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Zuva, Keneilwe, Zuva, Tranos.  2017.  Diversity and Serendipity in Recommender Systems. Proceedings of the International Conference on Big Data and Internet of Thing. :120–124.

The present age of digital information has presented a heterogeneous online environment which makes it a formidable mission for a noble user to search and locate the required online resources timely. Recommender systems were implemented to rescue this information overload issue. However, majority of recommendation algorithms focused on the accuracy of the recommendations, leaving out other important aspects in the definition of good recommendation such as diversity and serendipity. This results in low coverage, long-tail items often are left out in the recommendations as well. In this paper, we present and explore a recommendation technique that ensures that diversity, accuracy and serendipity are all factored in the recommendations. The proposed algorithm performed comparatively well as compared to other algorithms in literature.

Zurek, E.E., Gamarra, A.M.R., Escorcia, G.J.R., Gutierrez, C., Bayona, H., Perez, R., Garcia, X..  2014.  Spectral analysis techniques for acoustic fingerprints recognition. Image, Signal Processing and Artificial Vision (STSIVA), 2014 XIX Symposium on. :1-5.

This article presents results of the recognition process of acoustic fingerprints from a noise source using spectral characteristics of the signal. Principal Components Analysis (PCA) is applied to reduce the dimensionality of extracted features and then a classifier is implemented using the method of the k-nearest neighbors (KNN) to identify the pattern of the audio signal. This classifier is compared with an Artificial Neural Network (ANN) implementation. It is necessary to implement a filtering system to the acquired signals for 60Hz noise reduction generated by imperfections in the acquisition system. The methods described in this paper were used for vessel recognition.

Zúquete, André.  2016.  Exploitation of Dual Channel Transmissions to Increase Security and Reliability in Classic Bluetooth Piconets. Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks. :55–60.

In this paper we discuss several improvements to the security and reliability of a classic Bluetooth network (piconet) that can arise from the fact of being able to transmit the same frame with two frequencies on each slot, instead of the actual standard, that uses only one frequency. Furthermore, we build upon this possibility and we show that piconet participants can explore many strategies to increase the security of their communications by confounding eavesdroppers, such as multiple hopping sequences, random selection of a hopping sequence on each transmission slot and variable frame encryption per hopping sequence. Finally, all this can be decided independently by any piconet participant without having to agree in real time on some type of service with other participants of the same piconet.

Zuo, Xinbin, Pang, Xue, Zhang, Pengping, Zhang, Junsan, Dong, Tao, Zhang, Peiying.  2020.  A Security-Aware Software-Defined IoT Network Architecture. 2020 IEEE Computing, Communications and IoT Applications (ComComAp). :1–5.
With the improvement of people's living standards, more and more network users access the network, including a large number of infrastructure, these devices constitute the Internet of things(IoT). With the rapid expansion of devices in the IoT, the data transmission between the IoT has become more complex, and the security issues are facing greater challenges. SDN as a mature network architecture, its security has been affirmed by the industry, it separates the data layer from the control layer, thus greatly improving the security of the network. In this paper, we apply the SDN to the IoT, and propose a IoT network architecture based on SDN. In this architecture, we not only make use of the security features of SDN, but also deploy different security modules in each layer of SDN to integrate, analyze and plan various data through the IoT, which undoubtedly improves the security performance of the network. In the end, we give a comprehensive introduction to the system and verify its performance.
Zuo, Pengfei, Hua, Yu, Wang, Cong, Xia, Wen, Cao, Shunde, Zhou, Yukun, Sun, Yuanyuan.  2017.  Mitigating Traffic-Based Side Channel Attacks in Bandwidth-Efficient Cloud Storage. Proceedings of the 2017 Symposium on Cloud Computing. :638–638.

