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Su, C., Santoso, B., Li, Y., Deng, R. H., Huang, X..  2017.  Universally Composable RFID Mutual Authentication. IEEE Transactions on Dependable and Secure Computing. 14:83–94.

Universally Composable (UC) framework provides the strongest security notion for designing fully trusted cryptographic protocols, and it is very challenging on applying UC security in the design of RFID mutual authentication protocols. In this paper, we formulate the necessary conditions for achieving UC secure RFID mutual authentication protocols which can be fully trusted in arbitrary environment, and indicate the inadequacy of some existing schemes under the UC framework. We define the ideal functionality for RFID mutual authentication and propose the first UC secure RFID mutual authentication protocol based on public key encryption and certain trusted third parties which can be modeled as functionalities. We prove the security of our protocol under the strongest adversary model assuming both the tags' and readers' corruptions. We also present two (public) key update protocols for the cases of multiple readers: one uses Message Authentication Code (MAC) and the other uses trusted certificates in Public Key Infrastructure (PKI). Furthermore, we address the relations between our UC framework and the zero-knowledge privacy model proposed by Deng et al. [1].

Hogan, Kyle, Maleki, Hoda, Rahaeimehr, Reza, Canetti, Ran, van Dijk, Marten, Hennessey, Jason, Varia, Mayank, Zhang, Haibin.  2019.  On the Universally Composable Security of OpenStack. 2019 IEEE Cybersecurity Development (SecDev). :20–33.
We initiate an effort to provide a rigorous, holistic and modular security analysis of OpenStack. OpenStack is the prevalent open-source, non-proprietary package for managing cloud services and data centers. It is highly complex and consists of multiple inter-related components which are developed by separate, loosely coordinated groups. All of these properties make the security analysis of OpenStack both a worthy mission and a challenging one. We base our modeling and security analysis in the universally composable (UC) security framework. This allows specifying and proving security in a modular way – a crucial feature when analyzing systems of such magnitude. Our analysis has the following key features: 1) It is user-centric: It stresses the security guarantees given to users of the system in terms of privacy, correctness, and timeliness of the services. 2) It considers the security of OpenStack even when some of the components are compromised. This departs from the traditional design approach of OpenStack, which assumes that all services are fully trusted. 3) It is modular: It formulates security properties for individual components and uses them to prove security properties of the overall system. Specifically, this work concentrates on the high-level structure of OpenStack, leaving the further formalization and more detailed analysis of specific OpenStack services to future work. Specifically, we formulate ideal functionalities that correspond to some of the core OpenStack modules, and then proves security of the overall OpenStack protocol given the ideal components. As demonstrated within, the main challenge in the high-level design is to provide adequately fine-grained scoping of permissions to access dynamically changing system resources. We demonstrate security issues with current mechanisms in case of failure of some components, propose alternative mechanisms, and rigorously prove adequacy of then new mechanisms within our modeling.
Canetti, R., Hogan, K., Malhotra, A., Varia, M..  2017.  A Universally Composable Treatment of Network Time. 2017 IEEE 30th Computer Security Foundations Symposium (CSF). :360–375.
The security of almost any real-world distributed system today depends on the participants having some "reasonably accurate" sense of current real time. Indeed, to name one example, the very authenticity of practically any communication on the Internet today hinges on the ability of the parties to accurately detect revocation of certificates, or expiration of passwords or shared keys.,,However, as recent attacks show, the standard protocols for determining time are subvertible, resulting in wide-spread security loss. Worse yet, we do not have security notions for network time protocols that (a) can be rigorously asserted, and (b) rigorously guarantee security of applications that require a sense of real time.,,We propose such notions, within the universally composable (UC) security framework. That is, we formulate ideal functionalities that capture a number of prevalent forms of time measurement within existing systems. We show how they can be realized by real-world protocols, and how they can be used to assert security of time-reliant applications - specifically, certificates with revocation and expiration times. This allows for relatively clear and modular treatment of the use of time consensus in security-sensitive systems.,,Our modeling and analysis are done within the existing UC framework, in spite of its asynchronous, event-driven nature. This allows incorporating the use of real time within the existing body of analytical work done in this framework. In particular it allows for rigorous incorporation of real time within cryptographic tools and primitives.
Duque, Alexis, Stanica, Razvan, Rivano, Herve, Desportes, Adrien.  2016.  Unleashing the Power of LED-to-camera Communications for IoT Devices. Proceedings of the 3rd Workshop on Visible Light Communication Systems.
Isaakidis, Marios, Halpin, Harry, Danezis, George.  2016.  UnlimitID: Privacy-Preserving Federated Identity Management Using Algebraic MACs. Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society. :139–142.

