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Xiong, Leilei, Grijalva, Santiago.  2019.  N-1 RTU Cyber-Physical Security Assessment Using State Estimation. 2019 IEEE Power Energy Society General Meeting (PESGM). :1–5.
Real-time supervisory control and data acquisition (SCADA) systems use remote terminal units (RTUs) to monitor and manage the flow of power at electrical substations. As their connectivity to different utility and private networks increases, RTUs are becoming more vulnerable to cyber-attacks. Some attacks seek to access RTUs to directly control power system devices with the intent to shed load or cause equipment damage. Other attacks (such as denial-of-service) target network availability and seek to block, delay, or corrupt communications between the RTU and the control center. In the most severe case, when communications are entirely blocked, the loss of an RTU can cause the power system to become unobservable. It is important to understand how losing an RTU impacts the system state (bus voltage magnitudes and angles). The system state is determined by the state estimator and serves as the input to other critical EMS applications. There is currently no systematic approach for assessing the cyber-physical impact of losing RTUs. This paper proposes a methodology for N-1 RTU cyber-physical security assessment that could benefit power system control and operation. We demonstrate our approach on the IEEE 14-bus system as well as on a synthetic 200-bus system.
Zhang, Zhiyi, Yu, Yingdi, Afanasyev, Alexander, Burke, Jeff, Zhang, Lixia.  2017.  NAC: Name-based Access Control in Named Data Networking. Proceedings of the 4th ACM Conference on Information-Centric Networking. :186–187.

As a proposed Internet architecture, Named Data Networking must provide effective security support: data authenticity, confidentiality, and availability. This poster focuses on supporting data confidentiality via encryption. The main challenge is to provide an easy-to-use key management mechanism that ensures only authorized parties are given the access to protected data. We describe the design of name-based access control (NAC) which provides automated key management by developing systematic naming conventions for both data and cryptographic keys. We also discuss an enhanced version of NAC that leverages attribute-based encryption mechanisms (NAC-ABE) to improve the flexibility of data access control and reduce communication, storage, and processing overheads.

Lim, H., Ni, A., Kim, D., Ko, Y. B..  2017.  Named data networking testbed for scientific data. 2017 2nd International Conference on Computer and Communication Systems (ICCCS). :65–69.

Named Data Networking (NDN) is one of the future internet architectures, which is a clean-slate approach. NDN provides intelligent data retrieval using the principles of name-based symmetrical forwarding of Interest/Data packets and innetwork caching. The continually increasing demand for rapid dissemination of large-scale scientific data is driving the use of NDN in data-intensive science experiments. In this paper, we establish an intercontinental NDN testbed. In the testbed, an NDN-based application that targets climate science as an example data intensive science application is designed and implemented, which has differentiated features compared to those of previous studies. We verify experimental justification of using NDN for climate science in the intercontinental network, through performance comparisons between classical delivery techniques and NDN-based climate data delivery.

Karatas, Nihan, Yoshikawa, Soshi, Okada, Michio.  2016.  NAMIDA: Sociable Driving Agents with Multiparty Conversation. Proceedings of the Fourth International Conference on Human Agent Interaction. :35–42.

We propose a multi party conversational social interface NAMIDA through a pilot study. The system consists of three robots that can converse with each other about environment throughout the road. Through this model, the directed utterances towards the driver diminishes by utilizing turn-taking process between the agents, and the mental workload of the driver can be reduced compared to the conventional one-to-one communication based approach that directly addresses the driver. We set up an experiment to compare the both approaches to explore their effects on the workload and attention behaviors of drivers. The results indicated that the multi-party conversational approach has a better effect on reducing certain workload factors. Also, the analysis of attention behaviors of drivers revealed that our method can better promote the drivers to focus on the road.