Data deduplication [3] is able to effectively identify and eliminate redundant data and only maintain a single copy of files and chunks. Hence, it is widely used in cloud storage systems to save the users' network bandwidth for uploading data. However, the occurrence of deduplication can be easily identified by monitoring and analyzing network traffic, which leads to the risk of user privacy leakage. The attacker can carry out a very dangerous side channel attack, i.e., learn-the-remaining-information (LRI) attack, to reveal users' privacy information by exploiting the side channel of network traffic in deduplication [1]. In the LRI attack, the attacker knows a large part of the target file in the cloud and tries to learn the remaining unknown parts via uploading all possible versions of the file's content. For example, the attacker knows all the contents of the target file X except the sensitive information \texttheta. To learn the sensitive information, the attacker needs to upload m files with all possible values of \texttheta, respectively. If a file Xd with the value \textthetad is deduplicated and other files are not, the attacker knows that the information \texttheta = \textthetad. In the threat model of the LRI attack, we consider a general cloud storage service model that includes two entities, i.e., the user and cloud storage server. The attack is launched by the users who aim to steal the privacy information of other users [1]. The attacker can act as a user via its own account or use multiple accounts to disguise as multiple users. The cloud storage server communicates with the users through Internet. The connections from the clients to the cloud storage server are encrypted by SSL or TLS protocol. Hence, the attacker can monitor and measure the amount of network traffic between the client and server but cannot intercept and analyze the contents of the transmitted data due to the encryption. The attacker can then perform the sophisticated traffic analysis with sufficient computing resources. We propose a simple yet effective scheme, called randomized redundant chunk scheme (RRCS), to significantly mitigate the risk of the LRI attack while maintaining the high bandwidth efficiency of deduplication. The basic idea behind RRCS is to add randomized redundant chunks to mix up the real deduplication states of files used for the LRI attack, which effectively obfuscates the view of the attacker, who attempts to exploit the side channel of network traffic for the LRI attack. RRCS includes three key function modules, range generation (RG), secure bounds setting (SBS), and security-irrelevant redundancy elimination (SRE). When uploading the random-number redundant chunks, RRCS first uses RG to generate a fixed range [0,$łambda$N] ($łambda$ $ε$ (0,1]), in which the number of added redundant chunks is randomly chosen, where N is the total number of chunks in a file and $łambda$ is a system parameter. However, the fixed range may cause a security issue. SBS is used to deal with the bounds of the fixed range to avoid the security issue. There may exist security-irrelevant redundant chunks in RRCS. SRE reduces the security-irrelevant redundant chunks to improve the deduplication efficiency. The design details are presented in our technical report [5]. Our security analysis demonstrates RRCS can significantly reduce the risk of the LRI attack [5]. We examine the performance of RRCS using three real-world trace-based datasets, i.e., Fslhomes [2], MacOS [2], and Onefull [4], and compare RRCS with the randomized threshold scheme (RTS) [1]. Our experimental results show that source-based deduplication eliminates 100% data redundancy which however has no security guarantee. File-level (chunk-level) RTS only eliminates 8.1% – 16.8% (9.8% – 20.3%) redundancy, due to only eliminating the redundancy of the files (chunks) that have many copies. RRCS with $łambda$ = 0.5 eliminates 76.1% – 78.0% redundancy and RRCS with $łambda$ = 1 eliminates 47.9% – 53.6% redundancy.

Zuo, C., Shao, J., Liu, Z., Ling, Y., Wei, G..  2017.  Hidden-Token Searchable Public-Key Encryption. 2017 IEEE Trustcom/BigDataSE/ICESS. :248–254.

In this paper, we propose a variant of searchable public-key encryption named hidden-token searchable public-key encryption with two new security properties: token anonymity and one-token-per-trapdoor. With the former security notion, the client can obtain the search token from the data owner without revealing any information about the underlying keyword. Meanwhile, the client cannot derive more than one token from one trapdoor generated by the data owner according to the latter security notion. Furthermore, we present a concrete hiddentoken searchable public-key encryption scheme together with the security proofs in the random oracle model.

Zulkipli, Nurul Huda Nik, Wills, Gary B..  2017.  An Event-based Access Control for IoT. Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing. :121:1–121:4.

The Internet of Things (IoT) comes together with the connection between sensors and devices. These smart devices have been upgraded from a standalone device which can only handle a specific task at one time to an interactive device that can handle multiple tasks in time. However, this technology has been exposed to many vulnerabilities especially on the malicious attacks of the devices. With the IoT constraints and low-security mechanisms applied, the malicious attacks could exploit the sensor vulnerability to provide wrong data where it can lead to wrong interpretation and actuation to the users. Due to this problems, this short paper presents an event-based access control framework that considers integrity, privacy and the authenticity in the IoT devices.