UnlimitID is a method for enhancing the privacy of commodity OAuth and applications such as OpenID Connect, using anonymous attribute-based credentials based on algebraic Message Authentication Codes (aMACs). OAuth is one of the most widely used protocols on the Web, but it exposes each of the requests of a user for data by each relying party (RP) to the identity provider (IdP). Our approach allows for the creation of multiple persistent and unlinkable pseudo-identities and requires no change in the deployed code of relying parties, only in identity providers and the client.

Coulter, Rory, Zhang, Jun, Pan, Lei, Xiang, Yang.  2020.  Unmasking Windows Advanced Persistent Threat Execution. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :268—276.

The advanced persistent threat (APT) landscape has been studied without quantifiable data, for which indicators of compromise (IoC) may be uniformly analyzed, replicated, or used to support security mechanisms. This work culminates extensive academic and industry APT analysis, not as an incremental step in existing approaches to APT detection, but as a new benchmark of APT related opportunity. We collect 15,259 APT IoC hashes, retrieving subsequent sandbox execution logs across 41 different file types. This work forms an initial focus on Windows-based threat detection. We present a novel Windows APT executable (APT-EXE) dataset, made available to the research community. Manual and statistical analysis of the APT-EXE dataset is conducted, along with supporting feature analysis. We draw upon repeat and common APT paths access, file types, and operations within the APT-EXE dataset to generalize APT execution footprints. A baseline case analysis successfully identifies a majority of 117 of 152 live APT samples from campaigns across 2018 and 2019.

Suzuki, Ippei, Ochiai, Yoichi.  2017.  Unphotogenic Light: High-Speed Projection Method to Prevent Secret Photography by Small Cameras. ACM SIGGRAPH 2017 Posters. :65:1–65:2.
We present a new method to protect projected content from secret photography using high-speed projection. Protection techniques for digital copies have been discussed over many years from the viewpoint of data protection. However, content displayed by general display techniques is not only visible to the human eye but also can be captured by cameras. Therefore, projected content is, at times, secretly taken by malicious small cameras even when protection techniques for digital copies are adopted. In this study, we aim to realize a protectable projection method that allows people to observe content with their eyes but not record content with camera devices.
Chen, Lin, Xu, Lei, Shah, Nolan, Diallo, Nour, Gao, Zhimin, Lu, Yang, Shi, Weidong.  2017.  Unraveling Blockchain Based Crypto-Currency System Supporting Oblivious Transactions: A Formalized Approach. Proceedings of the ACM Workshop on Blockchain, Cryptocurrencies and Contracts. :23–28.

User privacy is an important issue in a blockchain based transaction system. Bitcoin, being one of the most widely used blockchain based transaction system, fails to provide enough protection on users' privacy. Many subsequent studies focus on establishing a system that hides the linkage between the identities (pseudonyms) of users and the transactions they carry out in order to provide a high level of anonymity. Examples include Zerocoin, Zerocash and so on. It thus becomes an interesting question whether such new transaction systems do provide enough protection on users' privacy. In this paper, we propose a novel and effective approach for de-anonymizing these transaction systems by leveraging information in the system that is not directly related, including the number of transactions made by each identity and time stamp of sending and receiving. Combining probability studies with optimization tools, we establish a model which allows us to determine, among all possible ways of linking between transactions and identities, the one that is most likely to be true. Subsequent transaction graph analysis could then be carried out, leading to the de-anonymization of the system. To solve the model, we provide exact algorithms based on mixed integer linear programming. Our research also establishes interesting relationships between the de-anonymization problem and other problems studied in the literature of theoretical computer science, e.g., the graph matching problem and scheduling problem.

