Biblio

Within few years, Cloud computing has emerged as the most promising IT business model. Thanks to its various technical and financial advantages, Cloud computing continues to convince every day new users coming from scientific and industrial sectors. To satisfy the various users' requirements, Cloud providers must maximize the performance of their IT resources to ensure the best service at the lowest cost. The performance optimization efforts in the Cloud can be achieved at different levels and aspects. In the present paper, we propose to introduce a fuzzy logic process in scheduling strategy for public Cloud in order to improve the response time, processing time and total cost. In fact, fuzzy logic has proven his ability to solve the problem of optimization in several fields such as data mining, image processing, networking and much more.

This paper contributes a systematic research approach as well as findings of an empirical study conducted to investigate the effect of virtual agents on task performance and player experience in digital games. As virtual agents are supposed to evoke social effects similar to real humans under certain conditions, the basic social phenomenon social facilitation is examined in a testbed game that was specifically developed to enable systematical variation of single impact factors of social facilitation. Independent variables were the presence of a virtual agent (present vs. not present) and the output device (ordinary monitor vs. head-mounted display). Results indicate social inhibition effects, but only for players using a head-mounted display. Additional potential impact factors and future research directions are discussed.

With cyber-physical systems opening to the outside world, security can no longer be considered a secondary issue. One of the key aspects in security of cyber-phyiscal systems is to deal with intrusions. In this paper, we highlight the several unique properties of control applications in cyber-physical systems. Using these unique properties, we propose a systematic intrusion-damage assessment and mitigation mechanism for the class of observable and controllable attacks. On the one hand, in cyber-physical systems, the plants follow certain laws of physics and this can be utilized to address the intrusion-damage assessment problem. That is, the states of the controlled plant should follow those expected according to the physics of the system and any major discrepancy is potentially an indication of intrusion. Here, we use a machine learning algorithm to capture the normal behavior of the system according to its dynamics. On the other hand, the control performance strongly depends on the amount of allocated resources and this can be used to address the intrusion-damage mitigation problem. That is, the intrusion-damage mitigation is based on the idea of allocating more resources to the control application under attack. This is done using a feedback-based approach including a convex optimization.

In this paper we propose a protocol that allows end-users in a decentralized setup (without requiring any trusted third party) to protect data shipped to remote servers using two factors - knowledge (passwords) and possession (a time based one time password generation for authentication) that is portable. The protocol also supports revocation and recreation of a new possession factor if the older possession factor is compromised, provided the legitimate owner still has a copy of the possession factor. Furthermore, akin to some other recent works, our approach naturally protects the outsourced data from the storage servers themselves, by application of encryption and dispersal of information across multiple servers. We also extend the basic protocol to demonstrate how collaboration can be supported even while the stored content is encrypted, and where each collaborator is still restrained from accessing the data through a multi-factor access mechanism. Such techniques achieving layered security is crucial to (opportunistically) harness storage resources from untrusted entities.

Nowadays, sentiment analysis methods become more and more popular especially with the proliferation of social media platform users number. In the same context, this paper presents a sentiment analysis approach which can faithfully translate the sentimental orientation of Arabic Twitter posts, based on a novel data representation and machine learning techniques. The proposed approach applied a wide range of features: lexical, surface-form, syntactic, etc. We also made use of lexicon features inferred from two Arabic sentiment words lexicons. To build our supervised sentiment analysis system, we use several standard classification methods (Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees, Random Forest) known by their effectiveness over such classification issues. In our study, Support Vector Machines classifier outperforms other supervised algorithms in Arabic Twitter sentiment analysis. Via an ablation experiments, we show the positive impact of lexicon based features on providing higher prediction performance.

