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Zuin, Gianlucca, Chaimowicz, Luiz, Veloso, Adriano.  2018.  Learning Transferable Features For Open-Domain Question Answering. 2018 International Joint Conference on Neural Networks (IJCNN). :1–8.

Corpora used to learn open-domain Question-Answering (QA) models are typically collected from a wide variety of topics or domains. Since QA requires understanding natural language, open-domain QA models generally need very large training corpora. A simple way to alleviate data demand is to restrict the domain covered by the QA model, leading thus to domain-specific QA models. While learning improved QA models for a specific domain is still challenging due to the lack of sufficient training data in the topic of interest, additional training data can be obtained from related topic domains. Thus, instead of learning a single open-domain QA model, we investigate domain adaptation approaches in order to create multiple improved domain-specific QA models. We demonstrate that this can be achieved by stratifying the source dataset, without the need of searching for complementary data unlike many other domain adaptation approaches. We propose a deep architecture that jointly exploits convolutional and recurrent networks for learning domain-specific features while transferring domain-shared features. That is, we use transferable features to enable model adaptation from multiple source domains. We consider different transference approaches designed to learn span-level and sentence-level QA models. We found that domain-adaptation greatly improves sentence-level QA performance, and span-level QA benefits from sentence information. Finally, we also show that a simple clustering algorithm may be employed when the topic domains are unknown and the resulting loss in accuracy is negligible.

Zhu, Yi, Liu, Sen, Newsam, Shawn.  2017.  Large-Scale Mapping of Human Activity Using Geo-Tagged Videos. Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. :68:1–68:4.

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to map activities both spatially and temporally.

Zheng, Yuxin, Guo, Qi, Tung, Anthony K.H., Wu, Sai.  2016.  LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index. Proceedings of the 2016 International Conference on Management of Data. :2023–2037.

Due to the "curse of dimensionality" problem, it is very expensive to process the nearest neighbor (NN) query in high-dimensional spaces; and hence, approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used for their theoretical guarantees and empirical performance. Current LSH-based approaches target at the L1 and L2 spaces, while as shown in previous work, the fractional distance metrics (Lp metrics with 0 textless p textless 1) can provide more insightful results than the usual L1 and L2 metrics for data mining and multimedia applications. However, none of the existing work can support multiple fractional distance metrics using one index. In this paper, we propose LazyLSH that answers approximate nearest neighbor queries for multiple Lp metrics with theoretical guarantees. Different from previous LSH approaches which need to build one dedicated index for every query space, LazyLSH uses a single base index to support the computations in multiple Lp spaces, significantly reducing the maintenance overhead. Extensive experiments show that LazyLSH provides more accurate results for approximate kNN search under fractional distance metrics.

Zhao, Liang, Chen, Liqun.  2018.  A Linear Distinguisher and Its Application for Analyzing Privacy-Preserving Transformation Used in Verifiable (Outsourced) Computation. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :253-260.

A distinguisher is employed by an adversary to explore the privacy property of a cryptographic primitive. If a cryptographic primitive is said to be private, there is no distinguisher algorithm that can be used by an adversary to distinguish the encodings generated by this primitive with non-negligible advantage. Recently, two privacy-preserving matrix transformations first proposed by Salinas et al. have been widely used to achieve the matrix-related verifiable (outsourced) computation in data protection. Salinas et al. proved that these transformations are private (in terms of indistinguishability). In this paper, we first propose the concept of a linear distinguisher and two constructions of the linear distinguisher algorithms. Then, we take those two matrix transformations (including Salinas et al.\$'\$s original work and Yu et al.\$'\$s modification) as example targets and analyze their privacy property when our linear distinguisher algorithms are employed by the adversaries. The results show that those transformations are not private even against passive eavesdropping.

