Biblio

Filters: Author is Wang, J.  [Clear All Filters]
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.

2019-09-23
Chen, W., Liang, X., Li, J., Qin, H., Mu, Y., Wang, J..  2018.  Blockchain Based Provenance Sharing of Scientific Workflows. 2018 IEEE International Conference on Big Data (Big Data). :3814–3820.
In a research community, the provenance sharing of scientific workflows can enhance distributed research cooperation, experiment reproducibility verification and experiment repeatedly doing. Considering that scientists in such a community are often in a loose relation and distributed geographically, traditional centralized provenance sharing architectures have shown their disadvantages in poor trustworthiness, reliabilities and efficiency. Additionally, they are also difficult to protect the rights and interests of data providers. All these have been largely hindering the willings of distributed scientists to share their workflow provenance. Considering the big advantages of blockchain in decentralization, trustworthiness and high reliability, an approach to sharing scientific workflow provenance based on blockchain in a research community is proposed. To make the approach more practical, provenance is handled on-chain and original data is delivered off-chain. A kind of block structure to support efficient provenance storing and retrieving is designed, and an algorithm for scientists to search workflow segments from provenance as well as an algorithm for experiments backtracking are provided to enhance the experiment result sharing, save computing resource and time cost by avoiding repeated experiments as far as possible. Analyses show that the approach is efficient and effective.
2019-01-21
Fahrbach, M., Miller, G. L., Peng, R., Sawlani, S., Wang, J., Xu, S. C..  2018.  Graph Sketching against Adaptive Adversaries Applied to the Minimum Degree Algorithm. 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS). :101–112.
Motivated by the study of matrix elimination orderings in combinatorial scientific computing, we utilize graph sketching and local sampling to give a data structure that provides access to approximate fill degrees of a matrix undergoing elimination in polylogarithmic time per elimination and query. We then study the problem of using this data structure in the minimum degree algorithm, which is a widely-used heuristic for producing elimination orderings for sparse matrices by repeatedly eliminating the vertex with (approximate) minimum fill degree. This leads to a nearly-linear time algorithm for generating approximate greedy minimum degree orderings. Despite extensive studies of algorithms for elimination orderings in combinatorial scientific computing, our result is the first rigorous incorporation of randomized tools in this setting, as well as the first nearly-linear time algorithm for producing elimination orderings with provable approximation guarantees. While our sketching data structure readily works in the oblivious adversary model, by repeatedly querying and greedily updating itself, it enters the adaptive adversarial model where the underlying sketches become prone to failure due to dependency issues with their internal randomness. We show how to use an additional sampling procedure to circumvent this problem and to create an independent access sequence. Our technique for decorrelating interleaved queries and updates to this randomized data structure may be of independent interest.
2019-10-14
Rong, Z., Xie, P., Wang, J., Xu, S., Wang, Y..  2018.  Clean the Scratch Registers: A Way to Mitigate Return-Oriented Programming Attacks. 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP). :1–8.

With the implementation of W ⊕ X security model on computer system, Return-Oriented Programming(ROP) has become the primary exploitation technique for adversaries. Although many solutions that defend against ROP exploits have been proposed, they still suffer from various shortcomings. In this paper, we propose a new way to mitigate ROP attacks that are based on return instructions. We clean the scratch registers which are also the parameter registers based on the features of ROP malicious code and calling convention. A prototype is implemented on x64-based Linux platform based on Pin. Preliminary experimental results show that our method can efficiently mitigate conventional ROP attacks.

2019-06-24
Wang, J., Zhang, X., Zhang, H., Lin, H., Tode, H., Pan, M., Han, Z..  2018.  Data-Driven Optimization for Utility Providers with Differential Privacy of Users' Energy Profile. 2018 IEEE Global Communications Conference (GLOBECOM). :1–6.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

2019-01-21
Xie, P., Feng, J., Cao, Z., Wang, J..  2018.  GeneWave: Fast Authentication and Key Agreement on Commodity Mobile Devices. IEEE/ACM Transactions on Networking. 26:1688–1700.

