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Lemeshko, Oleksandr, Yeremenko, Oleksandra, Yevdokymenko, Maryna, Ageyev, Dmytro.  2020.  Redundancy Cyber Resiliency Technique Based on Fast ReRouting under Security Metric. 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S T). :815–818.
The paper is devoted to the development and research of the redundancy cyber resiliency technique based on fast rerouting under security metric with the implementation of the basic schemes for network elements protection, namely node, link, path, and bandwidth. Within the model, the secure fast rerouting task is formulated as an optimization problem of nonlinear programming. The model is configured in order to calculate primary and backup paths that contain links with the minimum values of the probability of compromise that is achieved by using the appropriate weights in the objective function, the value of which is minimized. Numerical research has been conducted, results of which proved the proposed model efficiency and adequacy for the practical application.
Chheng, Kimhok, Priyadi, Ardyono, Pujiantara, Margo, Mahindara, Vincentius Raki.  2020.  The Coordination of Dual Setting DOCR for Ring System Using Adaptive Modified Firefly Algorithm. 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA). :44—50.
Directional Overcurrent Relays (DOCRs) play an essential role in the power system protection to guarantee the reliability, speed of relay operation and avoiding mal-trip in the primary and backup relays when unintentional fault conditions occur in the system. Moreover, the dual setting protection scheme is more efficient protection schemes for offering fast response protection and providing flexibility in the coordination of relay. In this paper, the Adaptive Modified Firefly Algorithm (AMFA) is used to determine the optimal coordination of dual setting DOCRs in the ring distribution system. The AMFA is completed by choosing the minimum value of pickup current (\textbackslashtextbackslashpmbI\textbackslashtextbackslashpmbP) and time dial setting (TDS). On the other hand, dual setting DOCRs protection scheme also proposed for operating in both forward and reverse directions that consisted of individual time current characteristics (TCC) curve for each direction. The previous method is applied to the ring distribution system network of PT. Pupuk Sriwidjaja by considering the fault on each bus. The result illustration that the AMFA within dual setting protection scheme is significantly reaching the optimized coordination and the relay coordination is certain for all simulation scenarios with the minimum operation. The AMFA has been successfully implemented in MATLAB software programming.
Ozmen, Alper, Yildiz, Huseyin Ugur, Tavli, Bulent.  2020.  Impact of Minimizing the Eavesdropping Risks on Lifetime of Underwater Acoustic Sensor Networks. 2020 28th Telecommunications Forum (℡FOR). :1—4.
Underwater Acoustic Sensor Networks (UASNs) are often deployed in hostile environments, and they face many security threats. Moreover, due to the harsh characteristics of the underwater environment, UASNs are vulnerable to malicious attacks. One of the most dangerous security threats is the eavesdropping attack, where an adversary silently collects the information exchanged between the sensor nodes. Although careful assignment of transmission power levels and optimization of data flow paths help alleviate the extent of eavesdropping attacks, the network lifetime can be negatively affected since routing could be established using sub-optimal paths in terms of energy efficiency. In this work, two optimization models are proposed where the first model minimizes the potential eavesdropping risks in the network while the second model maximizes the network lifetime under a certain level of an eavesdropping risk. The results show that network lifetimes obtained when the eavesdropping risks are minimized significantly shorter than the network lifetimes obtained without considering any eavesdropping risks. Furthermore, as the countermeasures against the eavesdropping risks are relaxed, UASN lifetime is shown to be prolonged, significantly.
Vuppalapati, C., Ilapakurti, A., Kedari, S., Vuppalapati, R., Vuppalapati, J., Kedari, S..  2020.  The Role of Combinatorial Mathematical Optimization and Heuristics to improve Small Farmers to Veterinarian access and to create a Sustainable Food Future for the World. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :214–221.