Zhang, Naiji, Jaafar, Fehmi, Malik, Yasir.  2019.  Low-Rate DoS Attack Detection Using PSD Based Entropy and Machine Learning. 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :59–62.
The Distributed Denial of Service attack is one of the most common attacks and it is hard to mitigate, however, it has become more difficult while dealing with the Low-rate DoS (LDoS) attacks. The LDoS exploits the vulnerability of TCP congestion-control mechanism by sending malicious traffic at the low constant rate and influence the victim machine. Recently, machine learning approaches are applied to detect the complex DDoS attacks and improve the efficiency and robustness of the intrusion detection system. In this research, the algorithm is designed to balance the detection rate and its efficiency. The detection algorithm combines the Power Spectral Density (PSD) entropy function and Support Vector Machine to detect LDoS traffic from normal traffic. In our solution, the detection rate and efficiency are adjustable based on the parameter in the decision algorithm. To have high efficiency, the detection method will always detect the attacks by calculating PSD-entropy first and compare it with the two adaptive thresholds. The thresholds can efficiently filter nearly 19% of the samples with a high detection rate. To minimize the computational cost and look only for the patterns that are most relevant for detection, Support Vector Machine based machine learning model is applied to learn the traffic pattern and select appropriate features for detection algorithm. The experimental results show that the proposed approach can detect 99.19% of the LDoS attacks and has an O (n log n) time complexity in the best case.
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.

Zhang, H., Liu, H., Deng, L., Wang, P., Rong, X., Li, Y., Li, B., Wang, H..  2018.  Leader Recognition and Tracking for Quadruped Robots. 2018 IEEE International Conference on Information and Automation (ICIA). :1438—1443.

To meet the high requirement of human-machine interaction, quadruped robots with human recognition and tracking capability are studied in this paper. We first introduce a marker recognition system which uses multi-thread laser scanner and retro-reflective markers to distinguish the robot's leader and other objects. When the robot follows leader autonomously, the variant A* algorithm which having obstacle grids extended virtually (EA*) is used to plan the path. But if robots need to track and follow the leader's path as closely as possible, it will trust that the path which leader have traveled is safe enough and uses the incremental form of EA* algorithm (IEA*) to reproduce the trajectory. The simulation and experiment results illustrate the feasibility and effectiveness of the proposed algorithms.

Zhang, F., Chan, P. P. K., Tang, T. Q..  2015.  L-GEM based robust learning against poisoning attack. 2015 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). :175–178.

Poisoning attack in which an adversary misleads the learning process by manipulating its training set significantly affect the performance of classifiers in security applications. This paper proposed a robust learning method which reduces the influences of attack samples on learning. The sensitivity, defined as the fluctuation of the output with small perturbation of the input, in Localized Generalization Error Model (L-GEM) is measured for each training sample. The classifier's output on attack samples may be sensitive and inaccurate since these samples are different from other untainted samples. An import score is assigned to each sample according to its localized generalization error bound. The classifier is trained using a new training set obtained by resampling the samples according to their importance scores. RBFNN is applied as the classifier in experimental evaluation. The proposed model outperforms than the traditional one under the well-known label flip poisoning attacks including nearest-first and farthest-first flips attack.

Zhan, Dongyang, Li, Huhua, Ye, Lin, Zhang, Hongli, Fang, Binxing, Du, Xiaojiang.  2019.  A Low-Overhead Kernel Object Monitoring Approach for Virtual Machine Introspection. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–6.

Monitoring kernel object modification of virtual machine is widely used by virtual-machine-introspection-based security monitors to protect virtual machines in cloud computing, such as monitoring dentry objects to intercept file operations, etc. However, most of the current virtual machine monitors, such as KVM and Xen, only support page-level monitoring, because the Intel EPT technology can only monitor page privilege. If the out-of-virtual-machine security tools want to monitor some kernel objects, they need to intercept the operation of the whole memory page. Since there are some other objects stored in the monitored pages, the modification of them will also trigger the monitor. Therefore, page-level memory monitor usually introduces overhead to related kernel services of the target virtual machine. In this paper, we propose a low-overhead kernel object monitoring approach to reduce the overhead caused by page-level monitor. The core idea is to migrate the target kernel objects to a protected memory area and then to monitor the corresponding new memory pages. Since the new pages only contain the kernel objects to be monitored, other kernel objects will not trigger our monitor. Therefore, our monitor will not introduce runtime overhead to the related kernel service. The experimental results show that our system can monitor target kernel objects effectively only with very low overhead.

Zhai, Zhongyi, Qian, Junyan, Tao, Yuan, Zhao, Lingzhong, Cheng, Bo.  2018.  A Lightweight Timestamp-Based MAC Detection Scheme for XOR Network Coding in Wireless Sensor Networks. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. :735-737.

