Biblio

The use of multi-terminal HVDC to integrate wind power coming from the North Sea opens de door for a new transmission system model, the DC-Independent System Operator (DC-ISO). DC-ISO will face highly stressed and varying conditions that requires new risk assessment tools to ensure security of supply. This paper proposes a novel risk-based static security assessment methodology named risk-based DC security assessment (RB-DCSA). It combines a probabilistic approach to include uncertainties and a fuzzy inference system to quantify the systemic and individual component risk associated with operational scenarios considering uncertainties. The proposed methodology is illustrated using a multi-terminal HVDC system where the variability of wind speed at the offshore wind is included.

Digital Forensics is an area of Forensics Science that uses the application of scientific method toward crime investigation. The thwarting of forensic evidence is known as anti-forensics, the aim of which is ambiguous in the sense that it could be bad or good. The aim of this project is to simulate digital crimes scenario and carry out forensic and anti-forensic analysis to enhance security. This project uses several forensics and anti-forensic tools and techniques to carry out this work. The data analyzed were gotten from result of the simulation. The results reveal that although it might be difficult to investigate digital crime but with the help of sophisticated forensic tools/anti-forensics tools it can be accomplished.

The smart grid aims to improve the efficiency, reliability and safety of the electric system via modern communication system, it's necessary to utilize cloud computing to process and store the data. In fact, it's a promising paradigm to integrate smart grid into cloud computing. However, access to cloud computing system also brings data security issues. This paper focuses on the protection of user privacy in smart meter system based on data combination privacy and trusted third party. The paper demonstrates the security issues for smart grid communication system and cloud computing respectively, and illustrates the security issues for the integration. And we introduce data chunk storage and chunk relationship confusion to protect user privacy. We also propose a chunk information list system for inserting and searching data.

Pervasive Computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work we review these general issues and propose a Pervasive Computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some Artificial Intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones.

Nowadays, a typical household owns multiple digital devices that can be connected to the Internet. Advertising companies always want to seamlessly reach consumers behind devices instead of the device itself. However, the identity of consumers becomes fragmented as they switch from one device to another. A naive attempt is to use deterministic features such as user name, telephone number and email address. However consumers might refrain from giving away their personal information because of privacy and security reasons. The challenge in ICDM2015 contest is to develop an accurate probabilistic model for predicting cross-device consumer identity without using the deterministic user information. In this paper we present an accurate and scalable cross-device solution using an ensemble of Gradient Boosting Decision Trees (GBDT) and Random Forest. Our final solution ranks 9th both on the public and private LB with F0.5 score of 0.855.

Food safety policies have aim to promote and develop feeding and nutrition in society. This paper presents a system dynamics model that studies the dynamic behavior between transport infrastructure and the food supply chain in the city of Bogotá. The results show that an adequate transport infrastructure is more effective to improve the service to the customer in the food supply chain. The system dynamics model allows analyze the behavior of transport infrastructure and supply chains of fruits and vegetables, groceries, meat and dairy. The study has gone some way towards enhancing our understanding of food security impact, food supply chain and transport infrastructure.

Segmentation of land and water regions is necessary in many applications involving analysis of remote sensing imagery. Not only is manual segmentation of these regions prone to considerable subjective variability, but the large volume of imagery collected by modern platforms makes manual segmentation extremely tedious to perform, particularly in applications that require frequent re-measurement. This paper examines a robust, semi-automated approach that utilizes simple and efficient machine learning algorithms to perform supervised classification of multi-spectral image data into land and water regions. By combining the four wavelength bands widely available in imaging platforms such as IKONOS, QuickBird, and GeoEye-1 with basic texture metrics, high quality segmentation can be achieved. An efficient workflow was created by constructing a Graphical User Interface (GUI) to these machine learning algorithms.

