Biblio

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.

Monitoring is an important issue in cloud environments because it assures that acquired cloud slices attend the user's expectations. However, these environments are multitenant and dynamic, requiring automation techniques to offload cloud administrators. In a previous work, we proposed FlexACMS: a framework to automate monitoring configuration related to cloud slices using multiple monitoring solutions. In this work, we enhanced FlexACMS to allow dynamic and automatic attribution of monitoring configuration tasks to servers without administrator intervention, which was not available in previous version. FlexACMS also considers the monitoring server load when attributing configuration tasks, which allows load balancing between monitoring servers. The evaluation showed that enhancements reduced FlexACMS response time up to 60% in comparison to previous version. The scalability evaluation of enhanced version demonstrated the feasibility of our approach in large scale cloud environments.
 

Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability of the control system against random hardware and software failures. However, the inability of information security techniques to protect against all intrusions means that the control system must be resilient to various signal attacks for which new analysis methods need to be developed. In this paper, sensor signal attacks are analyzed for observer-based controlled systems. The threat surface for sensor signal attacks is subdivided into denial of service, finite energy, and bounded attacks. In particular, the error signals between states of attack free systems and systems subject to these attacks are quantified. Optimal sensor and actuator signal attacks for the finite and infinite horizon linear quadratic (LQ) control in terms of maximizing the corresponding cost functions are computed. The closed-loop systems under optimal signal attacks are provided. Finally, an illustrative numerical example using a power generation network is provided together with distributed LQ controllers.

Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability of the control system against random hardware and software failures. However, the inability of information security techniques to protect against all intrusions means that the control system must be resilient to various signal attacks for which new analysis methods need to be developed. In this paper, sensor signal attacks are analyzed for observer-based controlled systems. The threat surface for sensor signal attacks is subdivided into denial of service, finite energy, and bounded attacks. In particular, the error signals between states of attack free systems and systems subject to these attacks are quantified. Optimal sensor and actuator signal attacks for the finite and infinite horizon linear quadratic (LQ) control in terms of maximizing the corresponding cost functions are computed. The closed-loop systems under optimal signal attacks are provided. Finally, an illustrative numerical example using a power generation network is provided together with distributed LQ controllers.

This article addresses trust in computer systems as a social phenomenon, which depends on the type of relationship that is established through the computer, or with other individuals. It starts by theoretically contextualizing trust, and then situates trust in the field of computer science. Then, describes the proposed model, which builds on what one perceives to be trustworthy and is influenced by a number of factors such as the history of participation and user's perceptions. It ends by situating the proposed model as a key contribution for leveraging trustful interactions and ends by proposing it used to serve as a complement to foster user's trust needs in what concerns Human-computer Iteration or Computermediated Interactions.

The United States, including the Department of Defense, relies heavily on information systems and networking technologies to efficiently conduct a wide variety of missions across the globe. With the ever-increasing rate of cyber attacks, this dependency places the nation at risk of a loss of confidentiality, integrity, and availability of its critical information resources; degrading its ability to complete the mission. In this paper, we introduce the operational data classes for establishing situational awareness in cyberspace. A system effectively using our key information components will be able to provide the nation's leadership timely and accurate information to gain an understanding of the operational cyber environment to enable strategic, operational, and tactical decision-making. In doing so, we present, define and provide examples of our key classes of operational data for cyber situational awareness and present a hypothetical case study demonstrating how they must be consolidated to provide a clear and relevant picture to a commander. In addition, current organizational and technical challenges are discussed, and areas for future research are addressed.
 

The future Internet has been a hot topic during the past decade and many approaches towards this future Internet, ranging from incremental evolution to complete clean slate ones, have been proposed. One of the proposition, LISP, advocates for the separation of the identifier and the locator roles of IP addresses to reduce BGP churn and BGP table size. Up to now, however, most studies concerning LISP have been theoretical and, in fact, little is known about the actual LISP deployment performance. In this paper, we fill this gap through measurement campaigns carried out on the LISP Beta Network. More precisely, we evaluate the performance of the two key components of the infrastructure: the control plane (i.e., the mapping system) and the interworking mechanism (i.e., communication between LISP and non-LISP sites). Our measurements highlight that performance offered by the LISP interworking infrastructure is strongly dependent on BGP routing policies. If we exclude misconfigured nodes, the mapping system typically provides reliable performance and relatively low median mapping resolution delays. Although the bias is not very important, control plane performance favors USA sites as a result of its larger LISP user base but also because European infrastructure appears to be less reliable.
 

