Biblio

The successful realization of the climate goals agreed upon in the European Union's COP21 commitments makes a fundamental change of the European energy system necessary. In particular, for a reduction of greenhouse gas emissions over 80%, the use of renewable energies must be increased not only in the electricity sector but also across all energy sectors, such as heat and mobility. Furthermore, a progressive integration of renewable energies increases the risk of congestions in the transmission grid and makes network expansion necessary. An efficient planning for future energy systems must comprise the coupling of energy sectors as well as interdependencies of generation and transmission grid infrastructure. However, in traditional energy system planning, these aspects are considered as decoupled. Therefore, the project PlaMES develops an approach for integrated planning of multi-energy systems on a European scale. This paper aims at analyzing the model requirements and describing the modeling approach.

Local flexibility markets are a concept to integrate distributed flexibilities such as power-to-gas, power-to-heat systems, electric vehicles, and battery storage systems into congestion management of distribution and transmission grids. However, the activation of the flexibility of storage systems changes their state-of-charge and thus reduces their available flexibility. Counter-trading or compensation of activated flexibility in later points of time lets storage operators regain flexibility. However, this compensation might create or amplify congestions when not supervised by system operators. Therefore, we propose the inclusion of compensation within the flexibility market clearing process by system operators. We further develop a linear formulation of flexibility market bids by storage systems that takes the need for compensation into account. For this, first, the operational planning formulation of a storage system is expanded by flexibility market participation. Subsequently, the linear formulation of bids is derived and demonstrated.

With increasing improvements in different areas, Internet control has been making prominent impacts in almost all areas of technology that has resulted in reasonable advances in every discrete field and therefore the industries too are proceeding to the field of IoT (Internet of Things), in which the communication among heterogeneous equipments is via Internet broadly. So imparting these advances of technology in the Power Station Plant sectors i.e. the power plants will be remotely controlled additional to remote monitoring, with no corporal place as a factor for controlling or monitoring. But imparting this technology the security factor needs to be considered as a basic and such methods need to be put into practice that the communication in such networks or control systems is defended against any third party interventions while the data is being transferred from one device to the other device through the internet (Unrestricted Channel). The paper puts forward exercising RSA,DES and AES encrypting schemes for the purpose of data encryption at the Data Link Layer i.e. before it is transmitted to the other device through Internet and as a result of this the security constraints are maintained. The records put to use have been supplied by NTPC, Dadri, India plus simulation part was executed employing MATLAB.

Volume, Variety, Velocity, Veracity & Value of data has drawn the attention of many analysts in the last few years. Performance optimization and comparison are the main challenges we face when we talk about the humongous volume of data. Data Analysts use data for activities like forecasting or deep learning and to process these data various tools are available which helps to achieve this task with minimum efforts. Recommendation System plays a crucial role while running any business such as a shopping website or travel agency where the system recommends the user according to their search history, likes, comments, or their past order/booking details. Recommendation System works on various strategies such as Content Filtering, Collaborative Filtering, Neighborhood Methods, or Matrix Factorization methods. For achieving maximum efficiency and accuracy based on the data a specific strategy can be the best case or the worst case for that scenario. Matrix Factorization is the key point of interest in this work. Matrix Factorization strategy includes multiplication of user matrix and item matrix in-order to get a rating matrix that can be recommended to the users. Matrix Multiplication can be achieved by using various algorithms such as Naive Algorithm, Strassen Algorithm, Coppersmith - Winograd (CW) Algorithm. In this work, a new algorithm is proposed to achieve less amount of time and space complexity used in-order for performing matrix multiplication which helps to get the results much faster. By using the Matrix Factorization strategy with various Matrix Multiplication Algorithm we are going to perform a comparative analysis of the same to conclude the proposed algorithm is more efficient.