Next generation cellular networks will provide users better experiences by densely deploying smaller cells, which results in more complicated interferences environment. In order to coordinate interference, power control for uplink is particularly challenging due to random locations of uplink transmitter and dense deployment. In this paper, we address the uplink fractional power control (FPC) optimization problem from network optimization perspective. The relations between FPC parameters and network KPIs (Key Performance Indicators) are investigated. Rather than considering any single KPI in conventional approaches, multi-KPI optimization problem is formulated and solved. By relaxing the discrete optimization problem to a continuous one, the gradients of multiple KPIs with respect to FPC parameters are derived. The gradient enables efficiently searching for optimized FPC parameters which is particularly desirable for dense deployment of large number of cells. Simulation results show that the proposed scheme greatly outperforms the traditional one, in terms of network mean load, call drop & block ratio, and convergence speed.

The ExFAT file system is for large capacity flash memory medium. On the base of analyzing the characteristics of ExFAT file system, this paper presents a model of electronic data recovery forensics and judicial Identification based on ExFAT. The proposed model aims at different destroyed situation of data recovery medium. It uses the file location algorithm, file character code algorithm, document fragment reassembly algorithm for accurate, efficient recovery of electronic data for forensics and judicial Identification. The model implements the digital multi-signature, process monitoring, media mirror and Hash authentication in the data recovery process to improve the acceptability, weight of evidence and Legal effect of the electronic data in the lawsuit. The experimental results show that the model has good work efficiency based on accuracy.

In this article, we review previous work on biometric security under a recent framework proposed in the field of adversarial machine learning. This allows us to highlight novel insights on the security of biometric systems when operating in the presence of intelligent and adaptive attackers that manipulate data to compromise normal system operation. We show how this framework enables the categorization of known and novel vulnerabilities of biometric recognition systems, along with the corresponding attacks, countermeasures, and defense mechanisms. We report two application examples, respectively showing how to fabricate a more effective face spoofing attack, and how to counter an attack that exploits an unknown vulnerability of an adaptive face-recognition system to compromise its face templates.

Cyber-physical systems (CPSs), due to their direct influence on the physical world, have to meet extended security and dependability requirements. This is particularly true for CPS that operate in close proximity to humans or that control resources that, when tampered with, put all our lives at stake. In this paper, we review the challenges and some early solutions that arise at the architectural and operating-system level when we require cyber-physical systems and CPS infrastructure to withstand advanced and persistent threats. We found that although some of the challenges we identified are already matched by rudimentary solutions, further research is required to ensure sustainable and dependable operation of physically exposed CPS infrastructure and, more importantly, to guarantee graceful degradation in case of malfunction or attack.

This paper proposes a fast and robust procedure for sensing and reconstruction of sparse or compressible magnetic resonance images based on the compressive sampling theory. The algorithm starts with incoherent undersampling of the k-space data of the image using a random matrix. The undersampled data is sparsified using Haar transformation. The Haar transform coefficients of the k-space data are then reconstructed using the orthogonal matching Pursuit algorithm. The reconstructed coefficients are inverse transformed into k-space data and then into the image in spatial domain. Finally, a median filter is used to suppress the recovery noise artifacts. Experimental results show that the proposed procedure greatly reduces the image data acquisition time without significantly reducing the image quality. The results also show that the error in the reconstructed image is reduced by median filtering.

Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems. However, the suitability of clustering algorithms for security-sensitive settings has been recently questioned by showing that they can be significantly compromised if an attacker can exercise some control over the input data. In this paper, we revisit this problem by focusing on behavioral malware clustering approaches, and investigate whether and to what extent an attacker may be able to subvert these approaches through a careful injection of samples with poisoning behavior. To this end, we present a case study on Malheur, an open-source tool for behavioral malware clustering. Our experiments not only demonstrate that this tool is vulnerable to poisoning attacks, but also that it can be significantly compromised even if the attacker can only inject a very small percentage of attacks into the input data. As a remedy, we discuss possible countermeasures and highlight the need for more secure clustering algorithms.

