Visible to the public Biblio

Filters: Author is Zhang, L.  [Clear All Filters]
2019-09-05
Sun, Y., Zhang, L., Zhao, C..  2018.  A Study of Network Covert Channel Detection Based on Deep Learning. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :637-641.
Information security has become a growing concern. Computer covert channel which is regarded as an important area of information security research gets more attention. In order to detect these covert channels, a variety of detection algorithms are proposed in the course of the research. The algorithms of machine learning type show better results in these detection algorithms. However, the common machine learning algorithms have many problems in the testing process and have great limitations. Based on the deep learning algorithm, this paper proposes a new idea of network covert channel detection and forms a new detection model. On the one hand, this algorithmic model can detect more complex covert channels and, on the other hand, greatly improve the accuracy of detection due to the use of a new deep learning model. By optimizing this test model, we can get better results on the evaluation index.
2019-06-10
Xue, S., Zhang, L., Li, A., Li, X., Ruan, C., Huang, W..  2018.  AppDNA: App Behavior Profiling via Graph-Based Deep Learning. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1475-1483.

Better understanding of mobile applications' behaviors would lead to better malware detection/classification and better app recommendation for users. In this work, we design a framework AppDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The behavior difference between two apps can be measured by the distance between their representations. As a result, the versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorizing, plagiarism detection, etc. Based on a systematic and deep understanding of an app's behavior, we propose to perform a function-call-graph-based app profiling. We carefully design a graph-encoding method to convert a typically extremely large call-graph to a 64-dimension fix-size vector to achieve robust app profiling. Our extensive evaluations based on 86,332 benign and malicious apps demonstrate that our system performs app profiling (thus malware detection, classification, and app recommendation) to a high accuracy with extremely low computation cost: it classifies 4024 (benign/malware) apps using around 5.06 second with accuracy about 93.07%; it classifies 570 malware's family (total 21 families) using around 0.83 second with accuracy 82.3%; it classifies 9,730 apps' functionality with accuracy 33.3% for a total of 7 categories and accuracy of 88.1 % for 2 categories.

2019-04-01
Zhang, T., Zheng, H., Zhang, L..  2018.  Verification CAPTCHA Based on Deep Learning. 2018 37th Chinese Control Conference (CCC). :9056–9060.
At present, the captcha is widely used in the Internet. The method of captcha recognition using the convolutional neural networks was introduced in this paper. It was easier to apply the convolution neural network model of simple training to segment the captcha, and the network structure was established imitating VGGNet model. and the correct rate can be reached more than 90%. For the more difficult segmentation captcha, it can be used the end-to-end thought to the captcha as a whole to training, In this way, the recognition rate of the more difficult segmentation captcha can be reached about 85%.
2019-03-22
Quweider, M., Lei, H., Zhang, L., Khan, F..  2018.  Managing Big Data in Visual Retrieval Systems for DHS Applications: Combining Fourier Descriptors and Metric Space Indexing. 2018 1st International Conference on Data Intelligence and Security (ICDIS). :188-193.

Image retrieval systems have been an active area of research for more than thirty years progressively producing improved algorithms that improve performance metrics, operate in different domains, take advantage of different features extracted from the images to be retrieved, and have different desirable invariance properties. With the ever-growing visual databases of images and videos produced by a myriad of devices comes the challenge of selecting effective features and performing fast retrieval on such databases. In this paper, we incorporate Fourier descriptors (FD) along with a metric-based balanced indexing tree as a viable solution to DHS (Department of Homeland Security) needs to for quick identification and retrieval of weapon images. The FDs allow a simple but effective outline feature representation of an object, while the M-tree provide a dynamic, fast, and balanced search over such features. Motivated by looking for applications of interest to DHS, we have created a basic guns and rifles databases that can be used to identify weapons in images and videos extracted from media sources. Our simulations show excellent performance in both representation and fast retrieval speed.

2019-01-21
Gao, J., Wang, J., Zhang, L., Yu, Q., Huang, Y., Shen, Y..  2019.  Magnetic Signature Analysis for Smart Security System Based on TMR Magnetic Sensor Array. IEEE Sensors Journal. :1–1.