The Global Demand for agriculture and dairy products is rising. Demand is expected to double by 2050. This will challenge agriculture markets in a way we have not seen before. For instance, unprecedented demand to increase in dairy farm productivity of already shrinking farms, untethered perpetual access to veterinarians by small dairy farms, economic engines of the developing countries, for animal husbandry and, finally, unprecedented need to increase productivity of veterinarians who're already understaffed, over-stressed, resource constrained to meet the current global dairy demands. The lack of innovative solutions to address the challenge would result in a major obstacle to achieve sustainable food future and a colossal roadblock ending economic disparities. The paper proposes a novel innovative data driven framework cropped by data generated using dairy Sensors and by mathematical formulations using Solvers to generate an exclusive veterinarian daily farms prioritized visit list so as to have a greater coverage of the most needed farms performed in-time and improve small farmers access to veterinarians, a precious and highly shortage & stressed resource.
Soliman, H. M..  2020.  An Optimization Approach to Graph Partitioning for Detecting Persistent Attacks in Enterprise Networks. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1—6.
Advanced Persistent Threats (APTs) refer to sophisticated, prolonged and multi-step attacks, planned and executed by skilled adversaries targeting government and enterprise networks. Attack graphs' topologies can be leveraged to detect, explain and visualize the progress of such attacks. However, due to the abundance of false-positives, such graphs are usually overwhelmingly large and difficult for an analyst to understand. Graph partitioning refers to the problem of reducing the graph of alerts to a set of smaller incidents that are easier for an analyst to process and better represent the actual attack plan. Existing approaches are oblivious to the security-context of the problem at hand and result in graphs which, while smaller, make little sense from a security perspective. In this paper, we propose an optimization approach allowing us to generate security-aware partitions, utilizing aspects such as the kill chain progression, number of assets involved, as well as the size of the graph. Using real-world datasets, the results show that our approach produces graphs that are better at capturing the underlying attack compared to state-of-the-art approaches and are easier for the analyst to understand.
Koda, S., Kambara, Y., Oikawa, T., Furukawa, K., Unno, Y., Murakami, M..  2020.  Anomalous IP Address Detection on Traffic Logs Using Novel Word Embedding. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :1504–1509.
This paper presents an anomalous IP address detection algorithm for network traffic logs. It is based on word embedding techniques derived from natural language processing to extract the representative features of IP addresses. However, the features extracted from vanilla word embeddings are not always compatible with machine learning-based anomaly detection algorithms. Therefore, we developed an algorithm that enables the extraction of more compatible features of IP addresses for anomaly detection than conventional methods. The proposed algorithm optimizes the objective functions of word embedding-based feature extraction and anomaly detection, simultaneously. According to the experimental results, the proposed algorithm outperformed conventional approaches; it improved the detection performance from 0.876 to 0.990 in the area under the curve criterion in a task of detecting the IP addresses of attackers from network traffic logs.
Ge, K., He, Y..  2020.  Detection of Sybil Attack on Tor Resource Distribution. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :328–332.
Tor anonymous communication system's resource publishing is vulnerable to enumeration attacks. Zhao determines users who requested resources are unavailable as suspicious malicious users, and gradually reduce the scope of suspicious users through several stages to reduce the false positive rate. However, it takes several stages to distinguish users. Although this method successfully detects the malicious user, the malicious user has acquired many resources in the previous stages, which reduce the availability of the anonymous communication system. This paper proposes a detection method based on Integer Linear Program to detect malicious users who perform enumeration attacks on resources in the process of resource distribution. First, we need construct a bipartite graph between the unavailable resources and the users who requested for these resources in the anonymous communication system; next we use Integer Linear Program to find the minimum malicious user set. We simulate the resource distribution process through computer program, we perform an experimental analysis of the method in this paper is carried out. Experimental results show that the accuracy of the method in this paper is above 80%, when the unavailable resources in the system account for no more than 50%. It is about 10% higher than Zhao's method.
Zhang, Y., Deng, L., Chen, M., Wang, P..  2018.  Joint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse? IEEE/ACM Transactions on Networking. 26:1049—1062.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