Abstract—Actor programming languages provide the kind of inherent parallelism that is needed for building applications in the mobile cloud. This is because the Actor model provides encapsulation (isolation of local state), fair scheduling, location transparency, and locality of reference. These properties facilitate building secure, scalable concurrent systems. Not surprisingly, very large-scale applications such as Facebook chat service and Twitter have been written in actor languages. The paper introduces the basics of the actor model and gives a high-level overview of the problem of coordination in actor systems. It then describes several novel methods for reasoning about concurrent systems that are both effective and scalable.

Security features are often hardwired into software applications, making it difficult to adapt security responses to reflect changes in runtime context and new attacks. In prior work, we proposed the idea of architecture-based self-protection as a way of separating adaptation logic from application logic and providing a global perspective for reasoning about security adaptations in the context of other business goals. In this paper, we present an approach, based on this idea, for combating denial-of-service (DoS) attacks. Our approach allows DoS-related tactics to be composed into more sophisticated mitigation strategies that encapsulate possible responses to a security problem. Then, utility-based reasoning can be used to consider different business contexts and qualities. We describe how this approach forms the underpinnings of a scientific approach to self-protection, allowing us to reason about how to make the best choice of mitigation at runtime. Moreover, we also show how formal analysis can be used to determine whether the mitigations cover the range of conditions the system is likely to encounter, and the effect of mitigations on other quality attributes of the system. We evaluate the approach using the Rainbow self-adaptive framework and show how Rainbow chooses DoS mitigation tactics that are sensitive to different business contexts.

Cloud computing has emerged as an increasingly popular means of delivering IT-enabled business services and a potential technology resource choice for many private and government organizations in today's rapidly changing computing environment. Consequently, as cloud computing technology, functionality and usability expands unique security vulnerabilities and treats requiring timely attention arise continuously. The primary challenge being providing continuous service availability. This paper will address cloud security vulnerability issues, the threats propagated by a distributed denial of service (DDOS) attack on cloud computing infrastructure and also discuss the means and techniques that could detect and prevent the attacks.

We provide an exact solution to two performance problems—one of disturbance attenuation and one of windowed variance minimization—subject to exponential stability. Considered are switched systems, whose parameters come from a finite set and switch according to a language such as that specified by an automaton. The controllers are path-dependent, having finite memory of past plant parameters and finite foreknowledge of future parameters. Exact, convex synthesis conditions for each performance problem are expressed in terms of nested linear matrix inequalities. The resulting semidefinite programming problem may be solved offline to arrive at a suitable controller. A notion of path-by-path performance is introduced for each performance problem, leading to improved system performance. Non-regular switching languages are considered and the results are extended to these languages. Two simple, physically motivated examples are given to demonstrate the application of these results.

Individuals sharing information can improve the cost or performance of a distributed control system. But, sharing may also violate privacy. We develop a general framework for studying the cost of differential privacy in systems where a collection of agents, with coupled dynamics, communicate for sensing their shared environment while pursuing individ- ual preferences. First, we propose a communication strategy that relies on adding carefully chosen random noise to agent states and show that it preserves differential privacy. Of course, the higher the standard deviation of the noise, the higher the cost of privacy. For linear distributed control systems with quadratic cost functions, the standard deviation becomes independent of the number agents and it decays with the maximum eigenvalue of the dynamics matrix. Furthermore, for stable dynamics, the noise to be added is independent of the number of agents as well as the time horizon up to which privacy is desired.

This paper documents and discusses the design of a low-cost Flapping-Wing Micro Air Vehicle (FW-MAV) designed to be easy to fabricate using readily available materials and equipment. Basic theory of operation as well as the rationale underlying various design decisions will be provided. Using this paper, it should be possible for readers to construct their own devices quickly and at little expense.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.

The concept of differential  privacy stems from the study of private query of datasets.  In  this work, we apply this concept  to metric spaces  to study a  mechanism  that randomizes a deterministic query by adding  mean-zero  noise to keep differential  privacy.