Network coding has become a promising approach to improve the communication capability for WSN, which is vulnerable to malicious attacks. There are some solutions, including cryptographic and information-theory schemes, just can thwart data pollution attacks but are not able to detect replay attacks. In the paper, we present a lightweight timestamp-based message authentication code method, called as TMAC. Based on TMAC and the time synchronization technique, the proposed detection scheme can not only resist pollution attacks but also defend replay attacks simultaneously. Finally

Y
Yu, Wenchao, Zheng, Cheng, Cheng, Wei, Aggarwal, Charu C., Song, Dongjin, Zong, Bo, Chen, Haifeng, Wang, Wei.  2018.  Learning Deep Network Representations with Adversarially Regularized Autoencoders. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2663-2671.

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.

Yoshikawa, Masaya, Nozaki, Yusuke.  2018.  Lightweight Cipher Aware Countermeasure Using Random Number Masks and Its Evaluation. Proceedings of the 2Nd International Conference on Vision, Image and Signal Processing. :55:1-55:5.

Recent advancements in the Internet of Things (IoT) technology has left built-in devices vulnerable to interference from external networks. Power analysis attacks against cryptographic circuits are of particular concern, as they operate by illegally analyzing confidential information via power consumption of a cryptographic circuit. In response to these threats, many researchers have turned to lightweight ciphers, which can be embedded in small-scale circuits, coupled with countermeasures to increase built-in device security, even against power analysis attacks. However, while researchers have examined the efficacy of embedding lightweight ciphers in circuits, neither cost nor tamper resistance have been considered in detail. To use lightweight ciphers and improve tamper resistance in the future, it is necessary to investigate the relationship between the cost of embedding a lightweight cipher with a countermeasure against power analysis in a circuit and the tamper resistance of the cipher. Accordingly, the present study determined the tamper resistance of TWINE, a typical lightweight cipher, both with and without a countermeasure; costs were calculated for embedding the cipher with and without a countermeasure as well.

Yoon, Man-Ki, Mohan, Sibin, Choi, Jaesik, Christodorescu, Mihai, Sha, Lui.  2017.  Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System. Proceedings of the Second International Conference on Internet-of-Things Design and Implementation. :191–196.

Existing techniques used for anomaly detection do not fully utilize the intrinsic properties of embedded devices. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. Our prototype applied to a real-world open-source embedded application shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths.

Yongdong, C., Wei, W., Yanling, Z., Jinshuai, W..  2018.  Lightweight Security Signaling Mechanism in Optical Network for Smart Power Grid. 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET). :110–113.

The communication security issue brought by Smart Grid is of great importance and should not be ignored in backbone optical networks. With the aim to solve this problem, this paper firstly conducts deep analysis into the security challenge of optical network under smart power grid environment and proposes a so-called lightweight security signaling mechanism of multi-domain optical network for Energy Internet. The proposed scheme makes full advantage of current signaling protocol with some necessary extensions and security improvement. Thus, this lightweight security signaling protocol is designed to make sure the end-to-end trusted connection. Under the multi-domain communication services of smart power grid, evaluation simulation for the signaling interaction is conducted. Simulation results show that this proposed approach can greatly improve the security level of large-scale multi-domain optical network for smart power grid with better performance in term of connection success rate performance.

Yin, Z., Dou, S., Bai, H., Hou, Y..  2019.  Light-Weighted Security Access Scheme of Broadband Power Line Communications for Multi-Source Information Collection. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1087–1090.

With the continuously development of smart meter-reading technologies for decades, remote information collection of electricity, water, gas and heat meters have been realized. Due to the difference of electrical interfaces and communication protocols among various types of meters, communication modes of meter terminals are not so compatible, it is difficult to realize communication optimization of electricity, water, gas and heat meters information collection services. In addition, with the development of power consumption information acquisition system, the number of acquisition terminals soars greatly and the data of terminal access is highly concurrent. Therefore, the risk of security access is increasing. This paper presents a light-weighted security access scheme of power line communication based on multi-source data acquisition of electricity, water, gas and heat meters, which separates multi-source data acquisition services and achieve services security isolation and channel security isolation. The communication reliability and security of the meter-reading service of "electricity, water, gas and heat" will be improved and the integrated meter service will be realized reliably.

