system were introduced to evaluate the security status of the industrial control system. Finally, taking the centralized control system of the thermal power plant as the experimental background, the case analysis is performed. The experimental results show that the method can comprehensively analyze the potential safety hazards in the industrial control system and provide basis for the safety management personnel to take effective defense measures.

2019-01-31
Liao, Y., Zhou, J., Yang, Y., Ruan, O..  2018.  An Efficient Oblivious Transfer Protocol with Access Control. 2018 13th Asia Joint Conference on Information Security (AsiaJCIS). :29–34.

Due to the rapid development of internet in our daily life, protecting privacy has become a focus of attention. To create privacy-preserving database and prevent illegal user access the database, oblivious transfer with access control (OTAC) was proposed, which is a cryptographic primitive that extends from oblivious transfer (OT). It allows a user to anonymously query a database where each message is protected by an access control policy and only if the user' s attribute satisfy that access control policy can obtain it. In this paper, we propose a new protocol for OTAC by using elliptic curve cryptography, which is more efficient compared to the existing similar protocols. In our scheme, we also preserves user's anonymity and ensures that the user's attribute is not disclosed to the sender. Additionally, our construction guarantees the user to verify the correctness of messages recovered at the end of each transfer phase.

2018-11-19
Yin, H., Yin, Z., Yang, Y., Sun, J..  2018.  Research on the Node Information Security of WSN Based on Multi-Party Data Fusion Algorithm. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :400–405.
Smart grid is the cornerstone of the modern urban construction, leading the development trend of the urban power industry. Wireless sensor network (WSN) is widely used in smart power grid. It mainly covers two routing methods, the plane routing protocol and the clustering routing protocol. Since the plane routing protocol needs to maintain a large routing table and works with a poor scalability, it will increase the overall cost of the system in practical use. Therefore, in this paper, the clustering routing protocol is selected to achieve a better operation performance of the wireless sensor network. In order to enhance the reliability of the routing security, the data fusion technology is also utilized. Based on this method, the rationality of the topology structure of the smart grid and the security of the node information can be effectively improved.
2018-09-28
Yang, Y., Wunsch, D., Yin, Y..  2017.  Hamiltonian-driven adaptive dynamic programming for nonlinear discrete-time dynamic systems. 2017 International Joint Conference on Neural Networks (IJCNN). :1339–1346.

In this paper, based on the Hamiltonian, an alternative interpretation about the iterative adaptive dynamic programming (ADP) approach from the perspective of optimization is developed for discrete time nonlinear dynamic systems. The role of the Hamiltonian in iterative ADP is explained. The resulting Hamiltonian driven ADP is able to evaluate the performance with respect to arbitrary admissible policies, compare two different admissible policies and further improve the given admissible policy. The convergence of the Hamiltonian ADP to the optimal policy is proven. Implementation of the Hamiltonian-driven ADP by neural networks is discussed based on the assumption that each iterative policy and value function can be updated exactly. Finally, a simulation is conducted to verify the effectiveness of the presented Hamiltonian-driven ADP.

2018-06-07
Yang, Y., Chen, J., Huang, Y., Wang, X..  2017.  Security-reliability tradeoff for cooperative multi-relay and jammer selection in Nakagami-m fading channels. 2017 IEEE 17th International Conference on Communication Technology (ICCT). :181–186.
In this paper, we analyze the security-reliability tradeoff (SRT) performance of the multi-relay cooperative networks over Nakagami-m fading channels. By considering the reliability of the first phase from the source to relay, a cooperative jamming (CJ) assisted secure transmission scheme is investigated to improve the security performance of the considered system. Specifically, we derive the approximate closed-form expression of the outage probability (OP) and exact closed-form expression of the intercepted probability (IP) for the CJ scheme to evaluate the SRT performance of the system. Finally, the simulation results verify the validity of our theoretical derivations and the advantage of the CJ scheme compared to the traditional scheme with no cooperative jammer.
2018-04-11
Yang, Y., Wu, L., Zhang, X., He, J..  2017.  A Novel Hardware Trojan Detection with Chip ID Based on Relative Time Delays. 2017 11th IEEE International Conference on Anti-Counterfeiting, Security, and Identification (ASID). :163–167.