Recommender systems (RSs) are intelligent systems to help e-commerce users to find their preferred items among millions of available items by considering the profiles of both users and items. These systems need to predict the unknown ratings and then recommend a set of high rated items. Among the others, Collaborative Filtering (CF) is a successful recommendation approach and has been utilized in many real-world systems. CF methods seek to predict missing ratings by considering the preferences of those users who are similar to the target user. A major task in Collaborative Filtering is to identify an accurate set of users and employing them in the rating prediction process. Most of the CF-based methods suffer from the cold-start issue which arising from an insufficient number of ratings in the prediction process. This is due to the fact that users only comment on a few items and thus CF methods faced with a sparse user-item matrix. To tackle this issue, a new collaborative filtering method is proposed that has a trust-aware strategy. The proposed method employs the trust relationships of users as additional information to help the CF tackle the cold-start issue. To this end, the proposed integrated trust relationships in the prediction process by using the Ant Colony Optimization (ACO). The proposed method has four main steps. The aim of the first step is ranking users based on their similarities to the target user. This step uses trust relationships and the available rating values in its process. Then in the second step, graph clustering methods are used to cluster the trust graph to group similar users. In the third step, the users are weighted based on their similarities to the target users. To this end, an ACO process is employed on the users' graph. Finally, those of top users with high similarity to the target user are used in the rating prediction process. The superiority of our method has been shown in the experimental results in comparison with well-known and state-of-the-art methods.

The number of Mobile device users in the word has evolved rapidly. Many internet users currently want to connect the internet for all utilities automatically. One of the technologies in the IPv6 network, which supports data access from moving users, is IPv6 Mobile protocol. In its mobility, the users on a range of networks can move the range to another network. High demand for this technology will interest to a hacker or a cracker to carry out an attack. One of them is a DoS attack that compromises a target to denial its services. A firewall is usually used to protect networks from external attacks. However, since the firewall based on the attacker database, the unknown may not be detected. In order to address the obstacle, a detection tool could be used. In this research, IDS as an intrusion detection tool was integrated with a firewall to be implemented in IPv6 Mobile to stop the DoS attack. The results of some experiments showed that the integration system could block the attack at 0.9 s in Correspondent Node and 1.2 s in Home Agent. The blocked attack can decrease the network throughput up to 27.44% when a Mobile Node in Home Agent, 28,87% when the Mobile Node in a Foreign Network. The final result of the blocked attack is reducing the average CPU utilization up to 30.99%.

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.

With the development of positioning technology and the popularity of mobile devices, location-based services have been widely deployed. To use the services, users must provide the server accurate location information, during which the attacker tends to infer sensitive information from intercepting queries. In this paper, we model the road network as an edge cluster graph with its location semantics considered. Then, we propose the Circle First Structure Optimization (CFSO) algorithm which generates an anonymous set by adding optimal adjacent locations. Furthermore, we introduce controllable randomness and propose the Attack-Resilient (AR) algorithm to enhance the anti-attack ability. Meanwhile, to reduce the system overhead, our algorithms build the anonymous set quickly and take the structure of the anonymous set into account. Finally, we conduct experiments on a real map and the results demonstrate a higher anonymity success rate and a stronger anti-attack capability with less system overhead.

The existence of adversarial samples makes the security of machine learning models in practical application questioned, especially the black-box adversarial attack, which is very close to the actual application scenario. Efficient search for black-box attack samples is helpful to train more robust models. We discuss the situation that the attacker can get nothing except the final predict label. As for this problem, the current state-of-the-art method is Boundary Attack(BA) and its variants, such as Biased Boundary Attack(BBA), however it still requires large number of queries and kills a lot of time. In this paper, we propose a novel method to solve these shortcomings. First, we improved the algorithm for generating initial adversarial samples with smaller L2 distance. Second, we innovatively combine a swarm intelligence algorithm - Particle Swarm Optimization(PSO) with Biased Boundary Attack and propose PSO-BBA method. Finally, we experiment on ImageNet dataset, and compared our algorithm with the baseline algorithm. The results show that:(1)our improved initial point selection algorithm effectively reduces the number of queries;(2)compared with the most advanced methods, our PSO-BBA method improves the convergence speed while ensuring the attack accuracy;(3)our method has a good effect on both targeted attack and untargeted attack.