Harrigan, M., Fretter, C..  2016.  The Unreasonable Effectiveness of Address Clustering. 2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld). :368–373.

Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe the primary reasons behind this effectiveness: address reuse, avoidable merging, super-clusters with high centrality,, the incremental growth of address clusters. We quantify their impact during Bitcoin's first seven years of existence.

Marques, J., Andrade, J., Falcao, G..  2017.  Unreliable memory operation on a convolutional neural network processor. 2017 IEEE International Workshop on Signal Processing Systems (SiPS). :1–6.

The evolution of convolutional neural networks (CNNs) into more complex forms of organization, with additional layers, larger convolutions and increasing connections, established the state-of-the-art in terms of accuracy errors for detection and classification challenges in images. Moreover, as they evolved to a point where Gigabytes of memory are required for their operation, we have reached a stage where it becomes fundamental to understand how their inference capabilities can be impaired if data elements somehow become corrupted in memory. This paper introduces fault-injection in these systems by simulating failing bit-cells in hardware memories brought on by relaxing the 100% reliable operation assumption. We analyze the behavior of these networks calculating inference under severe fault-injection rates and apply fault mitigation strategies to improve on the CNNs resilience. For the MNIST dataset, we show that 8x less memory is required for the feature maps memory space, and that in sub-100% reliable operation, fault-injection rates up to 10-1 (with most significant bit protection) can withstand only a 1% error probability degradation. Furthermore, considering the offload of the feature maps memory to an embedded dynamic RAM (eDRAM) system, using technology nodes from 65 down to 28 nm, up to 73 80% improved power efficiency can be obtained.

Villalobos, J. J., Rodero, Ivan, Parashar, Manish.  2017.  An Unsupervised Approach for Online Detection and Mitigation of High-Rate DDoS Attacks Based on an In-Memory Distributed Graph Using Streaming Data and Analytics. Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. :103–112.

A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack.

Lu, Yen-Cheng, Wu, Chih-Wei, Lu, Chang-Tien, Lerch, Alexander.  2016.  An Unsupervised Approach to Anomaly Detection in Music Datasets. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. :749–752.

This paper presents an unsupervised method for systematically identifying anomalies in music datasets. The model integrates categorical regression and robust estimation techniques to infer anomalous scores in music clips. When applied to a music genre recognition dataset, the new method is able to detect corrupted, distorted, or mislabeled audio samples based on commonly used features in music information retrieval. The evaluation results show that the algorithm outperforms other anomaly detection methods and is capable of finding problematic samples identified by human experts. The proposed method introduces a preliminary framework for anomaly detection in music data that can serve as a useful tool to improve data integrity in the future.

Wang, Gang, Zhang, Xinyi, Tang, Shiliang, Zheng, Haitao, Zhao, Ben Y..  2016.  Unsupervised Clickstream Clustering for User Behavior Analysis. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. :225–236.

Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool.

Shamsi, Zain, Loguinov, Dmitri.  2016.  Unsupervised Clustering Under Temporal Feature Volatility in Network Stack Fingerprinting. Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science. :127–138.

Maintaining and updating signature databases is a tedious task that normally requires a large amount of user effort. The problem becomes harder when features can be distorted by observation noise, which we call volatility. To address this issue, we propose algorithms and models to automatically generate signatures in the presence of noise, with a focus on stack fingerprinting, which is a research area that aims to discover the operating system (OS) of remote hosts using TCP/IP packets. Armed with this framework, we construct a database with 420 network stacks, label the signatures, develop a robust classifier for this database, and fingerprint 66M visible webservers on the Internet.