This paper introduces a hardware Trojan detection method using Chip ID which is generated by Relative Time-Delays (RTD) of sensor chains and the effectiveness of RTD is verified by post-layout simulations. The rank of time-delays of the sensor chains would be changed in Trojan-inserted chip. RTD is an accurate approach targeting to all kinds of Trojans, since it is based on the RELATIVE relationship between the time-delays rather than the absolute values, which are hard to be measured and will change with the fabricate process. RTD needs no golden chip, because the RELATIVE values would not change in most situations. Thus the genuine ID can be generated by simulator. The sensor chains can be inserted into a layout utilizing unused spaces, so RTD is a low-cost solution. A Trojan with 4x minimum NMOS is placed in different places of the chip. The behavior of the chip is obtained by using transient based post-layout simulation. All the Trojans are detected AND located, thus the effectiveness of RTD is verified.

2018-03-19
Liu, B., Zhu, Z., Yang, Y..  2017.  Convolutional Neural Networks Based Scale-Adaptive Kernelized Correlation Filter for Robust Visual Object Tracking. 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). :423–428.
Visual object tracking is challenging when the object appearances occur significant changes, such as scale change, background clutter, occlusion, and so on. In this paper, we crop different sizes of multiscale templates around object and input these multiscale templates into network to pretrain the network adaptive the size change of tracking object. Different from previous the tracking method based on deep convolutional neural network (CNN), we exploit deep Residual Network (ResNet) to offline train a multiscale object appearance model on the ImageNet, and then the features from pretrained network are transferred into tracking tasks. Meanwhile, the proposed method combines the multilayer convolutional features, it is robust to disturbance, scale change, and occlusion. In addition, we fuse multiscale search strategy into three kernelized correlation filter, which strengthens the ability of adaptive scale change of object. Unlike the previous methods, we directly learn object appearance change by integrating multiscale templates into the ResNet. We compared our method with other CNN-based or correlation filter tracking methods, the experimental results show that our tracking method is superior to the existing state-of-the-art tracking method on Object Tracking Benchmark (OTB-2015) and Visual Object Tracking Benchmark (VOT-2015).
2018-03-05
Guan, C., Mohaisen, A., Sun, Z., Su, L., Ren, K., Yang, Y..  2017.  When Smart TV Meets CRN: Privacy-Preserving Fine-Grained Spectrum Access. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). :1105–1115.

Dynamic spectrum sharing techniques applied in the UHF TV band have been developed to allow secondary WiFi transmission in areas with active TV users. This technique of dynamically controlling the exclusion zone enables vastly increasing secondary spectrum re-use, compared to the "TV white space" model where TV transmitters determine the exclusion zone and only "idle" channels can be re-purposed. However, in current such dynamic spectrum sharing systems, the sensitive operation parameters of both primary TV users (PUs) and secondary users (SUs) need to be shared with the spectrum database controller (SDC) for the purpose of realizing efficient spectrum allocation. Since such SDC server is not necessarily operated by a trusted third party, those current systems might cause essential threatens to the privacy requirement from both PUs and SUs. To address this privacy issue, this paper proposes a privacy-preserving spectrum sharing system between PUs and SUs, which realizes the spectrum allocation decision process using efficient multi-party computation (MPC) technique. In this design, the SDC only performs secure computation over encrypted input from PUs and SUs such that none of the PU or SU operation parameters will be revealed to SDC. The evaluation of its performance illustrates that our proposed system based on efficient MPC techniques can perform dynamic spectrum allocation process between PUs and SUs efficiently while preserving users' privacy.

2018-02-21
Yuan, Y., Wu, L., Zhang, X., Yang, Y..  2017.  Side-channel collision attack based on multiple-bits. 2017 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). :1–5.

Side-channel collision attacks have been one of the most powerful attack techniques, combining advantages of traditional side-channel attack and mathematical cryptanalysis. In this paper, we propose a novel multiple-bits side-channel collision attack based on double distance voting detection, which can find all 120 relations among 16 key bytes with only 32 averaged power traces when applied to AES (Advanced Encryption Standard) algorithm. Practical attack experiments are performed successfully on a hardware implementation of AES on FPGA board. Results show that the necessary number of traces for our method is about 50% less than correlation-enhanced collision attack and 76% less than binary voting test with 90% success rate.

2018-02-14
Yang, Y., Liu, X., Deng, R. H., Weng, J..  2017.  Flexible Wildcard Searchable Encryption System. IEEE Transactions on Services Computing. PP:1–1.