Zulkarnine, A. T., Frank, R., Monk, B., Mitchell, J., Davies, G..  2016.  Surfacing collaborated networks in dark web to find illicit and criminal content. 2016 IEEE Conference on Intelligence and Security Informatics (ISI). :109–114.
The Tor Network, a hidden part of the Internet, is becoming an ideal hosting ground for illegal activities and services, including large drug markets, financial frauds, espionage, child sexual abuse. Researchers and law enforcement rely on manual investigations, which are both time-consuming and ultimately inefficient. The first part of this paper explores illicit and criminal content identified by prominent researchers in the dark web. We previously developed a web crawler that automatically searched websites on the internet based on pre-defined keywords and followed the hyperlinks in order to create a map of the network. This crawler has demonstrated previous success in locating and extracting data on child exploitation images, videos, keywords and linkages on the public internet. However, as Tor functions differently at the TCP level, and uses socket connections, further technical challenges are faced when crawling Tor. Some of the other inherent challenges for advanced Tor crawling include scalability, content selection tradeoffs, and social obligation. We discuss these challenges and the measures taken to meet them. Our modified web crawler for Tor, termed the “Dark Crawler” has been able to access Tor while simultaneously accessing the public internet. We present initial findings regarding what extremist and terrorist contents are present in Tor and how this content is connected to each other in a mapped network that facilitates dark web crimes. Our results so far indicate the most popular websites in the dark web are acting as catalysts for dark web expansion by providing necessary knowledgebase, support and services to build Tor hidden services and onion websites.
Žulj, S., Delija, D., Sirovatka, G..  2020.  Analysis of secure data deletion and recovery with common digital forensic tools and procedures. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :1607–1610.
This paper presents how students practical’s is developed and used for the important task forensic specialist have to do when using common digital forensic tools for data deletion and data recovery from various types of digital media and live systems. Digital forensic tools like EnCase, FTK imager, BlackLight, and open source tools are discussed in developed practical’s scenarios. This paper shows how these tools can be used to train and enhance student understanding of the capabilities and limitations of digital forensic tools in uncommon digital forensic scenarios. Students’ practicals encourage students to efficiently use digital forensic tools in the various professional scenarios that they will encounter.
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.

Zügner, Daniel, Akbarnejad, Amir, Günnemann, Stephan.  2018.  Adversarial Attacks on Neural Networks for Graph Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2847-2856.
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g. the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we introduce the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model.We generate adversarial perturbations targeting the node's features and the graph structure, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain we propose an efficient algorithm Nettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given.
Zubov, Ilya G., Lysenko, Nikolai V., Labkov, Gleb M..  2019.  Detection of the Information Hidden in Image by Convolutional Neural Networks. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :393–394.

This article shows the possibility of detection of the hidden information in images. This is the approach to steganalysis than the basic data about the image and the information about the hiding method of the information are unknown. The architecture of the convolutional neural network makes it possible to detect small changes in the image with high probability.

Zubaydi, H. D., Anbar, M., Wey, C. Y..  2017.  Review on Detection Techniques against DDoS Attacks on a Software-Defined Networking Controller. 2017 Palestinian International Conference on Information and Communication Technology (PICICT). :10–16.

The evolution of information and communication technologies has brought new challenges in managing the Internet. Software-Defined Networking (SDN) aims to provide easily configured and remotely controlled networks based on centralized control. Since SDN will be the next disruption in networking, SDN security has become a hot research topic because of its importance in communication systems. A centralized controller can become a focal point of attack, thus preventing attack in controller will be a priority. The whole network will be affected if attacker gain access to the controller. One of the attacks that affect SDN controller is DDoS attacks. This paper reviews different detection techniques that are available to prevent DDoS attacks, characteristics of these techniques and issues that may arise using these techniques.

Zubarev, Dmytro, Skarga-Bandurova, Inna.  2019.  Cross-Site Scripting for Graphic Data: Vulnerabilities and Prevention. 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT). :154–160.

In this paper, we present an overview of the problems associated with the cross-site scripting (XSS) in the graphical content of web applications. The brief analysis of vulnerabilities for graphical files and factors responsible for making SVG images vulnerable to XSS attacks are discussed. XML treatment methods and their practical testing are performed. As a result, the set of rules for protecting the graphic content of the websites and prevent XSS vulnerabilities are proposed.

Zouari, J., Hamdi, M., Kim, T. H..  2017.  A privacy-preserving homomorphic encryption scheme for the Internet of Things. 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). :1939–1944.

The Internet of Things is a disruptive paradigm based on the cooperation of a plethora of heterogeneous smart things to collect, transmit, and analyze data from the ambient environment. To this end, many monitored variables are combined by a data analysis module in order to implement efficient context-aware decision mechanisms. To ensure resource efficiency, aggregation is a long established solution, however it is applicable only in the case of one sensed variable. We extend the use of aggregation to the complex context of IoT by proposing a novel approach for secure cooperation of smart things while granting confidentiality and integrity. Traditional solutions for data concealment in resource constrained devices rely on hop-by-hop or end-to-end encryption, which are shown to be inefficient in our context. We use a more sophisticated scheme relying on homomorphic encryption which is not compromise resilient. We combine fully additive encryption with fully additive secret sharing to fulfill the required properties. Thorough security analysis and performance evaluation show a viable tradeoff between security and efficiency for our scheme.