In today's enterprise networks, there are many ways for a determined attacker to obtain a foothold, bypass current protection technologies, and attack the intended target. Over several years we have developed the Self-shielding Dynamic Network Architecture (SDNA) technology, which prevents an attacker from targeting, entering, or spreading through an enterprise network by adding dynamics that present a changing view of the network over space and time. SDNA was developed with the support of government sponsored research and development and corporate internal resources. The SDNA technology was purchased by Cryptonite, LLC in 2015 and has been developed into a robust product offering called Cryptonite NXT. In this paper, we describe the journey and lessons learned along the course of feasibility demonstration, technology development, security testing, productization, and deployment in a production network.

Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as well as its original unpainted version are required. This limits their domain of applicability. We present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style. We impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures. We generate a solution through Convolutional Neural Networks and we present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shower-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art.

Phishing is one of the most dangerous information security threats present in the world today, with losses toping 5.9 billion dollars in 2013. Evolving from the original concept of phishing, spear phishing also attempts to scam individuals online, however it uses personalized mail to yield a far higher success rate. This paper suggests an increased threat of spear phishing success due to the presence of social media. Assessing this new threat is important not only to the individuals, but also to companies whose employees may specifically be targeted through their social media accounts. The paper presents the design and implementation of an architecture to determine phishing susceptibility of a user through their social media accounts, and methods to reduce the threat. Preliminary testing shows that social media provides a publicly accessible resource to assess targeted individuals for phishing attacks through their accounts.

Using stolen or weak credentials to bypass authentication is one of the top 10 network threats, as shown in recent studies. Disguising as legitimate users, attackers use stealthy techniques such as rootkits and covert channels to gain persistent access to a target system. However, such attacks are often detected after the system misuse stage, i.e., the attackers have already executed attack payloads such as: i) stealing secrets, ii) tampering with system services, and ii) disrupting the availability of production services.

In this talk, we analyze a real-world credential stealing attack observed at the National Center for Supercomputing Applications. We show the disadvantages of traditional detection techniques such as signature-based and anomaly-based detection for such attacks. Our approach is a complement to existing detection techniques. We investigate the use of Probabilistic Graphical Model, specifically Factor Graphs, to integrate security logs from multiple sources for a more accurate detection. Finally, we propose a security testbed architecture to: i) simulate variants of known attacks that may happen in the future, ii) replay such attack variants in an isolated environment, and iii) collect and share security logs of such replays for the security research community.

Processing smart grid data for analytics purposes brings about a series of privacy-related risks. In order to allow for the most suitable mitigation strategies, reasonable privacy risks need to be addressed by taking into consideration the perspective of each smart grid stakeholder separately. In this context, we use the notion of privacy concerns to reflect potential privacy risks from the perspective of different smart grid stakeholders. Privacy concerns help to derive privacy goals, which we represent using the goals structuring notation. Thus represented goals can more comprehensibly be addressed through technical and non-technical strategies and solutions. The thread of argumentation - from concerns to goals to strategies and solutions - is presented in form of a privacy case, which is analogous to the safety case used in the automotive domain. We provide an exemplar privacy case for the smart grid developed as part of the Aspern Smart City Research project.

Web applications are a frequent target of successful attacks. In most web frameworks, the damage is amplified by the fact that application code is responsible for security enforcement. In this paper, we design and evaluate Radiatus, a shared-nothing web framework where application-specific computation and storage on the server is contained within a sandbox with the privileges of the end-user. By strongly isolating users, user data and service availability can be protected from application vulnerabilities. To make Radiatus practical at the scale of modern web applications, we introduce a distributed capabilities system to allow fine-grained secure resource sharing across the many distributed services that compose an application. We analyze the strengths and weaknesses of a shared-nothing web architecture, which protects applications from a large class of vulnerabilities, but adds an overhead of 60.7% per server and requires an additional 31MB of memory per active user. We demonstrate that the system can scale to 20K operations per second on a 500-node AWS cluster.