Agadakos, I., Ciocarlie, G. F., Copos, B., George, J., Leslie, N., Michaelis, J..  2019.  Security for Resilient IoBT Systems: Emerging Research Directions. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :1—6.

Continued advances in IoT technology have prompted new investigation into its usage for military operations, both to augment and complement existing military sensing assets and support next-generation artificial intelligence and machine learning systems. Under the emerging Internet of Battlefield Things (IoBT) paradigm, a multitude of operational conditions (e.g., diverse asset ownership, degraded networking infrastructure, adversary activities) necessitate the development of novel security techniques, centered on establishment of trust for individual assets and supporting resilience of broader systems. To advance current IoBT efforts, a set of research directions are proposed that aim to fundamentally address the issues of trust and trustworthiness in contested battlefield environments, building on prior research in the cybersecurity domain. These research directions focus on two themes: (1) Supporting trust assessment for known/unknown IoT assets; (2) Ensuring continued trust of known IoBT assets and systems.

Carvalho, Martha R, Bezerra, Bernardo, Dall'Orto, Celso, Carlos, Luiz, Rosenblatt, Jose, Veiga, Mario.  2018.  Methodology for determining the energy deficit penalty function for hydrothermal dispatch. 2018 Simposio Brasileiro de Sistemas Eletricos (SBSE). :1—6.
The penalization of the objective function due to energy deficits is a key element for determining the operational policy of hydroelectric reservoirs. Its definition impacts not only operations, but also system expansion. Brazil historically defined these penalties with basis on a proxy of the economic deficit cost, a value in \$/MWh obtained with aid of the Input-Output Matrix. We propose an approach where these penalties are obtained in order to minimize the operation cost and cost of rationing of the system, considering a criterion of security of supply. A case study with data from the Brazilian System illustrates its application.
Ramachandran, Ragesh K., Preiss, James A., Sukhatme, Gaurav S..  2019.  Resilience by Reconfiguration: Exploiting Heterogeneity in Robot Teams. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). :6518—6525.

We propose a method to maintain high resource availability in a networked heterogeneous multi-robot system subject to resource failures. In our model, resources such as sensing and computation are available on robots. The robots are engaged in a joint task using these pooled resources. When a resource on a particular robot becomes unavailable (e.g., a sensor ceases to function), the system automatically reconfigures so that the robot continues to have access to this resource by communicating with other robots. Specifically, we consider the problem of selecting edges to be modified in the system's communication graph after a resource failure has occurred. We define a metric that allows us to characterize the quality of the resource distribution in the network represented by the communication graph. Upon a resource becoming unavailable due to failure, we reconFigure the network so that the resource distribution is brought as close to the maximal resource distribution as possible without a large change in the number of active inter-robot communication links. Our approach uses mixed integer semi-definite programming to achieve this goal. We employ a simulated annealing method to compute a spatial formation that satisfies the inter-robot distances imposed by the topology, along with other constraints. Our method can compute a communication topology, spatial formation, and formation change motion planning in a few seconds. We validate our method in simulation and real-robot experiments with a team of seven quadrotors.

Parvina, Hashem, Moradi, Parham, Esmaeilib, Shahrokh, Jalilic, Mahdi.  2018.  An Efficient Recommender System by Integrating Non-Negative Matrix Factorization With Trust and Distrust Relationships. 2018 IEEE Data Science Workshop (DSW). :135—139.

Matrix factorization (MF) has been proved to be an effective approach to build a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of user-item matrix. Moreover, these methods suffer from the scalability issues when applying on large-scale real-world tasks. To tackle these issues, in this paper a social regularization method called TrustRSNMF is proposed that incorporates the social trust information of users in nonnegative matrix factorization framework. The proposed method integrates trust statements along with user-item ratings as an additional information source into the recommendation model to deal with the data sparsity and cold-start issues. In order to evaluate the effectiveness of the proposed method, a number of experiments are performed on two real-world datasets. The obtained results demonstrate significant improvements of the proposed method compared to state-of-the-art recommendation methods.