Drakopoulos, G., Giotopoulos, K., Giannoukou, I., Sioutas, S..  2020.  Unsupervised Discovery Of Semantically Aware Communities With Tensor Kruskal Decomposition: A Case Study In Twitter. 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA. :1–8.
Substantial empirical evidence, including the success of synthetic graph generation models as well as of analytical methodologies, suggests that large, real graphs have a recursive community structure. The latter results, in part at least, in other important properties of these graphs such as low diameter, high clustering coefficient values, heavy degree distribution tail, and clustered graph spectrum. Notice that this structure need not be official or moderated like Facebook groups, but it can also take an ad hoc and unofficial form depending on the functionality of the social network under study as for instance the follow relationship on Twitter or the connections between news aggregators on Reddit. Community discovery is paramount in numerous applications such as political campaigns, digital marketing, crowdfunding, and fact checking. Here a tensor representation for Twitter subgraphs is proposed which takes into consideration both the followfollower relationships but also the coherency in hashtags. Community structure discovery then reduces to the computation of Tucker tensor decomposition, a higher order counterpart of the well-known unsupervised learning method of singular value decomposition (SVD). Tucker decomposition clearly outperforms the SVD in terms of finding a more compact community size distribution in experiments done in Julia on a Twitter subgraph. This can be attributed to the facts that the proposed methodology combines both structural and functional Twitter elements and that hashtags carry an increased semantic weight in comparison to ordinary tweets.
Atul Bohara, University of Illinois at Urbana-Champaign, Mohammad A. Noureddine, University of Illinois at Urbana-Champaign, Ahmed Fawaz, University of Illinois at Urbana-Champaign, William Sanders, University of Illinois at Urbana-Champaign.  2017.  An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement. IEEE 36th Symposium on Reliable Distributed Systems (SRDS).

Abstract—Lateral movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and lateral movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based utlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.

Schroeder, Jan, Berger, Christian, Staron, Miroslaw, Herpel, Thomas, Knauss, Alessia.  2016.  Unveiling Anomalies and Their Impact on Software Quality in Model-based Automotive Software Revisions with Software Metrics and Domain Experts. Proceedings of the 25th International Symposium on Software Testing and Analysis. :154–164.

The validation of simulation models (e.g., of electronic control units for vehicles) in industry is becoming increasingly challenging due to their growing complexity. To systematically assess the quality of such models, software metrics seem to be promising. In this paper we explore the use of software metrics and outlier analysis as a means to assess the quality of model-based software. More specifically, we investigate how results from regression analysis applied to measurement data received from size and complexity metrics can be mapped to software quality. Using the moving averages approach, models were fit to data received from over 65,000 software revisions for 71 simulation models that represent different electronic control units of real premium vehicles. Consecutive investigations using studentized deleted residuals and Cook’s Distance revealed outliers among the measurements. From these outliers we identified a subset, which provides meaningful information (anomalies) by comparing outlier scores with expert opinions. Eight engineers were interviewed separately for outlier impact on software quality. Findings were validated in consecutive workshops. The results show correlations between outliers and their impact on four of the considered quality characteristics. They also demonstrate the applicability of this approach in industry.