This paper proposes a security architecture for an IoT transparent middleware. Focused on bringing real life objects to the virtual realm, the proposed architecture is deployable and comprises protection measures based on existent technologies for security such as AES, TLS and oAuth. This way, privacy, authenticity, integrity and confidentiality on data exchange services are integrated to provide security for generated smart objects and for involved users and services in a reliable and deployable manner.

Moving Target Defense (MTD) changes the attack surface of a system that confuses intruders to thwart attacks. Various MTD techniques are developed to enhance the security of a networked system, but the effectiveness of these techniques is not well assessed. Security models (e.g., Attack Graphs (AGs)) provide formal methods of assessing security, but modeling the MTD techniques in security models has not been studied. In this paper, we incorporate the MTD techniques in security modeling and analysis using a scalable security model, namely Hierarchical Attack Representation Models (HARMs), to assess the effectiveness of the MTD techniques. In addition, we use importance measures (IMs) for scalable security analysis and deploying the MTD techniques in an effective manner. The performance comparison between the HARM and the AG is given. Also, we compare the performance of using the IMs and the exhaustive search method in simulations.

Wireless channel reciprocity can be successfully exploited as a common source of randomness for the generation of a secret key by two legitimate users willing to achieve confidential communications over a public channel. This paper presents an analytical framework to investigate the theoretical limits of secret-key generation when wireless multi-dimensional Gaussian channels are used as source of randomness. The intrinsic secrecy content of wide-sense stationary wireless channels in frequency, time and spatial domains is derived through asymptotic analysis as the number of observations in a given domain tends to infinity. Some significant case studies are presented where single and multiple antenna eavesdroppers are considered. In the numerical results, the role of signal-to-noise ratio, spatial correlation, frequency and time selectivity is investigated.

The vast majority of today's critical infrastructure is supported by numerous feedback control loops and an attack on these control loops can have disastrous consequences. This is a major concern since modern control systems are becoming large and decentralized and thus more vulnerable to attacks. This paper is concerned with the estimation and control of linear systems when some of the sensors or actuators are corrupted by an attacker. We give a new simple characterization of the maximum number of attacks that can be detected and corrected as a function of the pair (A,C) of the system and we show in particular that it is impossible to accurately reconstruct the state of a system if more than half the sensors are attacked. In addition, we show how the design of a secure local control loop can improve the resilience of the system. When the number of attacks is smaller than a threshold, we propose an efficient algorithm inspired from techniques in compressed sensing to estimate the state of the plant despite attacks. We give a theoretical characterization of the performance of this algorithm and we show on numerical simulations that the method is promising and allows to reconstruct the state accurately despite attacks. Finally, we consider the problem of designing output-feedback controllers that stabilize the system despite sensor attacks. We show that a principle of separation between estimation and control holds and that the design of resilient output feedback controllers can be reduced to the design of resilient state estimators.

The trend towards Cloud computing infrastructure has increased the need for new methods that allow data owners to share their data with others securely taking into account the needs of multiple stakeholders. The data owner should be able to share confidential data while delegating much of the burden of access control management to the Cloud and trusted enterprises. The lack of such methods to enhance privacy and security may hinder the growth of cloud computing. In particular, there is a growing need to better manage security keys of data shared in the Cloud. BYOD provides a first step to enabling secure and efficient key management, however, the data owner cannot guarantee that the data consumers device itself is secure. Furthermore, in current methods the data owner cannot revoke a particular data consumer or group efficiently. In this paper, we address these issues by incorporating a hardware-based Trusted Platform Module (TPM) mechanism called the Trusted Extension Device (TED) together with our security model and protocol to allow stronger privacy of data compared to software-based security protocols. We demonstrate the concept of using TED for stronger protection and management of cryptographic keys and how our secure data sharing protocol will allow a data owner (e.g, author) to securely store data via untrusted Cloud services. Our work prevents keys to be stolen by outsiders and/or dishonest authorised consumers, thus making it particularly attractive to be implemented in a real-world scenario.

Future wireless communications are made up of different wireless technologies. In such a scenario, cognitive and cooperative principles create a promising framework for the interaction of these systems. The opportunistic behavior of cognitive radio (CR) provides an efficient use of radio spectrum and makes wireless network setup easier. However more and more frequently, CR features are exploited by malicious attacks, e.g., denial-of-service (DoS). This paper introduces active radio frequency fingerprinting (RFF) with double application scenario. CRs could encapsulate common-control-channel (CCC) information in an existing channel using active RFF and avoiding any additional or dedicated link. On the other hand, a node inside a network could use the same technique to exchange a public key during the setup of secure communication. Results indicate how the active RFF aims to a valuable technique for cognitive radio manager (CRM) framework facilitating data exchange between CRs without any dedicated channel or additional radio resource.