Yi, F., Cai, H. Y., Xin, F. Z..  2018.  A Logic-Based Attack Graph for Analyzing Network Security Risk Against Potential Attack. 2018 IEEE International Conference on Networking, Architecture and Storage (NAS). :1-4.
In this paper, we present LAPA, a framework for automatically analyzing network security risk and generating attack graph for potential attack. The key novelty in our work is that we represent the properties of networks and zero day vulnerabilities, and use logical reasoning algorithm to generate potential attack path to determine if the attacker can exploit these vulnerabilities. In order to demonstrate the efficacy, we have implemented the LAPA framework and compared with three previous network vulnerability analysis methods. Our analysis results have a low rate of false negatives and less cost of processing time due to the worst case assumption and logical property specification and reasoning. We have also conducted a detailed study of the efficiency for generation attack graph with different value of attack path number, attack path depth and network size, which affect the processing time mostly. We estimate that LAPA can produce high quality results for a large portion of networks.
Ye, S., Feigh, K., Howard, A..  2020.  Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :1186—1189.

Embodiment of actions and tasks has typically been analyzed from the robot's perspective where the robot's embodiment helps develop and maintain trust. However, we ask a similar question looking at the interaction from the human perspective. Embodied cognition has been shown in the cognitive science literature to produce increased social empathy and cooperation. To understand how human embodiment can help develop and increase trust in human-robot interactions, we created conducted a study where participants were tasked with memorizing greek letters associated with dance motions with the help of a humanoid robot. Participants either performed the dance motion or utilized a touch screen during the interaction. The results showed that participants' trust in the robot increased at a higher rate during human embodiment of motions as opposed to utilizing a touch screen device.

Ye, M., Hu, N., Wei, S..  2016.  Lightweight secure sensing using hardware isolation. 2016 IEEE SENSORS. :1–3.
This paper develops a new lightweight secure sensing technique using hardware isolation. We focus on protecting the sensor from unauthorized accesses, which can be issued by attackers attempting to compromise the security and privacy of the sensed data. We satisfy the security requirements by employing the hardware isolation feature provided by the secure processor of the target sensor system. In particular, we deploy the sensor in a hardware isolated secure environment, which eliminates the potential vulnerability exposed to unauthorized attackers. We implement the hardware isolation-based secure sensing approach on an Xilinx Zynq-7000 SoC leveraging ARM TrustZone. Our experiments and security analysis on the real hardware prove the effectiveness and low overhead of the proposed approach.
Yaswinski, Matthew R., Chowdhury, Md Minhaz, Jochen, Mike.  2019.  Linux Security: A Survey. 2019 IEEE International Conference on Electro Information Technology (EIT). :357–362.
Linux is used in a large variety of situations, from private homes on personal machines to businesses storing personal data on servers. This operating system is often seen as more secure than Windows or Mac OS X, but this does not mean that there are no security concerns to be had when running it. Attackers can crack simple passwords over a network, vulnerabilities can be exploited if firewalls do not close enough ports, and malware can be downloaded and run on a Linux system. In addition, sensitive information can be accessed through physical or network access if proper permissions are not set on the files or directories containing it. However, most of these attacks can be prevented by keeping a system up to date, maintaining a secure firewall, using an antivirus, making complex passwords, and setting strong file permissions. This paper presents a list of methods for securing a Linux system from both external and internal threats.
Yao, Yuanshun, Li, Huiying, Zheng, Haitao, Zhao, Ben Y..  2019.  Latent Backdoor Attacks on Deep Neural Networks. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. :2041–2055.

Recent work proposed the concept of backdoor attacks on deep neural networks (DNNs), where misclassification rules are hidden inside normal models, only to be triggered by very specific inputs. However, these "traditional" backdoors assume a context where users train their own models from scratch, which rarely occurs in practice. Instead, users typically customize "Teacher" models already pretrained by providers like Google, through a process called transfer learning. This customization process introduces significant changes to models and disrupts hidden backdoors, greatly reducing the actual impact of backdoors in practice. In this paper, we describe latent backdoors, a more powerful and stealthy variant of backdoor attacks that functions under transfer learning. Latent backdoors are incomplete backdoors embedded into a "Teacher" model, and automatically inherited by multiple "Student" models through transfer learning. If any Student models include the label targeted by the backdoor, then its customization process completes the backdoor and makes it active. We show that latent backdoors can be quite effective in a variety of application contexts, and validate its practicality through real-world attacks against traffic sign recognition, iris identification of volunteers, and facial recognition of public figures (politicians). Finally, we evaluate 4 potential defenses, and find that only one is effective in disrupting latent backdoors, but might incur a cost in classification accuracy as tradeoff.