Bartan, Burak, Pilanci, Mert.  2019.  Distributed Black-Box optimization via Error Correcting Codes. 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :246—252.
We introduce a novel distributed derivative-free optimization framework that is resilient to stragglers. The proposed method employs coded search directions at which the objective function is evaluated, and a decoding step to find the next iterate. Our framework can be seen as an extension of evolution strategies and structured exploration methods where structured search directions were utilized. As an application, we consider black-box adversarial attacks on deep convolutional neural networks. Our numerical experiments demonstrate a significant improvement in the computation times.
Osia, Seyed Ali, Rassouli, Borzoo, Haddadi, Hamed, Rabiee, Hamid R., Gündüz, Deniz.  2019.  Privacy Against Brute-Force Inference Attacks. 2019 IEEE International Symposium on Information Theory (ISIT). :637—641.
Privacy-preserving data release is about disclosing information about useful data while retaining the privacy of sensitive data. Assuming that the sensitive data is threatened by a brute-force adversary, we define Guessing Leakage as a measure of privacy, based on the concept of guessing. After investigating the properties of this measure, we derive the optimal utility-privacy trade-off via a linear program with any f-information adopted as the utility measure, and show that the optimal utility is a concave and piece-wise linear function of the privacy-leakage budget.
Huang, Hao, Kazerooni, Maryam, Hossain-McKenzie, Shamina, Etigowni, Sriharsha, Zonouz, Saman, Davis, Katherine.  2019.  Fast Generation Redispatch Techniques for Automated Remedial Action Schemes. 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP). :1–8.
To ensure power system operational security, it not only requires security incident detection, but also automated intrusion response and recovery mechanisms to tolerate failures and maintain the system's functionalities. In this paper, we present a design procedure for remedial action schemes (RAS) that improves the power systems resiliency against accidental failures or malicious endeavors such as cyber attacks. A resilience-oriented optimal power flow is proposed, which optimizes the system security instead of the generation cost. To improve its speed for online application, a fast greedy algorithm is presented to narrow the search space. The proposed techniques are computationally efficient and are suitable for online RAS applications in large-scale power systems. To demonstrate the effectiveness of the proposed methods, there are two case studies with IEEE 24-bus and IEEE 118-bus systems.
Grochol, David, Sekanina, Lukas.  2018.  Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs. 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS). :257—263.

Efficient monitoring of high speed computer networks operating with a 100 Gigabit per second (Gbps) data throughput requires a suitable hardware acceleration of its key components. We present a platform capable of automated designing of hash functions suitable for network flow hashing. The platform employs a multi-objective linear genetic programming developed for the hash function design. We evolved high-quality hash functions and implemented them in a field programmable gate array (FPGA). Several evolved hash functions were combined together in order to form a new reconfigurable hash function. The proposed reconfigurable design significantly reduces the area on a chip while the maximum operation frequency remains very close to the fastest hash functions. Properties of evolved hash functions were compared with the state-of-the-art hash functions in terms of the quality of hashing, area and operation frequency in the FPGA.

Sahabandu, Dinuka, Moothedath, Shana, Bushnell, Linda, Poovendran, Radha, Aller, Joey, Lee, Wenke, Clark, Andrew.  2019.  A Game Theoretic Approach for Dynamic Information Flow Tracking with Conditional Branching. 2019 American Control Conference (ACC). :2289–2296.
In this paper, we study system security against Advanced Persistent Threats (APTs). APTs are stealthy and persistent but APTs interact with system and introduce information flows in the system as data-flow and control-flow commands. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism against APTs which taints suspicious input sources in the system and performs online security analysis when a tainted information is used in unauthorized manner. Our objective in this paper is to model DIFT that handle data-flow and conditional branches in the program that arise from control-flow commands. We use game theoretic framework and provide the first analytical model of DIFT with data-flow and conditional-branch tracking. Our game model which is an undiscounted infinite-horizon stochastic game captures the interaction between APTs and DIFT and the notion of conditional branching. We prove that the best response of the APT is a maximal reachability probability problem and provide a polynomial-time algorithm to find the best response by solving a linear optimization problem. We formulate the best response of the defense as a linear optimization problem and show that an optimal solution to the linear program returns a deterministic optimal policy for the defense. Since finding Nash equilibrium for infinite-horizon undiscounted stochastic games is computationally difficult, we present a nonlinear programming based polynomial-time algorithm to find an E-Nash equilibrium. Finally, we perform experimental analysis of our algorithm on real-world data for NetRecon attack augmented with conditional branching.
Patel, Himanshu B., Jinwala, Devesh C..  2019.  Blackhole Detection in 6LoWPAN Based Internet of Things: An Anomaly Based Approach. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). :947—954.

The Internet of things networks is vulnerable to many DOS attacks. Among them, Blackhole attack is one of the severe attacks as it hampers communication among network devices. In general, the solutions presented in the literature for Blackhole detection are not efficient. In addition, the existing approaches do not factor-in, the consumption in resources viz. energy, bandwidth and network lifetime. Further, these approaches are also insensitive to the mechanism used for selecting a parent in on Blackhole formation. Needless to say, a blackhole node if selected as parent would lead to orchestration of this attack trivially and hence it is an important factor in selection of a parent. In this paper, we propose SIEWE (Strainer based Intrusion Detection of Blackhole in 6LoWPAN for the Internet of Things) - an Intrusion detection mechanism to identify Blackhole attack on Routing protocol RPL in IoT. In contrast to the Watchdog based approaches where every node in network runs in promiscuous mode, SIEWE filters out suspicious nodes first and then verifies the behavior of those nodes only. The results that we obtain, show that SIEWE improves the Packet Delivery Ratio (PDR) of the system by blacklisting malicious Blackhole nodes.