Device-to-device communication is widely used for mobile devices and Internet of Things. Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then, we derive the initial acoustic channel response for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easyto-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10× faster than the state-of-the-art approach.

2019-03-15
Bian, R., Xue, M., Wang, J..  2018.  Building Trusted Golden Models-Free Hardware Trojan Detection Framework Against Untrustworthy Testing Parties Using a Novel Clustering Ensemble Technique. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1458-1463.

As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).

Xue, M., Bian, R., Wang, J., Liu, W..  2018.  A Co-Training Based Hardware Trojan Detection Technique by Exploiting Unlabeled ICs and Inaccurate Simulation Models. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1452-1457.

Integrated circuits (ICs) are becoming vulnerable to hardware Trojans. Most of existing works require golden chips to provide references for hardware Trojan detection. However, a golden chip is extremely difficult to obtain. In previous work, we have proposed a classification-based golden chips-free hardware Trojan detection technique. However, the algorithm in the previous work are trained by simulated ICs without considering that there may be a shift which occurs between the simulation and the silicon fabrication. It is necessary to learn from actual silicon fabrication in order to obtain an accurate and effective classification model. We propose a co-training based hardware Trojan detection technique exploiting unlabeled fabricated ICs and inaccurate simulation models, to provide reliable detection capability when facing fabricated ICs, while eliminating the need of fabricated golden chips. First, we train two classification algorithms using simulated ICs. During test-time, the two algorithms can identify different patterns in the unlabeled ICs, and thus be able to label some of these ICs for the further training of the another algorithm. Moreover, we use a statistical examination to choose ICs labeling for the another algorithm in order to help prevent a degradation in performance due to the increased noise in the labeled ICs. We also use a statistical technique for combining the hypotheses from the two classification algorithms to obtain the final decision. The theoretical basis of why the co-training method can work is also described. Experiment results on benchmark circuits show that the proposed technique can detect unknown Trojans with high accuracy (92% 97%) and recall (88% 95%).

2019-01-21
Wang, J., Lin, S., Liu, C., Wang, J., Zhu, B., Jiang, Y..  2018.  Secrecy Capacity of Indoor Visible Light Communication Channels. 2018 IEEE International Conference on Communications Workshops (ICC Workshops). :1–6.
In the indoor scenario, visible light communications (VLC) is regarded as one of the most promising candidates for future wireless communications. Recently, the physical layer security for indoor VLC has drawn considerable attention. In this paper, the secrecy capacity of indoor VLC is analyzed. Initially, an VLC system with a transmitter, a legitimate receiver, and an eavesdropper is established. In the system, the nonnegativity, the peak optical intensity constraint and the dimmable average optical intensity constraint are considered. Based on the principle of information theory, the closed-form expressions of the upper and the lower bounds on the secrecy capacity are derived, respectively. Numerical results show that the upper and the lower bounds on secrecy capacity are very tight, which verify the accuracy of the derived closed-form expressions.
2018-01-10
Xie, P., Feng, J., Cao, Z., Wang, J..  2017.  GeneWave: Fast authentication and key agreement on commodity mobile devices. 2017 IEEE 25th International Conference on Network Protocols (ICNP). :1–10.
Device-to-device (D2D) communication is widely used for mobile devices and Internet of Things (IoT). Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then we derive the initial acoustic channel response (ACR) for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easy-to-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10x faster than the state-of-the-art approach.
2018-09-05
Wang, J., Shi, D., Li, Y., Chen, J., Duan, X..  2017.  Realistic measurement protection schemes against false data injection attacks on state estimators. 2017 IEEE Power Energy Society General Meeting. :1–5.
False data injection attacks (FDIA) on state estimators are a kind of imminent cyber-physical security issue. Fortunately, it has been proved that if a set of measurements is strategically selected and protected, no FDIA will remain undetectable. In this paper, the metric Return on Investment (ROI) is introduced to evaluate the overall returns of the alternative measurement protection schemes (MPS). By setting maximum total ROI as the optimization objective, the previously ignored cost-benefit issue is taken into account to derive a realistic MPS for power utilities. The optimization problem is transformed into the Steiner tree problem in graph theory, where a tree pruning based algorithm is used to reduce the computational complexity and find a quasi-optimal solution with acceptable approximations. The correctness and efficiency of the algorithm are verified by case studies.
2018-04-04
Wu, F., Wang, J., Liu, J., Wang, W..  2017.  Vulnerability detection with deep learning. 2017 3rd IEEE International Conference on Computer and Communications (ICCC). :1298–1302.
Vulnerability detection is an import issue in information system security. In this work, we propose the deep learning method for vulnerability detection. We present three deep learning models, namely, convolution neural network (CNN), long short term memory (LSTM) and convolution neural network — long short term memory (CNN-LSTM). In order to test the performance of our approach, we collected 9872 sequences of function calls as features to represent the patterns of binary programs during their execution. We apply our deep learning models to predict the vulnerabilities of these binary programs based on the collected data. The experimental results show that the prediction accuracy of our proposed method reaches 83.6%, which is superior to that of traditional method like multi-layer perceptron (MLP).
2018-03-19
Das, A., Shen, M. Y., Shashanka, M., Wang, J..  2017.  Detection of Exfiltration and Tunneling over DNS. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). :737–742.