Agirre, I., Onaindia, P., Poggi, T., Yarza, I., Cazorla, F. J., Kosmidis, L., Grüttner, K., Abuteir, M., Loewe, J., Orbegozo, J. M. et al..  2020.  UP2DATE: Safe and secure over-the-air software updates on high-performance mixed-criticality systems. 2020 23rd Euromicro Conference on Digital System Design (DSD). :344–351.
Following the same trend of consumer electronics, safety-critical industries are starting to adopt Over-The-Air Software Updates (OTASU) on their embedded systems. The motivation behind this trend is twofold. On the one hand, OTASU offer several benefits to the product makers and users by improving or adding new functionality and services to the product without a complete redesign. On the other hand, the increasing connectivity trend makes OTASU a crucial cyber-security demand to download latest security patches. However, the application of OTASU in the safety-critical domain is not free of challenges, specially when considering the dramatic increase of software complexity and the resulting high computing performance demands. This is the mission of UP2DATE, a recently launched project funded within the European H2020 programme focused on new software update architectures for heterogeneous high-performance mixed-criticality systems. This paper gives an overview of UP2DATE and its foundations, which seeks to improve existing OTASU solutions by considering safety, security and availability from the ground up in an architecture that builds around composability and modularity.
Zhao, Yongjun, Chow, Sherman S.M..  2017.  Updatable Block-Level Message-Locked Encryption. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :449–460.
Deduplication is a widely used technique for reducing storage space of cloud service providers. Yet, it is unclear how to support deduplication of encrypted data securely until the study of Bellareetal on message-locked encryption (Eurocrypt 2013). Since then, there are many improvements such as strengthening its security, reducing client storage, etc. While updating a (shared) file is common, there is little attention on how to efficiently update large encrypted files in a remote storage with deduplication. To modify even a single bit, existing solutions require the trivial and expensive way of downloading and decrypting the large ciphertext. We initiate the study of updatable block-level message-locked encryption. We propose a provably secure construction that is efficiently updatable with O(logtextbarFtextbar) computational cost, where textbarFtextbar is the file size. It also supports proof-of-ownership, a nice feature which protects storage providers from being abused as a free content distribution network.
Jarecki, Stanislaw, Krawczyk, Hugo, Resch, Jason.  2019.  Updatable Oblivious Key Management for Storage Systems. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. :379–393.

We introduce Oblivious Key Management Systems (KMS) as a much more secure alternative to traditional wrapping-based KMS that form the backbone of key management in large-scale data storage deployments. The new system, that builds on Oblivious Pseudorandom Functions (OPRF), hides keys and object identifiers from the KMS, offers unconditional security for key transport, provides key verifiability, reduces storage, and more. Further, we show how to provide all these features in a distributed threshold implementation that enhances protection against server compromise. We extend this system with updatable encryption capability that supports key updates (known as key rotation) so that upon the periodic change of OPRF keys by the KMS server, a very efficient update procedure allows a client of the KMS service to non-interactively update all its encrypted data to be decryptable only by the new key. This enhances security with forward and post-compromise security, namely, security against future and past compromises, respectively, of the client's OPRF keys held by the KMS. Additionally, and in contrast to traditional KMS, our solution supports public key encryption and dispenses with any interaction with the KMS for data encryption (only decryption by the client requires such communication). Our solutions build on recent work on updatable encryption but with significant enhancements applicable to the remote KMS setting. In addition to the critical security improvements, our designs are highly efficient and ready for use in practice. We report on experimental implementation and performance.

Yang, Bo, He, Suining, Chan, S.-H. Gary.  2016.  Updating Wireless Signal Map with Bayesian Compressive Sensing. Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. :310–317.

In a wireless system, a signal map shows the signal strength at different locations termed reference points (RPs). As access points (APs) and their transmission power may change over time, keeping an updated signal map is important for applications such as Wi-Fi optimization and indoor localization. Traditionally, the signal map is obtained by a full site survey, which is time-consuming and costly. We address in this paper how to efficiently update a signal map given sparse samples randomly crowdsourced in the space (e.g., by signal monitors, explicit human input, or implicit user participation). We propose Compressive Signal Reconstruction (CSR), a novel learning system employing Bayesian compressive sensing (BCS) for online signal map update. CSR does not rely on any path loss model or line of sight, and is generic enough to serve as a plug-in of any wireless system. Besides signal map update, CSR also computes the estimation error of signals in terms of confidence interval. CSR models the signal correlation with a kernel function. Using it, CSR constructs a sensing matrix based on the newly sampled signals. The sensing matrix is then used to compute the signal change at all the RPs with any BCS algorithm. We have conducted extensive experiments on CSR in our university campus. Our results show that CSR outperforms other state-of-the-art algorithms by a wide margin (reducing signal error by about 30% and sampling points by 20%).

Naik, N., Jenkins, P..  2020.  uPort Open-Source Identity Management System: An Assessment of Self-Sovereign Identity and User-Centric Data Platform Built on Blockchain. 2020 IEEE International Symposium on Systems Engineering (ISSE). :1—7.