Zou, Zhenwan, Hou, Yingsa, Yang, Huiting, Li, Mingxuan, Wang, Bin, Guo, Qingrui.  2019.  Research and Implementation of Intelligent Substation Information Security Risk Assessment Tool. 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). :1306–1310.

In order to improve the information security level of intelligent substation, this paper proposes an intelligent substation information security assessment tool through the research and analysis of intelligent substation information security risk and information security assessment method, and proves that the tool can effectively detect it. It is of great significance to carry out research on industrial control systems, especially intelligent substation information security.

Zou, Zhenwan, Chen, Jia, Hou, Yingsa, Song, Panpan, He, Ling, Yang, Huiting, Wang, Bin.  2019.  Design and Implementation of a New Intelligent Substation Network Security Defense System. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:2709–2713.
In order to enhance the network security protection level of intelligent substation, this paper puts forward a model of intelligent substation network security defense system through the analysis of intelligent substation network security risk and protection demand, and using example proved the feasibility and effectiveness of the defense system. It is intelligent substation network security protection provides a new solution.
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.

Zou, Yang, Zeng, Xiaoqin, Liu, Yufeng, Liu, Huiyi.  2017.  Partial Precedence of Context-sensitive Graph Grammars. Proceedings of the 10th International Symposium on Visual Information Communication and Interaction. :16–23.
Context-sensitive graph grammars have been rigorous formalisms for specifying visual programming languages, as they possess sufficient expressive powers and intuitive forms. Efficient parsing mechanisms are essential to these formalisms. However, the existent parsing algorithms are either inefficient or confined to a minority of graph grammars. This paper introduces the notion of partial precedence, defines the partial precedence graph of a graph grammar and theoretically unveils the existence of a valid parsing path conforming to the topological orderings of the partial precedence graph. Then, it provides algorithms for computing the partial precedence graph and presents an approach to improving general parsing algorithms with the graph based on the drawn conclusion. It is shown that the approach can considerably improve the efficiency of general parsing algorithms.
Zou, Shuai, Kuzushima, Kento, Mitake, Hironori, Hasegawa, Shoichi.  2017.  Conversational Agent Learning Natural Gaze and Motion of Multi-Party Conversation from Example. Proceedings of the 5th International Conference on Human Agent Interaction. :405–409.

Recent developments in robotics and virtual reality (VR) are making embodied agents familiar, and social behaviors of embodied conversational agents are essential to create mindful daily lives with conversational agents. Especially, natural nonverbal behaviors are required, such as gaze and gesture movement. We propose a novel method to create an agent with human-like gaze as a listener in multi-party conversation, using Hidden Markov Model (HMM) to learn the behavior from real conversation examples. The model can generate gaze reaction according to users' gaze and utterance. We implemented an agent with proposed method, and created VR environment to interact with the agent. The proposed agent reproduced several features of gaze behavior in example conversations. Impression survey result showed that there is at least a group who felt the proposed agent is similar to human and better than conventional methods.

Zou, Changwei, Xue, Jingling.  2020.  Burn After Reading: A Shadow Stack with Microsecond-level Runtime Rerandomization for Protecting Return Addresses**Thanks to all the reviewers for their valuable comments. This research is supported by an Australian Research Council grant (DP180104069).. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :258–270.
Return-oriented programming (ROP) is an effective code-reuse attack in which short code sequences (i.e., gadgets) ending in a ret instruction are found within existing binaries and then executed by taking control of the call stack. The shadow stack, control flow integrity (CFI) and code (re)randomization are three popular techniques for protecting programs against return address overwrites. However, existing runtime rerandomization techniques operate on concrete return addresses, requiring expensive pointer tracking. By adding one level of indirection, we introduce BarRA, the first shadow stack mechanism that applies continuous runtime rerandomization to abstract return addresses for protecting their corresponding concrete return addresses (protected also by CFI), thus avoiding expensive pointer tracking. As a nice side-effect, BarRA naturally combines the shadow stack, CFI and runtime rerandomization in the same framework. The key novelty of BarRA, however, is that once some abstract return addresses are leaked, BarRA will enforce the burn-after-reading property by rerandomizing the mapping from the abstract to the concrete return address space in the order of microseconds instead of seconds required for rerandomizing a concrete return address space. As a result, BarRA can be used as a superior replacement for the shadow stack, as demonstrated by comparing both using the 19 C/C++ benchmarks in SPEC CPU2006 (totalling 2,047,447 LOC) and analyzing a proof-of-concept attack, provided that we can tolerate some slight binary code size increases (by an average of 29.44%) and are willing to use 8MB of dedicated memory for holding up to 220 return addresses (on a 64-bit platform). Under an information leakage attack (for some return addresses), the shadow stack is always vulnerable but BarRA is significantly more resilient (by reducing an attacker's success rate to [1/(220)] on average). In terms of the average performance overhead introduced, both are comparable: 6.09% (BarRA) vs. 5.38% (the shadow stack).
Zonouz, S.A., Khurana, H., Sanders, W.H., Yardley, T.M..  2014.  RRE: A Game-Theoretic Intrusion Response and Recovery Engine. Parallel and Distributed Systems, IEEE Transactions on. 25:395-406.