Elliott, Sean.  2019.  Nash Equilibrium of Multiple, Non-Uniform Bitcoin Block Withholding Attackers. 2019 2nd International Conference on Data Intelligence and Security (ICDIS). :144—151.
This research analyzes a seemingly malicious behavior known as a block withholding (BWH) attack between pools of cryptocurrency miners in Bitcoin-like systems featuring blockchain distributed databases. This work updates and builds on a seminal paper, The Miner's Dilemma, which studied a simplified scenario and showed that a BWH attack can be rational behavior that is profitable for the attacker. The new research presented here provides an in-depth profit analysis of a more complex and realistic BWH attack scenario, which includes mutual attacks between multiple, non-uniform Bitcoin mining pools. As a result of mathematical analysis and MATLAB modeling, this paper illustrates the Nash equilibrium conditions of a system of independent mining pools with varied mining rates and computes the equilibrium rates of mutual BWH attack. The analysis method quantifies the additional profit the largest pools extract from the system at the expense of the smaller pools. The results indicate that while the presence of BWH is a net negative for smaller pools, they must participate in BWH to maximize their remaining profits, and the results quantify the attack rates the smaller pools must maintain. Also, the smallest pools maximize profit by not attacking at all-that is, retaliation is not a rational move for them.
Ferguson, B., Tall, A., Olsen, D..  2014.  National Cyber Range Overview. Military Communications Conference (MILCOM), 2014 IEEE. :123-128.

The National Cyber Range (NCR) is an innovative Department of Defense (DoD) resource originally established by the Defense Advanced Research Projects Agency (DARPA) and now under the purview of the Test Resource Management Center (TRMC). It provides a unique environment for cyber security testing throughout the program development life cycle using unique methods to assess resiliency to advanced cyberspace security threats. This paper describes what a cyber security range is, how it might be employed, and the advantages a program manager (PM) can gain in applying the results of range events. Creating realism in a test environment isolated from the operational environment is a special challenge in cyberspace. Representing the scale and diversity of the complex DoD communications networks at a fidelity detailed enough to realistically portray current and anticipated attack strategies (e.g., Malware, distributed denial of service attacks, cross-site scripting) is complex. The NCR addresses this challenge by representing an Internet-like environment by employing a multitude of virtual machines and physical hardware augmented with traffic emulation, port/protocol/service vulnerability scanning, and data capture tools. Coupled with a structured test methodology, the PM can efficiently and effectively engage with the Range to gain cyberspace resiliency insights. The NCR capability, when applied, allows the DoD to incorporate cyber security early to avoid high cost integration at the end of the development life cycle. This paper provides an overview of the resources of the NCR which may be especially helpful for DoD PMs to find the best approach for testing the cyberspace resiliency of their systems under development.

Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. July/August

Bot detection - identifying a software program that's using a computer system -- is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. 20(4):69–73.