Unstructured data mining has become topical recently due to the availability of high-dimensional and voluminous digital content (known as "Big Data") across the enterprise spectrum. The Relational Database Management Systems (RDBMS) have been employed over the past decades for content storage and management, but, the ever-growing heterogeneity in today's data calls for a new storage approach. Thus, the NoSQL database has emerged as the preferred storage facility nowadays since the facility supports unstructured data storage. This creates the need to explore efficient data mining techniques from such NoSQL systems since the available tools and frameworks which are designed for RDBMS are often not directly applicable. In this paper, we focused on topics and terms mining, based on clustering, in document-based NoSQL. This is achieved by adapting the architectural design of an analytics-as-a-service framework and the proposal of the Viterbi algorithm to enhance the accuracy of the terms classification in the system. The results from the pilot testing of our work show higher accuracy in comparison to some previously proposed techniques such as the parallel search.

Face-to-face negotiations always benefit if the interacting individuals trust each other. But trust is also important in online interactions, even for humans interacting with a computational agent. In this article, the authors describe a behavioral experiment to determine whether, by volunteering information that it need not disclose, a software agent in a multi-issue negotiation can alleviate mistrust in human counterparts who differ in their propensities to mistrust others. Results indicated that when cynical, mistrusting humans negotiated with an agent that proactively communicated its issue priority and invited reciprocation, there were significantly more agreements and better utilities than when the agent didn't volunteer such information. Furthermore, when the agent volunteered its issue priority, the outcomes for mistrusting individuals were as good as those for trusting individuals, for whom the volunteering of issue priority conferred no advantage. These findings provide insights for designing more effective, socially intelligent agents in online negotiation settings.

The gradient-descent total least-squares (GD-TLS) algorithm is a stochastic-gradient adaptive filtering algorithm that compensates for error in both input and output data. We study the local convergence of the GD-TLS algoritlun and find bounds for its step-size that ensure its stability. We also analyze the steady-state performance of the GD-TLS algorithm and calculate its steady-state mean-square deviation. Our steady-state analysis is inspired by the energy-conservation-based approach to the performance analysis of adaptive filters. The results predicted by the analysis show good agreement with the simulation experiments.

DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.

Mutation analysis generates tests that distinguish variations, or mutants, of an artifact from the original. Mutation analysis is widely considered to be a powerful approach to testing, and hence is often used to evaluate other test criteria in terms of mutation score, which is the fraction of mutants that are killed by a test set. But mutation analysis is also known to provide large numbers of redundant mutants, and these mutants can inflate the mutation score. While mutation approaches broadly characterized as reduced mutation try to eliminate redundant mutants, the literature lacks a theoretical result that articulates just how many mutants are needed in any given situation. Hence, there is, at present, no way to characterize the contribution of, for example, a particular approach to reduced mutation with respect to any theoretical minimal set of mutants. This paper's contribution is to provide such a theoretical foundation for mutant set minimization. The central theoretical result of the paper shows how to minimize efficiently mutant sets with respect to a set of test cases. We evaluate our method with a widely-used benchmark.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

This paper presents on-going research to define the basic models and architecture patterns for federated access control in heterogeneous (multi-provider) multi-cloud and inter-cloud environment. The proposed research contributes to the further definition of Intercloud Federation Framework (ICFF) which is a part of the general Intercloud Architecture Framework (ICAF) proposed by authors in earlier works. ICFF attempts to address the interoperability and integration issues in provisioning on-demand multi-provider multi-domain heterogeneous cloud infrastructure services. The paper describes the major inter-cloud federation scenarios that in general involve two types of federations: customer-side federation that includes federation between cloud based services and customer campus or enterprise infrastructure, and provider-side federation that is created by a group of cloud providers to outsource or broker their resources when provisioning services to customers. The proposed federated access control model uses Federated Identity Management (FIDM) model that can be also supported by the trusted third party entities such as Cloud Service Broker (CSB) and/or trust broker to establish dynamic trust relations between entities without previously existing trust. The research analyses different federated identity management scenarios, defines the basic architecture patterns and the main components of the distributed federated multi-domain Authentication and Authorisation infrastructure.

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo- location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo- location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.