This paper presents a novel low power security system based on magnetic anomaly detection by using Tunneling Magnetoresistance (TMR) magnetic sensors. In this work, a smart light has been developed, which consists of TMR sensors array, detection circuits, a micro-controller and a battery. Taking the advantage of low power consumption of TMR magnetic sensors, the smart light powered by Li-ion battery can work for several months. Power Spectrum Density of the obtained signal was analyzed to reject background noise and improve the signal to noise ratio effectively by 1.3 dB, which represented a 30% detection range improvement. Also, by sending the signals to PC, the magnetic fingerprints of the objects have been configured clearly. In addition, the quick scan measurement has been also performed to demonstrate that the system can discriminate the multiple objects with 30 cm separation. Since the whole system was compact and portable, it can be used for security check at office, meeting room or other private places without attracting any attention. Moreover, it is promising to integrate multiply such systems together to achieve a wireless security network in large-scale monitoring.

2019-01-16
Khan, F., Quweider, M., Torres, M., Goldsmith, C., Lei, H., Zhang, L..  2018.  Block Level Streaming Based Alternative Approach for Serving a Large Number of Workstations Securely and Uniformly. 2018 1st International Conference on Data Intelligence and Security (ICDIS). :92–98.
There are different traditional approaches to handling a large number of computers or workstations in a campus setting, ranging from imaging to virtualized environments. The common factor among the traditional approaches is to have a user workstation with a local hard drive (nonvolatile storage), scratchpad volatile memory, a CPU (Central Processing Unit) and connectivity to access resources on the network. This paper presents the use of block streaming, normally used for storage, to serve operating system and applications on-demand over the network to a workstation, also referred to as a client, a client computer, or a client workstation. In order to avoid per seat licensing, an Open Source solution is used, and in order to minimize the field maintenance and meet security privacy constraints, a workstation need not have a permanent storage such as a hard disk drive. A complete blue print, based on performance analyses, is provided to determine the type of network architecture, servers, workstations per server, and minimum workstation configuration, suitable for supporting such a solution. The results of implementing the proposed solution campus wide, supporting more than 450 workstations, are presented as well.
2018-11-19
Wang, Y., Zhang, L..  2017.  High Security Orthogonal Factorized Channel Scrambling Scheme with Location Information Embedded for MIMO-Based VLC System. 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). :1–5.
The broadcast nature of visible light beam has aroused great concerns about the privacy and confidentiality of visible light communication (VLC) systems.In this paper, in order to enhance the physical layer security, we propose a channel scrambling scheme, which realizes orthogonal factorized channel scrambling with location information embedded (OFCS-LIE) for the VLC systems. We firstly embed the location information of the legitimate user, including the transmission angle and the distance, into a location information embedded (LIE) matrix, then the LIE matrix is factorized orthogonally in order that the LIE matrix is approximately uncorrelated to the multiple-input, multiple-output (MIMO) channels by the iterative orthogonal factorization method, where the iteration number is determined based on the orthogonal error. The resultant OFCS-LIE matrix is approximately orthogonal and used to enhance both the reliability and the security of information transmission. Furthermore, we derive the information leakage at the eavesdropper and the secrecy capacity to analyze the system security. Simulations are performed, and the results demonstrate that with the aid of the OFCS-LIE scheme, MIMO-based VLC system has achieved higher security when compared with the counterpart scrambling scheme and the system without scrambling.
2018-06-20
Zhang, L., Li, C., Li, Y., Luo, Q., Zhu, R..  2017.  Group signature based privacy protection algorithm for mobile ad hoc network. 2017 IEEE International Conference on Information and Automation (ICIA). :947–952.

Nowadays, Vehicular ad hoc Network as a special class of Mobile ad hoc Network(MANET), provides plenty of services. However, it also brings the privacy protection issues, and there are conflicts between the privacy protection and the services. In this paper, we will propose a privacy protection algorithm based on group signature including two parts, group signature based anonymous verification and batch verification. The anonymous verification is based on the network model we proposed, which can reduce the trust authority burden by dividing the roadside units into different levels, and the batch verification can reduce the time of message verification in one group. We also prove our algorithm can satisfy the demand of privacy protection. Finally, the simulation shows that the algorithm we proposed is better than the BBS on the length of the signature, time delay and packet loss rate.

2018-01-10
Zhang, L., Restuccia, F., Melodia, T., Pudlewski, S. M..  2017.  Learning to detect and mitigate cross-layer attacks in wireless networks: Framework and applications. 2017 IEEE Conference on Communications and Network Security (CNS). :1–9.