Bot detection - identifying a software program that's using a computer system – is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Robert St. Amant, David Roberts.  2016.  Natural interaction for bot detection. IEEE Internet Computing. 20:69–73.
Buck, Joshua W., Perugini, Saverio, Nguyen, Tam V..  2018.  Natural Language, Mixed-initiative Personal Assistant Agents. Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication. :82:1–82:8.
The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an activity, and is often exhibited in human-human conversations. In this paper, we study user support for mixed-initiative interaction with dialog-based systems through natural language using a bag-of-words model and k-nearest-neighbor classifier. We study this problem in the context of a toolkit we developed for automated, mixed-initiative dialog system construction, involving a dialog authoring notation and management engine based on lambda calculus, for specifying and implementing task-based, mixed-initiative dialogs. We use ordering at Subway through natural language, human-computer dialogs as a case study. Our results demonstrate that the dialogs authored with our toolkit support the end user's completion of a natural language, human-computer dialog in a mixed-initiative fashion. The use of natural language in the resulting mixed-initiative dialogs afford the user the ability to experience multiple self-directed paths through the dialog and makes the flexibility in communicating user utterances commensurate with that in dialog completion paths—an aspect missing from commercial assistants like Siri.
Kadebu, Prudence, Thada, Vikas, Chiurunge, Panashe.  2018.  Natural Language Processing and Deep Learning Towards Security Requirements Classification. 2018 3rd International Conference on Contemporary Computing and Informatics (IC3I). :135–140.
Security Requirements classification is an important area to the Software Engineering community in order to build software that is secure, robust and able to withstand attacks. This classification facilitates proper analysis of security requirements so that adequate security mechanisms are incorporated in the development process. Machine Learning techniques have been used in Security Requirements classification to aid in the process that lead to ensuring that correct security mechanisms are designed corresponding to the Security Requirements classifications made to eliminate the risk of security being incorporated in the late stages of development. However, these Machine Learning techniques have been found to have problems including, handcrafting of features, overfitting and failure to perform well with high dimensional data. In this paper we explore Natural Language Processing and Deep Learning to determine if this can be applied to Security Requirements classification.
Nambiar, Sindhya K, Leons, Antony, Jose, Soniya, Arunsree.  2019.  Natural Language Processing Based Part of Speech Tagger using Hidden Markov Model. 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :782–785.
In various natural language processing applications, PART-OF-SPEECH (POS) tagging is performed as a preprocessing step. For making POS tagging accurate, various techniques have been explored. But in Indian languages, not much work has been done. This paper describes the methods to build a Part of speech tagger by using hidden markov model. Supervised learning approach is implemented in which, already tagged sentences in malayalam is used to build hidden markov model.
Chai, Yadeng, Liu, Yong.  2019.  Natural Spoken Instructions Understanding for Robot with Dependency Parsing. 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). :866–871.
This paper presents a method based on syntactic information, which can be used for intent determination and slot filling tasks in a spoken language understanding system including the spoken instructions understanding module for robot. Some studies in recent years attempt to solve the problem of spoken language understanding via syntactic information. This research is a further extension of these approaches which is based on dependency parsing. In this model, the input for neural network are vectors generated by a dependency parsing tree, which we called window vector. This vector contains dependency features that improves performance of the syntactic-based model. The model has been evaluated on the benchmark ATIS task, and the results show that it outperforms many other syntactic-based approaches, especially in terms of slot filling, it has a performance level on par with some state of the art deep learning algorithms in recent years. Also, the model has been evaluated on FBM3, a dataset of the RoCKIn@Home competition. The overall rate of correctly understanding the instructions for robot is quite good but still not acceptable in practical use, which is caused by the small scale of FBM3.
Peeters, Roel, Hermans, Jens, Maene, Pieter, Grenman, Katri, Halunen, Kimmo, Häikiö, Juha.  2017.  n-Auth: Mobile Authentication Done Right. Proceedings of the 33rd Annual Computer Security Applications Conference. :1–15.
Weak security, excessive personal data collection for user profiling, and a poor user experience are just a few of the many problems that mobile authentication solutions suffer from. Despite being an interesting platform, mobile devices are still not being used to their full potential for authentication. n-Auth is a firm step in unlocking the full potential of mobile devices in authentication, by improving both security and usability whilst respecting the privacy of the user. Our focus is on the combined usage of several strong cryptographic techniques with secure HCI design principles to achieve a better user experience. We specified and built n-Auth, for which robust Android and iOS apps are openly available through the official stores.
Zhou, Xinyan, Ji, Xiaoyu, Yan, Chen, Deng, Jiangyi, Xu, Wenyuan.  2019.  NAuth: Secure Face-to-Face Device Authentication via Nonlinearity. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :2080–2088.