Managing identity across an ever-growing digital services landscape has become one of the most challenging tasks for security experts. Over the years, several Identity Management (IDM) systems were introduced and adopted to tackle with the growing demand of an identity. In this series, a recently emerging IDM system is Self-Sovereign Identity (SSI) which offers greater control and access to users regarding their identity. This distinctive feature of the SSI IDM system represents a major development towards the availability of sovereign identity to users. uPort is an emerging open-source identity management system providing sovereign identity to users, organisations, and other entities. As an emerging identity management system, it requires meticulous analysis of its architecture, working, operational services, efficiency, advantages and limitations. Therefore, this paper contributes towards achieving all of these objectives. Firstly, it presents the architecture and working of the uPort identity management system. Secondly, it develops a Decentralized Application (DApp) to demonstrate and evaluate its operational services and efficiency. Finally, based on the developed DApp and experimental analysis, it presents the advantages and limitations of the uPort identity management system.

Honghui Dong, Xiaoqing Ding, Mingchao Wu, Yan Shi, Limin Jia, Yong Qin, Lianyu Chu.  2014.  Urban traffic commuting analysis based on mobile phone data. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on. :611-616.

With the urban traffic planning and management development, it is a highly considerable issue to analyze and estimate the original-destination data in the city. Traditional method to acquire the OD information usually uses household survey, which is inefficient and expensive. In this paper, the new methodology proposed that using mobile phone data to analyze the mechanism of trip generation, trip attraction and the OD information. The mobile phone data acquisition is introduced. A pilot study is implemented on Beijing by using the new method. And, much important traffic information can be extracted from the mobile phone data. We use the K-means clustering algorithm to divide the traffic zone. The attribution of traffic zone is identified using the mobile phone data. Then the OD distribution and the commuting travel are analyzed. At last, an experiment is done to verify availability of the mobile phone data, that analyzing the "Traffic tide phenomenon" in Beijing. The results of the experiments in this paper show a great correspondence to the actual situation. The validated results reveal the mobile phone data has tremendous potential on OD analysis.
 

Wang, Kai, Zhao, Yude, liu, Shugang, Tong, Xiangrong.  2018.  On the urgency of implementing Interest NACK into CCN: from the perspective of countering advanced interest flooding attacks. IET Networks. 7:136–140.
Content centric networking (CCN) where content/named data as the first entity has become one of the most promising architectures for the future Internet. To achieve better security, the Interest NACK mechanism is introduced into CCN; however, it has not attracted enough attention and most of the CCN architectures do not embed Interest NACK until now. This study focuses on analysing the urgency of implementing Interest NACK into CCN, by designing a novel network threat named advanced interest flooding attack (AIFA) to attack CCN, which can not only exhaust the pending interest table (PIT) resource of each involved router just as normal interest flooding attack (IFA), but also keep each PIT entry unexpired until it finishes, making it harder to detect and more harmful when compared with the normal IFA. Specifically, the damage of AIFA on CCN architecture with and without Interest NACK is evaluated and analysed, compared with normal IFA, and then the urgency of implementing Interest NACK is highlighted.
Ivanov, A. V., Sklyarov, V. A..  2018.  The Urgency of the Threats of Attacks on Interfaces and Field-Layer Protocols in Industrial Control Systems. 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). :162-165.

The paper is devoted to analysis of condition of executing devices and sensors of Industrial Control Systems information security. The work contains structures of industrial control systems divided into groups depending on system's layer. The article contains the analysis of analog interfaces work and work features of data transmission protocols in industrial control system field layer. Questions about relevance of industrial control systems information security, both from the point of view of the information security occurring incidents, and from the point of view of regulators' reaction in the form of normative legal acts, are described. During the analysis of the information security systems of industrial control systems a possibility of leakage through technical channels of information leakage at the field layer was found. Potential vectors of the attacks on devices of field layer and data transmission network of an industrial control system are outlined in the article. The relevance analysis of the threats connected with the attacks at the field layer of an industrial control system is carried out, feature of this layer and attractiveness of this kind of attacks is observed.