This paper proposes a method to detect two primary means of using the Domain Name System (DNS) for malicious purposes. We develop machine learning models to detect information exfiltration from compromised machines and the establishment of command & control (C&C) servers via tunneling. We validate our approach by experiments where we successfully detect a malware used in several recent Advanced Persistent Threat (APT) attacks [1]. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.

2018-03-05
Shen, Y., Chen, W., Wang, J..  2017.  Distributed Self-Healing for Mobile Robot Networks with Multiple Robot Failures. 2017 Chinese Automation Congress (CAC). :5939–5944.

In the multi-robot applications, the maintained and desired network may be destroyed by failed robots. The existing self-healing algorithms only handle with the case of single robot failure, however, multiple robot failures may cause several challenges, such as disconnected network and conflicts among repair paths. This paper presents a distributed self-healing algorithm based on 2-hop neighbor infomation to resolve the problems caused by multiple robot failures. Simulations and experiment show that the proposed algorithm manages to restore connectivity of the mobile robot network and improves the synchronization of the network globally, which validate the effectiveness of the proposed algorithm in resolving multiple robot failures.

2018-05-30
Liu, Y., Li, R., Liu, X., Wang, J., Tang, C., Kang, H..  2017.  Enhancing Anonymity of Bitcoin Based on Ring Signature Algorithm. 2017 13th International Conference on Computational Intelligence and Security (CIS). :317–321.

Bitcoin is a decentralized digital currency, widely used for its perceived anonymity property, and has surged in popularity in recent years. Bitcoin publishes the complete transaction history in a public ledger, under pseudonyms of users. This is an alternative way to prevent double-spending attack instead of central authority. Therefore, if pseudonyms of users are attached to their identities in real world, the anonymity of Bitcoin will be a serious vulnerability. It is necessary to enhance anonymity of Bitcoin by a coin mixing service or other modifications in Bitcoin protocol. But in a coin mixing service, the relationship among input and output addresses is not hidden from the mixing service provider. So the mixing server still has the ability to track the transaction records of Bitcoin users. To solve this problem, We present a new coin mixing scheme to ensure that the relationship between input and output addresses of any users is invisible for the mixing server. We make use of a ring signature algorithm to ensure that the mixing server can't distinguish specific transaction from all these addresses. The ring signature ensures that a signature is signed by one of its users in the ring and doesn't leak any information about who signed it. Furthermore, the scheme is fully compatible with existing Bitcoin protocol and easily to scale for large amount of users.

2018-06-11
Ding, W., Wang, J., Lu, K., Zhao, R., Wang, X., Zhu, Y..  2017.  Optimal Cache Management and Routing for Secure Content Delivery in Information-Centric Networks with Network Coding. 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). :267–274.