With the increasing prevalence of mobile devices, face-to-face device-to-device (D2D) communication has been applied to a variety of daily scenarios such as mobile payment and short distance file transfer. In D2D communications, a critical security problem is verifying the legitimacy of devices when they share no secrets in advance. Previous research addressed the problem with device authentication and pairing schemes based on user intervention or exploiting physical properties of the radio or acoustic channels. However, a remaining challenge is to secure face-to-face D2D communication even in the middle of a crowd, within which an attacker may hide. In this paper, we present Nhuth, a nonlinearity-enhanced, location-sensitive authentication mechanism for such communication. Especially, we target at the secure authentication within a limited range such as 20 cm, which is the common case for face-to-face scenarios. Nhuth contains averification scheme based on the nonlinear distortion of speaker-microphone systems and a location-based-validation model. The verification scheme guarantees device authentication consistency by extracting acoustic nonlinearity patterns (ANP) while the validation model ensures device legitimacy by measuring the time difference of arrival (TDOA) at two microphones. We analyze the security of Nhuth theoretically and evaluate its performance experimentally. Results show that Nhuth can verify the device legitimacy in the presence of nearby attackers.
Chu, Jacqueline, Bryan, Chris, Shih, Min, Ferrer, Leonardo, Ma, Kwan-Liu.  2017.  Navigable Videos for Presenting Scientific Data on Affordable Head-Mounted Displays. Proceedings of the 8th ACM on Multimedia Systems Conference. :250–260.
Immersive, stereoscopic visualization enables scientists to better analyze structural and physical phenomena compared to traditional display mediums. Unfortunately, current head-mounted displays (HMDs) with the high rendering quality necessary for these complex datasets are prohibitively expensive, especially in educational settings where their high cost makes it impractical to buy several devices. To address this problem, we develop two tools: (1) An authoring tool allows domain scientists to generate a set of connected, 360° video paths for traversing between dimensional keyframes in the dataset. (2) A corresponding navigational interface is a video selection and playback tool that can be paired with a low-cost HMD to enable an interactive, non-linear, storytelling experience. We demonstrate the authoring tool's utility by conducting several case studies and assess the navigational interface with a usability study. Results show the potential of our approach in effectively expanding the accessibility of high-quality, immersive visualization to a wider audience using affordable HMDs.
Wang, Xiao-yu, Li, Cong-cong, Wu, Hao-dong, Zhang, De, Zhang, Xiao-dong, Gong, Xun.  2019.  NDE Application of Air-Coupled Transducer for Surface Crack Detection. 2019 13th Symposium on Piezoelectrcity, Acoustic Waves and Device Applications (SPAWDA). :1–4.
According to the technical difficulties of the air-coupled piezoelectric ultrasonic transducer, 1-3 type piezoelectric composites and double matching layers structure are adopted in order to solve the acoustic impedance mismatch at the interface between the piezoelectric materials and air. The optimal design of the matching layer thickness for double matching layers structure air-coupled ultrasonic transducer is also completed through experiments. Based on this, 440 kHz flat-plate and focused air-coupled piezoelectric ultrasonic transducer are designed, fabricated and characterized. Finally, surface cracks are detected using the focused air-coupled piezoelectric ultrasonic transducer.
Zhang, Zhiyi, Lu, Edward, Li, Yanbiao, Zhang, Lixia, Yu, Tianyuan, Pesavento, Davide, Shi, Junxiao, Benmohamed, Lotfi.  2018.  NDNoT: A Framework for Named Data Network of Things. Proceedings of the 5th ACM Conference on Information-Centric Networking. :200–201.
The Named Data Networking (NDN) architecture provides simple solutions to the communication needs of Internet of Things (IoT) in terms of ease-of-use, security, and content delivery. To utilize the desirable properties of NDN architecture in IoT scenarios, we are working to provide an integrated framework, dubbed NDNoT, to support IoT over NDN. NDNoT provides solutions to auto configuration, service discovery, data-centric security, content delivery, and other needs of IoT application developers. Utilizing NDN naming conventions, NDNoT aims to create an open environment where IoT applications and different services can easily cooperate and work together. This poster introduces the basic components of our framework and explains how these components function together.
van Do, Thanh, Engelstad, Paal, Feng, Boning, Do, Van Thuan.  2017.  A Near Real Time SMS Grey Traffic Detection. Proceedings of the 6th International Conference on Software and Computer Applications. :244–249.
Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user's security and privacy and consequently the operator's reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly.
Ito, Toshitaka, Itotani, Yuri, Wakabayashi, Shin'ichi, Nagayama, Shinobu, Inagi, Masato.  2018.  A Nearest Neighbor Search Engine Using Distance-Based Hashing. 2018 International Conference on Field-Programmable Technology (FPT). :150—157.
This paper proposes an FPGA-based nearest neighbor search engine for high-dimensional data, in which nearest neighbor search is performed based on distance-based hashing. The proposed hardware search engine implements a nearest neighbor search algorithm based on an extension of flexible distance-based hashing (FDH, for short), which finds an exact solution with high probability. The proposed engine is a parallel processing and pipelined circuit so that search results can be obtained in a short execution time. Experimental results show the effectiveness and efficiency of the proposed engine.
Shejawal, Pooja, Pansare, Jayshree R..  2016.  Nearest Neighbor Search Technique Using Keywords and Threshold. Proceedings of the ACM Symposium on Women in Research 2016. :7–11.