Preserving the availability and integrity of networked computing systems in the face of fast-spreading intrusions requires advances not only in detection algorithms, but also in automated response techniques. In this paper, we propose a new approach to automated response called the response and recovery engine (RRE). Our engine employs a game-theoretic response strategy against adversaries modeled as opponents in a two-player Stackelberg stochastic game. The RRE applies attack-response trees (ART) to analyze undesired system-level security events within host computers and their countermeasures using Boolean logic to combine lower level attack consequences. In addition, the RRE accounts for uncertainties in intrusion detection alert notifications. The RRE then chooses optimal response actions by solving a partially observable competitive Markov decision process that is automatically derived from attack-response trees. To support network-level multiobjective response selection and consider possibly conflicting network security properties, we employ fuzzy logic theory to calculate the network-level security metric values, i.e., security levels of the system's current and potentially future states in each stage of the game. In particular, inputs to the network-level game-theoretic response selection engine, are first fed into the fuzzy system that is in charge of a nonlinear inference and quantitative ranking of the possible actions using its previously defined fuzzy rule set. Consequently, the optimal network-level response actions are chosen through a game-theoretic optimization process. Experimental results show that the RRE, using Snort's alerts, can protect large networks for which attack-response trees have more than 500 nodes.

Zonouz, S., Davis, C.M., Davis, K.R., Berthier, R., Bobba, R.B., Sanders, W.H..  2014.  SOCCA: A Security-Oriented Cyber-Physical Contingency Analysis in Power Infrastructures. Smart Grid, IEEE Transactions on. 5:3-13.

Contingency analysis is a critical activity in the context of the power infrastructure because it provides a guide for resiliency and enables the grid to continue operating even in the case of failure. In this paper, we augment this concept by introducing SOCCA, a cyber-physical security evaluation technique to plan not only for accidental contingencies but also for malicious compromises. SOCCA presents a new unified formalism to model the cyber-physical system including interconnections among cyber and physical components. The cyber-physical contingency ranking technique employed by SOCCA assesses the potential impacts of events. Contingencies are ranked according to their impact as well as attack complexity. The results are valuable in both cyber and physical domains. From a physical perspective, SOCCA scores power system contingencies based on cyber network configuration, whereas from a cyber perspective, control network vulnerabilities are ranked according to the underlying power system topology.

Zonouz, S., Davis, C.M., Davis, K.R., Berthier, R., Bobba, R.B., Sanders, W.H..  2014.  SOCCA: A Security-Oriented Cyber-Physical Contingency Analysis in Power Infrastructures. Smart Grid, IEEE Transactions on. 5:3-13.

Contingency analysis is a critical activity in the context of the power infrastructure because it provides a guide for resiliency and enables the grid to continue operating even in the case of failure. In this paper, we augment this concept by introducing SOCCA, a cyber-physical security evaluation technique to plan not only for accidental contingencies but also for malicious compromises. SOCCA presents a new unified formalism to model the cyber-physical system including interconnections among cyber and physical components. The cyber-physical contingency ranking technique employed by SOCCA assesses the potential impacts of events. Contingencies are ranked according to their impact as well as attack complexity. The results are valuable in both cyber and physical domains. From a physical perspective, SOCCA scores power system contingencies based on cyber network configuration, whereas from a cyber perspective, control network vulnerabilities are ranked according to the underlying power system topology.

Zong, Zhaorong, Hong, Changchun.  2018.  On Application of Natural Language Processing in Machine Translation. 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :506–510.
Natural language processing is the core of machine translation. In the history, its development process is almost the same as machine translation, and the two complement each other. This article compares the natural language processing of statistical corpora with neural machine translation and concludes the natural language processing: Neural machine translation has the advantage of deep learning, which is very suitable for dealing with the high dimension, label-free and big data of natural language, therefore, its application is more general and reflects the power of big data and big data thinking.