Information-Centric Network (ICN) is one of the most promising network architecture to handle the problem of rapid increase of data traffic because it allows in-network cache. ICNs with Linear Network Coding (LNC) can greatly improve the performance of content caching and delivery. In this paper, we propose a Secure Content Caching and Routing (SCCR) framework based on Software Defined Network (SDN) to find the optimal cache management and routing for secure content delivery, which aims to firstly minimize the total cost of cache and bandwidth consumption and then minimize the usage of random chunks to guarantee information theoretical security (ITS). Specifically, we firstly propose the SCCR problem and then introduce the main ideas of the SCCR framework. Next, we formulate the SCCR problem to two Linear Programming (LP) formulations and design the SCCR algorithm based on them to optimally solve the SCCR problem. Finally, extensive simulations are conducted to evaluate the proposed SCCR framework and algorithms.

2018-03-26
Pallaprolu, S. C., Sankineni, R., Thevar, M., Karabatis, G., Wang, J..  2017.  Zero-Day Attack Identification in Streaming Data Using Semantics and Spark. 2017 IEEE International Congress on Big Data (BigData Congress). :121–128.

Intrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.

2018-03-05
Das, A., Shen, M. Y., Wang, J..  2017.  Modeling User Communities for Identifying Security Risks in an Organization. 2017 IEEE International Conference on Big Data (Big Data). :4481–4486.

In this paper, we address the problem of peer grouping employees in an organization for identifying security risks. Our motivation for studying peer grouping is its importance for a clear understanding of user and entity behavior analytics (UEBA) that is the primary tool for identifying insider threat through detecting anomalies in network traffic. We show that using Louvain method of community detection it is possible to automate peer group creation with feature-based weight assignments. Depending on the number of employees and their features we show that it is also possible to give each group a meaningful description. We present three new algorithms: one that allows an addition of new employees to already generated peer groups, another that allows for incorporating user feedback, and lastly one that provides the user with recommended nodes to be reassigned. We use Niara's data to validate our claims. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.

2018-02-06
Zhang, H., Wang, J., Chang, J..  2017.  A Multi-Level Security Access Control Framework for Cross-Domain Networks. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 2:316–319.

The increasing demand for secure interactions between network domains brings in new challenges to access control technologies. In this paper we design an access control framework which provides a multilevel mapping method between hierarchical access control structures for achieving multilevel security protection in cross-domain networks. Hierarchical access control structures ensure rigorous multilevel security in intra domains. And the mapping method based on subject attributes is proposed to determine the subject's security level in its target domain. Experimental results we obtained from simulations are also reported in this paper to verify the effectiveness of the proposed access control model.

2017-03-08
Ji, Y., Wang, J., Yan, S., Gao, W., Li, H..  2015.  Optimal microgrid energy management integrating intermittent renewable energy and stochastic load. 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). :334–338.

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.

Wang, J., Zhou, Y..  2015.  Multi-objective dynamic unit commitment optimization for energy-saving and emission reduction with wind power. 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT). :2074–2078.

As a clean energy, wind power is massively utilized in net recent years, which significantly reduced the pollution emission created from unit. This article referred to the concept of energy-saving and emission reducing; built a multiple objective function with represent of the emission of CO2& SO2, the coal-fired from units and the lowest unit fees of commitment; Proposed a algorithm to improving NSGA-D (Non-dominated Sorting Genetic Algorithm-II) for the dynamic characteristics, consider of some constraint conditions such as the shortest operation and fault time and climbing etc.; Optimized and commitment discrete magnitude and Load distribution continuous quantity with the double-optimization strategy; Introduced the fuzzy satisfaction-maximizing method to reaching a decision for Pareto solution and also nested into each dynamic solution; Through simulation for 10 units of wind power, the result show that this method is an effective way to optimize the Multi-objective unit commitment modeling in wind power integrated system with Mixed-integer variable.