Today's applications asking for finding spatial protests nearest to a predefined area in the meantime fulfill limitation of keywords. Best answer for such questions depends on the IR2-tree, which has some inadequacies that truly affect system s efficiency. To defeat those inadequacies another access strategy is produced called the Spatial-inverted Index (SI) that extends the modified file to adapt to multidimensional information, and accompanies calculations that can answer closest neighbor queries with keywords continuously. This new technique SI is produced broadens the capacities of routine modified record makes do with multidimensional information, alongside the arrangement of using so as to move reach queries replied SI results to calculation which tackles the issue continuously.

Ahsan, Ramoza, Bashir, Muzammil, Neamtu, Rodica, Rundensteiner, Elke A., Sarkozy, Gabor.  2019.  Nearest Neighbor Subsequence Search in Time Series Data. 2019 IEEE International Conference on Big Data (Big Data). :2057—2066.
Continuous growth in sensor data and other temporal sequence data necessitates efficient retrieval and similarity search support on these big time series datasets. However, finding exact similarity results, especially at the granularity of subsequences, is known to be prohibitively costly for large data sets. In this paper, we thus propose an efficient framework for solving this exact subsequence similarity match problem, called TINN (TIme series Nearest Neighbor search). Exploiting the range interval diversity properties of time series datasets, TINN captures similarity at two levels of abstraction, namely, relationships among subsequences within each long time series and relationships across distinct time series in the data set. These relationships are compactly organized in an augmented relationship graph model, with the former relationships encoded in similarity vectors at TINN nodes and the later captured by augmented edge types in the TINN Graph. Query processing strategy deploy novel pruning techniques on the TINN Graph, including node skipping, vertical and horizontal pruning, to significantly reduce the number of time series as well as subsequences to be explored. Comprehensive experiments on synthetic and real world time series data demonstrate that our TINN model consistently outperforms state-of-the-art approaches while still guaranteeing to retrieve exact matches.
Ghaffari, Mohsen, Parter, Merav.  2016.  Near-Optimal Distributed Algorithms for Fault-Tolerant Tree Structures. Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures. :387–396.

Tree structures such as breadth-first search (BFS) trees and minimum spanning trees (MST) are among the most fundamental graph structures in distributed network algorithms. However, by definition, these structures are not robust against failures and even a single edge's removal can disrupt their functionality. A well-studied concept which attempts to circumvent this issue is Fault-Tolerant Tree Structures, where the tree gets augmented with additional edges from the network so that the functionality of the structure is maintained even when an edge fails. These structures, or other equivalent formulations, have been studied extensively from a centralized viewpoint. However, despite the fact that the main motivations come from distributed networks, their distributed construction has not been addressed before. In this paper, we present distributed algorithms for constructing fault tolerant BFS and MST structures. The time complexity of our algorithms are nearly optimal in the following strong sense: they almost match even the lower bounds of constructing (basic) BFS and MST trees.

Novak, E., Qun Li.  2014.  Near-pri: Private, proximity based location sharing. INFOCOM, 2014 Proceedings IEEE. :37-45.

As the ubiquity of smartphones increases we see an increase in the popularity of location based services. Specifically, online social networks provide services such as alerting the user of friend co-location, and finding a user's k nearest neighbors. Location information is sensitive, which makes privacy a strong concern for location based systems like these. We have built one such service that allows two parties to share location information privately and securely. Our system allows every user to maintain and enforce their own policy. When one party, (Alice), queries the location of another party, (Bob), our system uses homomorphic encryption to test if Alice is within Bob's policy. If she is, Bob's location is shared with Alice only. If she is not, no user location information is shared with anyone. Due to the importance and sensitivity of location information, and the easily deployable design of our system, we offer a useful, practical, and important system to users. Our main contribution is a flexible, practical protocol for private proximity testing, a useful and efficient technique for representing location values, and a working implementation of the system we design in this paper. It is implemented as an Android application with the Facebook online social network used for communication between users.

Zhou, Wenxuan, Croft, Jason, Liu, Bingzhe, Caesar, Matthew.  2017.  NEAt: Network Error Auto-Correct. Proceedings of the Symposium on SDN Research. :157–163.

Configuring and maintaining an enterprise network is a challenging and error-prone process. Administrators must often consider security policies from a variety of sources simultaneously, including regulatory requirements, industry standards, and to mitigate attack vectors. Erroneous implementation of a policy, however, can result in costly data breaches and intrusions. Relying on humans to discover and troubleshoot violations is slow and prone to error, considering the speed at which new attack vectors propagate and the increasing network dynamics, partly an effect of SDN. To ensure the network is always in a state consistent with the desired policies, administrators need frameworks to automatically diagnose and repair violations in real-time. To address this problem, we present NEAt, a system analogous to a smartphone's autocorrect feature that enables on-the-fly repair to policy-violating updates. NEAt modifies the forwarding behavior of updates to automatically repair violations of properties such as reachability, service chaining, and segmentation. NEAt sits between an SDN controller and the forwarding devices, and intercepts updates proposed by SDN applications. If an update violates the policy defined by an administrator, such as reachability or segmentation, NEAt transforms the update into one that complies with the policy. Unlike domain-specific languages or synthesis platforms, NEAt allows enterprise networks to leverage the advanced functionality of SDN applications while simultaneously achieving strong, automated enforcement of general policies.