Yao, Lin, Jiang, Binyao, Deng, Jing, Obaidat, Mohammad S..  2019.  LSTM-Based Detection for Timing Attacks in Named Data Network. 2019 IEEE Global Communications Conference (GLOBECOM). :1—6.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.

Yang, Xiaodong, Chen, Guilan, Wang, Meiding, Pei, Xizhen.  2019.  Lightweight Searchable Encryption Scheme Based on Certificateless Cryptosystem. 2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :669–6693.
Searchable encryption technology can guarantee the confidentiality of cloud data and the searchability of ciphertext data, which has a very broad application prospect in cloud storage environments. However, most existing searchable encryption schemes have problems, such as excessive computational overhead and low security. In order to solve these problems, a lightweight searchable encryption scheme based on certificateless cryptosystem is proposed. The user's final private key consists of partial private key and secret value, which effectively solves the certificate management problem of the traditional cryptosystem and the key escrow problem of identity-based cryptosystem. At the same time, the introduction of third-party manager has significantly reduced the burden in the cloud server and achieved lightweight multi-user ciphertext retrieval. In addition, the data owner stores the file index in the third-party manager, while the file ciphertext is stored in the cloud server. This ensures that the file index is not known by the cloud server. The analysis results show that the scheme satisfies trapdoor indistinguishability and can resist keyword guessing attacks. Compared with similar certificateless encryption schemes, it has higher computational performance in key generation, keyword encryption, trapdoor generation and keyword search.
Yang, Wenti, Wang, Ruimiao, Guan, Zhitao, Wu, Longfei, Du, Xiaojiang, Guizani, Mohsen.  2020.  A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.

Yang, B., Xu, G., Zeng, X., Liu, J., Zhang, Y..  2018.  A Lightweight Anonymous Mobile User Authentication Scheme for Smart Grid. 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :821-827.

Smart Grid (SG) technology has been developing for years, which facilitates users with portable access to power through being applied in numerous application scenarios, one of which is the electric vehicle charging. In order to ensure the security of the charging process, users need authenticating with the smart meter for the subsequent communication. Although there are many researches in this field, few of which have endeavored to protect the anonymity and the untraceability of users during the authentication. Further, some studies consider the problem of user anonymity, but they are non-light-weight protocols, even some can not assure any fairness in key agreement. In this paper, we first points out that existing authentication schemes for Smart Grid are neither lack of critical security nor short of important property such as untraceability, then we propose a new two-factor lightweight user authentication scheme based on password and biometric. The authentication process of the proposed scheme includes four message exchanges among the user mobile, smart meter and the cloud server, and then a security one-time session key is generated for the followed communication process. Moreover, the scheme has some new features, such as the protection of the user's anonymity and untraceability. Security analysis shows that our proposed scheme can resist various well-known attacks and the performance analysis shows that compared to other three schemes, our scheme is more lightweight, secure and efficient.

Yan-Tao, Zhong.  2018.  Lattice Based Authenticated Key Exchange with Universally Composable Security. 2018 International Conference on Networking and Network Applications (NaNA). :86–90.

The Internet of things (IoT) has experienced rapid development these years, while its security and privacy remains a major challenge. One of the main security goals for the IoT is to build secure and authenticated channels between IoT nodes. A common way widely used to achieve this goal is using authenticated key exchange protocol. However, with the increasing progress of quantum computation, most authenticated key exchange protocols nowadays are threatened by the rise of quantum computers. In this study, we address this problem by using ring-SIS based KEM and hash function to construct an authenticated key exchange scheme so that we base the scheme on lattice based hard problems believed to be secure even with quantum attacks. We also prove the security of universal composability of our scheme. The scheme hence can keep security while runs in complicated environment.