Security threats such as jamming and route manipulation can have significant consequences on the performance of modern wireless networks. To increase the efficacy and stealthiness of such threats, a number of extremely challenging, next-generation cross-layer attacks have been recently unveiled. Although existing research has thoroughly addressed many single-layer attacks, the problem of detecting and mitigating cross-layer attacks still remains unsolved. For this reason, in this paper we propose a novel framework to analyze and address cross-layer attacks in wireless networks. Specifically, our framework consists of a detection and a mitigation component. The attack detection component is based on a Bayesian learning detection scheme that constructs a model of observed evidence to identify stealthy attack activities. The mitigation component comprises a scheme that achieves the desired trade-off between security and performance. We specialize and evaluate the proposed framework by considering a specific cross-layer attack that uses jamming as an auxiliary tool to achieve route manipulation. Simulations and experimental results obtained with a testbed made up by USRP software-defined radios demonstrate the effectiveness of the proposed methodology.

2017-12-12
Zheng, L., Xue, Y., Zhang, L., Zhang, R..  2017.  Mutual Authentication Protocol for RFID Based on ECC. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 2:320–323.

In this paper, a mutual authentication protocol based on ECC is designed for RFID systems. This protocol is described in detail and the performance of this protocol is analyzed. The results show that the protocol has many advantages, such as mutual authentication, confidentiality, anonymity, availability, forward security, scalability and so on, which can resist camouflage attacks, tracking attacks, denial of service attacks, system internal attack.

Ullah, S., Li, X. Y., Zhang, L..  2017.  A Review of Signcryption Schemes Based on Hyper Elliptic Curve. 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM). :51–58.

Now-a-days security is a challenging task in different types of networks, such as Mobile Networks, Wireless Sensor Networks (WSN) and Radio Frequency Identifications Systems (RFIS) etc, to overcome these challenges we use sincryption. Signcryption is a new public key cryptographic primitive that performs the functions of digital signature and encryption in single logical step. The main contribution of signcrytion scheme, it is more suitable for low constrained environment. Moreover some signcryption schemes based on RSA, Elliptic Curve (EC) and Hyper Elliptic Curve (HEC). This paper contains a critical review of signcryption schemes based on hyper elliptic curve, since it reduce communication and computational costs for low constrained devices. It also explores advantages and disadvantages of different signcryption schemes based on HEC.

2017-03-08
Sim, T., Zhang, L..  2015.  Controllable Face Privacy. 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). 04:1–8.

We present the novel concept of Controllable Face Privacy. Existing methods that alter face images to conceal identity inadvertently also destroy other facial attributes such as gender, race or age. This all-or-nothing approach is too harsh. Instead, we propose a flexible method that can independently control the amount of identity alteration while keeping unchanged other facial attributes. To achieve this flexibility, we apply a subspace decomposition onto our face encoding scheme, effectively decoupling facial attributes such as gender, race, age, and identity into mutually orthogonal subspaces, which in turn enables independent control of these attributes. Our method is thus useful for nuanced face de-identification, in which only facial identity is altered, but others, such gender, race and age, are retained. These altered face images protect identity privacy, and yet allow other computer vision analyses, such as gender detection, to proceed unimpeded. Controllable Face Privacy is therefore useful for reaping the benefits of surveillance cameras while preventing privacy abuse. Our proposal also permits privacy to be applied not just to identity, but also to other facial attributes as well. Furthermore, privacy-protection mechanisms, such as k-anonymity, L-diversity, and t-closeness, may be readily incorporated into our method. Extensive experiments with a commercial facial analysis software show that our alteration method is indeed effective.

2017-02-27
Zhang, L., Li, B., Zhang, L., Li, D..  2015.  Fuzzy clustering of incomplete data based on missing attribute interval size. 2015 IEEE 9th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :101–104.

Fuzzy c-means algorithm is used to identity clusters of similar objects within a data set, while it is not directly applied to incomplete data. In this paper, we proposed a novel fuzzy c-means algorithm based on missing attribute interval size for the clustering of incomplete data. In the new algorithm, incomplete data set was transformed to interval data set according to the nearest neighbor rule. The missing attribute value was replaced by the corresponding interval median and the interval size was set as the additional property for the incomplete data to control the effect of interval size in clustering. Experiments on standard UCI data set show that our approach outperforms other clustering methods for incomplete data.