We address energy-efficient placement of data and analytics components of composite analytics services on a wireless network to minimize execution-time energy consumption (computation and communication) subject to compute, storage and network resource constraints. We introduce an expressive analytics service hypergraph model for representing k-ary composability relationships (k ≥ 2) between various analytics and data components and leverage binary quadratic programming (BQP) to minimize the total energy consumption of a given placement of the analytics hypergraph nodes on the network subject to resource availability constraints. Then, after defining a potential energy functional Φ(·) to model the affinities of analytics components and network resources using analogs of attractive and repulsive forces in physics, we propose a decentralized Metropolis Monte Carlo (MMC) sampling method which seeks to minimize Φ by moving analytics and data on the network. Although Φ is non-convex, using a potential game formulation, we identify conditions under which the algorithm provably converges to a local minimum energy equilibrium placement configuration. Trace-based simulations of the placement of a deep-neural-network analytics service on a realistic wireless network show that for smaller problem instances our MMC algorithm yields placements with total energy within a small factor of BQP and more balanced workload distributions; for larger problems, it yields low-energy configurations while the BQP approach fails.

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.

With the concept of “wireless city”, 3G, WIFI and other wireless network coverages have become more extensive. Data transmission rate has achieved a qualitative leap, providing feasibility for the implementation of mobile video surveillance solutions. The mobile video monitoring system based on the bee population optimization algorithm proposed in this paper makes up for the defects of traditional network video surveillance, and according to the video surveillance system monitoring command, the optimal visual effect of the current state of the observed object can be rendered quickly and steadily through the optimization of the camera linkage model and simulation analysis.

This paper discusses formulations and algorithms which allow a number of agents to collectively solve problems involving both (non-convex) minimization and (concave) maximization operations. These problems have a number of interesting applications in information processing and machine learning, and in particular can be used to model an adversary learning problem called network data poisoning. We develop a number of algorithms to efficiently solve these non-convex min-max optimization problems, by combining techniques such as gradient tracking in the decentralized optimization literature and gradient descent-ascent schemes in the min-max optimization literature. Also, we establish convergence to a first order stationary point under certain conditions. Finally, we perform experiments to demonstrate that the proposed algorithms are effective in the data poisoning attack.

Following substantial advancements in stochastic classes of decision-making optimization problems, scenario-based stochastic optimization, robust\textbackslashtextbackslash distributionally robust optimization, and chance-constrained optimization have recently gained an increasing attention. Despite the remarkable developments in probabilistic forecast of uncertainties (e.g., in renewable energies), most approaches are still being employed in a univariate framework which fails to unlock a full understanding on the underlying interdependence among uncertain variables of interest. In order to yield cost-optimal solutions with predefined probabilistic guarantees, conditional and dynamic interdependence in uncertainty forecasts should be accommodated in power systems decision-making. This becomes even more important during the emergencies where high-impact low-probability (HILP) disasters result in remarkable fluctuations in the uncertain variables. In order to model the interdependence correlation structure between different sources of uncertainty in power systems during both normal and emergency operating conditions, this paper aims to bridge the gap between the probabilistic forecasting methods and advanced optimization paradigms; in particular, perdition regions are generated in the form of ellipsoids with probabilistic guarantees. We employ a modified Khachiyan's algorithm to compute the minimum volume enclosing ellipsoids (MVEE). Application results based on two datasets on wind and photovoltaic power are used to verify the efficiency of the proposed framework.

In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to compute the true states in-spite of the adversarial corruption. In the formulation, we use a measurement prior distribution generated by the auxiliary model to refine the feasible region of a traditional compressive sensing-based regression problem. A constrained optimization-based observer is developed using l1-minimization scheme. Numerical experiments show that the solution of the resulting problem recovers the true states of the system. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system.