Searchable encryption is an important technique for public cloud storage service to provide user data confidentiality protection and at the same time allow users performing keyword search over their encrypted data. Previous schemes only deal with exact or fuzzy keyword search to correct some spelling errors. In this paper, we propose a new wildcard searchable encryption system to support wildcard keyword queries which has several highly desirable features. First, our system allows multiple keywords search in which any queried keyword may contain zero, one or two wildcards, and a wildcard may appear in any position of a keyword and represent any number of symbols. Second, it supports simultaneous search on multiple data owner’s data using only one trapdoor. Third, it provides flexible user authorization and revocation to effectively manage search and decryption privileges. Fourth, it is constructed based on homomorphic encryption rather than Bloom filter and hence completely eliminates the false probability caused by Bloom filter. Finally, it achieves a high level of privacy protection since matching results are unknown to the cloud server in the test phase. The proposed system is thoroughly analyzed and is proved secure. Extensive experimental results indicate that our system is efficient compared with other existing wildcard searchable encryption schemes in the public key setting.

2017-12-12
Pan, X., Yang, Y., Zhang, G., Zhang, B..  2017.  Resilience-based optimization of recovery strategies for network systems. 2017 Second International Conference on Reliability Systems Engineering (ICRSE). :1–6.

Network systems, such as transportation systems and water supply systems, play important roles in our daily life and industrial production. However, a variety of disruptive events occur during their life time, causing a series of serious losses. Due to the inevitability of disruption, we should not only focus on improving the reliability or the resistance of the system, but also pay attention to the ability of the system to response timely and recover rapidly from disruptive events. That is to say we need to pay more attention to the resilience. In this paper, we describe two resilience models, quotient resilience and integral resilience, to measure the final recovered performance and the performance cumulative process during recovery respectively. Based on these two models, we implement the optimization of the system recovery strategies after disruption, focusing on the repair sequence of the damaged components and the allocation scheme of resource. The proposed research in this paper can serve as guidance to prioritize repair tasks and allocate resource reasonably.

2017-11-20
You, L., Li, Y., Wang, Y., Zhang, J., Yang, Y..  2016.  A deep learning-based RNNs model for automatic security audit of short messages. 2016 16th International Symposium on Communications and Information Technologies (ISCIT). :225–229.

The traditional text classification methods usually follow this process: first, a sentence can be considered as a bag of words (BOW), then transformed into sentence feature vector which can be classified by some methods, such as maximum entropy (ME), Naive Bayes (NB), support vector machines (SVM), and so on. However, when these methods are applied to text classification, we usually can not obtain an ideal result. The most important reason is that the semantic relations between words is very important for text categorization, however, the traditional method can not capture it. Sentiment classification, as a special case of text classification, is binary classification (positive or negative). Inspired by the sentiment analysis, we use a novel deep learning-based recurrent neural networks (RNNs)model for automatic security audit of short messages from prisons, which can classify short messages(secure and non-insecure). In this paper, the feature of short messages is extracted by word2vec which captures word order information, and each sentence is mapped to a feature vector. In particular, words with similar meaning are mapped to a similar position in the vector space, and then classified by RNNs. RNNs are now widely used and the network structure of RNNs determines that it can easily process the sequence data. We preprocess short messages, extract typical features from existing security and non-security short messages via word2vec, and classify short messages through RNNs which accept a fixed-sized vector as input and produce a fixed-sized vector as output. The experimental results show that the RNNs model achieves an average 92.7% accuracy which is higher than SVM.

2017-03-08
Mao, Y., Yang, J., Zhu, B., Yang, Y..  2015.  A new mesh simplification algorithm based on quadric error metric. 2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin). :463–466.

This paper proposes an improved mesh simplification algorithm based on quadric error metrics (QEM) to efficiently processing the huge data in 3D image processing. This method fully uses geometric information around vertices to avoid model edge from being simplified and to keep details. Meanwhile, the differences between simplified triangular meshes and equilateral triangles are added as weights of errors to decrease the possibilities of narrow triangle and then to avoid the visual mutation. Experiments show that our algorithm has obvious advantages over the time cost, and can better save the visual characteristics of model, which is suitable for solving most image processing, that is, "Real-time interactive" problem.

2015-05-01
Yang, Y., McLaughlin, K., Sezer, S., Littler, T., Im, E.G., Pranggono, B., Wang, H.F..  2014.  Multiattribute SCADA-Specific Intrusion Detection System for Power Networks. Power Delivery, IEEE Transactions on. 29:1092-1102.

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.