Pan, Huan, Lian, Honghui, Na, Chunning.  2019.  Vulnerability Analysis of Smart Grid under Community Attack Style. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 1:5971—5976.
The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.
Gregory, Jason M., Al-Hussaini, Sarah, Gupta, Satyandra K..  2019.  Heuristics-Based Multi-Agent Task Allocation for Resilient Operations. 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). :1–8.
Multi-Agent Task Allocation is a pre-requisite for many autonomous, real-world systems because of the need for intelligent task assignment amongst a team for maximum efficiency. Similarly, agent failure, task, failure, and a lack of state information are inherent challenges when operating in complex environments. Many existing solutions make simplifying assumptions regarding the modeling of these factors, e.g., Markovian state information. However, it is not clear that this is always the appropriate approach or that results from these approaches are necessarily representative of performance in the natural world. In this work, we demonstrate that there exists a class of problems for which non-Markovian state modeling is beneficial. Furthermore, we present and characterize a novel heuristic for task allocation that incorporates realistic state and uncertainty modeling in order to improve performance. Our quantitative analysis, when tested in a simulated search and rescue (SAR) mission, shows a decrease in performance of more than 57% when a representative method with Markovian assumptions is tested in a non-Markovian setting. Our novel heuristic has shown an improvement in performance of 3-15%, in the same non-Markovian setting, by modeling probabilistic failure and making fewer assumptions.
Zhao, Guowei, Zhao, Rui, Wang, Qiang, Xue, Hui, Luo, Fang.  2019.  Virtual Network Mapping Algorithm for Self-Healing of Distribution Network. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1442–1445.
This paper focuses on how to provide virtual network (VN) with the survivability of node failure. In the SVNE that responds to node failures, the backup mechanism provided by the VN initial mapping method should be as flexible as possible, so that backup resources can be shared among the VNs, thereby providing survivability support for the most VNs with the least backup overhead, which can improve The utilization of backup resources can also improve the survivability of VN to deal with multi-node failures. For the remapping method of virtual networks, it needs to be higher because it involves both remapping of virtual nodes and remapping of related virtual links. The remapping efficiency, so as to restore the affected VN to a normal state as soon as possible, to avoid affecting the user's business experience. Considering that the SVNE method that actively responds to node failures always has a certain degree of backup resource-specific phenomenon, this section provides a SVNE method that passively responds to node failures. This paper mainly introduces the survivability virtual network initial mapping method based on physical node recoverability in this method.
Fuchs, Caro, Spolaor, Simone, Nobile, Marco S., Kaymak, Uzay.  2019.  A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.
Tabakhpour, Adel, Abdelaziz, Morad M. A..  2019.  Neural Network Model for False Data Detection in Power System State Estimation. 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). :1-5.

False data injection is an on-going concern facing power system state estimation. In this work, a neural network is trained to detect the existence of false data in measurements. The proposed approach can make use of historical data, if available, by using them in the training sets of the proposed neural network model. However, the inputs of perceptron model in this work are the residual elements from the state estimation, which are highly correlated. Therefore, their dimension could be reduced by preserving the most informative features from the inputs. To this end, principal component analysis is used (i.e., a data preprocessing technique). This technique is especially efficient for highly correlated data sets, which is the case in power system measurements. The results of different perceptron models that are proposed for detection, are compared to a simple perceptron that produces identical result to the outlier detection scheme. For generating the training sets, state estimation was run for different false data on different measurements in 13-bus IEEE test system, and the residuals are saved as inputs of training sets. The testing results of the trained network show its good performance in detection of false data in measurements.

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.

Nasiruzzaman, A. B. M., Akter, M. N., Mahmud, M. A., Pota, H. R..  2018.  Network Theory Based Power Grid Criticality Assessment. 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). :1-5.

A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.