Implementation of Internet-of-Things (IoT) can take place in many applications, for instance, automobiles, and industrial automation. We generally view the role of an Electronic Control Unit (ECU) or industrial network node that is occupied and interconnected in many different configurations in a vehicle or a factory. This condition may raise the occurrence of problems related to security issues, such as unauthorized access to data or components in ECUs or industrial network nodes. In this paper, we propose a hardware (HW)/software (SW) framework having integrated security extensions complemented with various security-related features that later can be implemented directly from the framework to All Programmable Multiprocessor System-on-Chip (AP MPSoC)-based ECUs. The framework is a software-defined one that can be configured or reconfigured in a higher level of abstraction language, including High-Level Synthesis (HLS), and the output of the framework is hardware configuration in multiprocessor or reconfigurable components in the FPGA. The system comprises high-level requirements, covert and side-channel estimation, cryptography, optimization, artificial intelligence, and partial reconfiguration. With this framework, we may reduce the design & development time, and provide significant flexibility to configure/reconfigure our framework and its target platform equipped with security extensions.

Users leave when page loads take too long. This simple fact has complex implications for virtually all modern businesses, because accelerating content delivery through caching is not as simple as it used to be. As a fundamental technical challenge, the high degree of personalization in today's Web has seemingly outgrown the capabilities of traditional content delivery networks (CDNs) which have been designed for distributing static assets under fixed caching times. As an additional legal challenge for services with personalized content, an increasing number of regional data protection laws constrain the ways in which CDNs can be used in the first place. In this paper, we present Speed Kit as a radically different approach for content distribution that combines (1) a polyglot architecture for efficiently caching personalized content with (2) a natively GDPR-compliant client proxy that handles all sensitive information within the user device. We describe the system design and implementation, explain the custom cache coherence protocol to avoid data staleness and achieve Δ-atomicity, and we share field experiences from over a year of productive use in the e-commerce industry.

Caching strategies have been evaluated and compared in many studies, most often via simulation, but also in analytic methods. Knapsack solutions provide a general analytical approach for upper bounds on web caching performance. They assume objects of maximum (value/size) ratio being selected as cache content, with flexibility to define the caching value. Therefore the popularity, cost, size, time-to-live restrictions etc. per object can be included an overall caching goal, e.g., for reducing delay and/or transport path length in content delivery. The independent request model (IRM) leads to basic knapsack bounds for static optimum cache content. We show that a 2-dimensional (2D-)knapsack solution covers arbitrary request pattern, which selects dynamically changing content yielding maximum caching value for any predefined request sequence. Moreover, Belady's optimum strategy for clairvoyant caching is identified as a special case of our 2D-knapsack solution when all objects are unique. We also summarize a comprehensive picture of the demands and efficiency criteria for web caching, including updating speed and overheads. Our evaluations confirm significant performance gaps from LRU to advanced GreedyDual and score-based web caching methods and to the knapsack bounds.

Binary similarity has been widely used in function recognition and vulnerability detection. How to define a proper similarity is the key element in implementing a fast detection method. We proposed a scalable method to detect binary vulnerabilities based on similarity. Procedures lifted from binaries are divided into several comparable strands by data dependency, and those strands are transformed into a normalized form by our tool named VulneraBin, so that similarity can be determined between two procedures through a hash value comparison. The low computational complexity allows semantically equivalent code to be identified in binaries compiled from million lines of source code in a fast and accurate way.

Antisparse coding aims at spreading the information uniformly over representation coefficients and can be expressed as the solution of an ℓ∞-norm regularized problem. In this paper, we propose a new methodology, coined "safe squeezing", accelerating the computation of antisparse representations. The idea consists in identifying saturated entries of the solution via simple tests and compacting their contribution to achieve some form of dimensionality reduction. Numerical experiments show that the proposed approach leads to significant computational gain.

Automated techniques for rigorous floating-point round-off error analysis are a prerequisite to placing important activities in HPC such as precision allocation, verification, and code optimization on a formal footing. Yet existing techniques cannot provide tight bounds for expressions beyond a few dozen operators-barely enough for HPC. In this work, we offer an approach embedded in a new tool called SATIHE that scales error analysis by four orders of magnitude compared to today's best-of-class tools. We explain how three key ideas underlying SATIHE helps it attain such scale: path strength reduction, bound optimization, and abstraction. SATIHE provides tight bounds and rigorous guarantees on significantly larger expressions with well over a hundred thousand operators, covering important examples including FFT, matrix multiplication, and PDE stencils.

This paper presents a novel method, combining new formulations and sampling, to improve the scalability of sum-of-squares (SOS) programming-based system verification. Region-of-attraction approximation problems are considered for polynomial, polynomial with generalized Lur'e uncertainty, and rational trigonometric multi-rigid-body systems. Our method starts by identifying that Lagrange multipliers, traditionally heavily used for S-procedures, are a major culprit of creating bloated SOS programs. In light of this, we exploit inherent system properties-continuity, convexity, and implicit algebraic structure-and reformulate the problems as quotient-ring SOS programs, thereby eliminating all the multipliers. These new programs are smaller, sparser, less constrained, yet less conservative. Their computation is further improved by leveraging a recent result on sampling algebraic varieties. Remarkably, solution correctness is guaranteed with just a finite (in practice, very small) number of samples. Altogether, the proposed method can verify systems well beyond the reach of existing SOS-based approaches (32 states); on smaller problems where a baseline is available, it computes tighter solution 2-3 orders of magnitude faster.

This work considers the trade-off between security and performance when revealing partial information about encrypted data computed on. The focus of our work is on information revealed through control flow side-channels when executing programs on encrypted data. We use quantitative information flow to measure security, running time to measure performance and program transformation techniques to alter the trade-off between the two. Combined with information flow policies, we perform a policy-aware security and performance trade-off (PASAPTO) analysis. We formalize the problem of PASAPTO analysis as an optimization problem, prove the NP-hardness of the corresponding decision problem and present two algorithms solving it heuristically. We implemented our algorithms and combined them with the Dataflow Authentication (DFAuth) approach for outsourcing sensitive computations. Our DFAuth Trade-off Analyzer (DFATA) takes Java Bytecode operating on plaintext data and an associated information flow policy as input. It outputs semantically equivalent program variants operating on encrypted data which are policy-compliant and approximately Pareto-optimal with respect to leakage and performance. We evaluated DFATA in a commercial cloud environment using Java programs, e.g., a decision tree program performing machine learning on medical data. The decision tree variant with the worst performance is 357% slower than the fastest variant. Leakage varies between 0% and 17% of the input.

The problem of optimizing the security policy for the composite information system is formulated. Subject-object model for information system is used. Combining different types of security policies is formalized. The target function for the optimization task is recorded. The optimization problem for combining two discretionary security policies is solved. The case of combining two mandatory security policies is studied. The main problems of optimization the composite security policy are formulated. +50 CHMBOJIOB‼!

Packet classification is used to determine the behavior of incoming packets to network devices. Because it is achieved using a linear search on a classification rule list, a larger number of rules leads to a longer communication latency. To decrease this latency, the problem is generalized as Optimal Rule Ordering (ORO), which aims to identify the order of rules that minimizes the classification latency caused by packet classification while preserving the classification policy. Because ORO is known to be NP-complete by Hamed and Al-Shaer [Dynamic rule-ordering optimization for high-speed firewall filtering, ASIACCS (2006) 332-342], various heuristics for ORO have been proposed. Sub-graph merging (SGM) by Tapdiya and Fulp [Towards optimal firewall rule ordering utilizing directed acyclical graphs, ICCCN (2009) 1-6] is the state of the art heuristic algorithm for ORO. In this paper, we propose a novel heuristic method for ORO. Although most heuristics try to recursively determine the maximum-weight rule and move it as far as possible to an upper position, our algorithm pairs rules that cause policy violations until there are no such rules to simply sort the rules by these weights. Our algorithm markedly decreases the classification latency and reordering time compared with SGM in experiments. The sets consisting of thousands of rules that require one or more hours for reordering by SGM can be reordered by the proposed method within one minute.