Detection of previously unknown attacks and malicious messages is a challenging problem faced by modern network intrusion detection systems. Anomaly-based solutions, despite being able to detect unknown attacks, have not been used often in practice due to their high false positive rate, and because they provide little actionable information to the security officer in case of an alert. In this paper we focus on intrusion detection in industrial control systems networks and we propose an innovative, practical and semantics-aware framework for anomaly detection. The network communication model and alerts generated by our framework are userunderstandable, making them much easier to manage. At the same time the framework exhibits an excellent tradeoff between detection rate and false positive rate, which we show by comparing it with two existing payload-based anomaly detection methods on several ICS datasets.

Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

Internet Service Providers (ISPs) use the Border Gateway Protocol (BGP) to announce and exchange routes for de- livering packets through the internet. ISPs must carefully configure their BGP routers to ensure traffic is routed reli- ably and securely. Correctly configuring BGP routers has proven challenging in practice, and misconfiguration has led to worldwide outages and traffic hijacks. This paper presents Bagpipe, a system that enables ISPs to declaratively express BGP policies and that automatically verifies that router configurations implement such policies. The novel initial network reduction soundly reduces policy verification to a search for counterexamples in a finite space. An SMT-based symbolic execution engine performs this search efficiently. Bagpipe reduces the size of its search space using predicate abstraction and parallelizes its search using symbolic variable hoisting. Bagpipe's policy specification language is expressive: we expressed policies inferred from real AS configurations, policies from the literature, and policies for 10 Juniper TechLibrary configuration scenarios. Bagpipe is efficient: we ran it on three ASes with a total of over 240,000 lines of Cisco and Juniper BGP configuration. Bagpipe is effective: it revealed 19 policy violations without issuing any false positives.

Software Defined Networking (SDN) is the new promise towards an easily configured and remotely controlled network. Based on Centralized control, SDN technology has proved its positive impact on the world of network communications from different aspects. Security in SDN, as in traditional networks, is an essential feature that every communication system should possess. In this paper, we propose an SDN security design approach, which strikes a good balance between network performance and security features. We show how such an approach can be used to prevent DDoS attacks targeting either the controller or the different hosts in the network, and how to trace back the source of the attack. The solution lies in introducing a third plane, the security plane, in addition to the data plane, which is responsible for forwarding data packets between SDN switches, and parallel to the control plane, which is responsible for rule and data exchange between the switches and the SDN controller. The security plane is designed to exchange security-related data between a third party agent on the switch and a third party software module alongside the controller. Our evaluation shows the capability of the proposed system to enforce different levels of real-time user-defined security with low overhead and minimal configuration.

Cyber-Physical Systems (CPSs) are often tested at different test levels following "X-in-the-Loop" configurations: Model-, Software- and Hardware-in-the-loop (MiL, SiL and HiL). While MiL and SiL test levels aim at testing functional requirements at the system level, the HiL test level tests functional as well as non-functional requirements by performing a real-time simulation. As testing CPS product line configurations is costly due to the fact that there are many variants to test, test cases are long, the physical layer has to be simulated and co-simulation is often necessary. It is therefore extremely important to select the appropriate test cases that cover the objectives of each level in an allowable amount of time. We propose an efficient test case selection approach adapted to the "X-in-the-Loop" test levels. Search algorithms are employed to reduce the amount of time required to test configurations of CPS product lines while achieving the test objectives of each level. We empirically evaluate three commonly-used search algorithms, i.e., Genetic Algorithm (GA), Alternating Variable Method (AVM) and Greedy (Random Search (RS) is used as a baseline) by employing two case studies with the aim of integrating the best algorithm into our approach. Results suggest that as compared with RS, our approach can reduce the costs of testing CPS product line configurations by approximately 80% while improving the overall test quality.

When a group of individuals and organizations wish to compute a stable matching–-for example, when medical students are matched to medical residency programs–-they often outsource the computation to a trusted arbiter in order to preserve the privacy of participants' preferences. Secure multi-party computation offers the possibility of private matching processes that do not rely on any common trusted third party. However, stable matching algorithms have previously been considered infeasible for execution in a secure multi-party context on non-trivial inputs because they are computationally intensive and involve complex data-dependent memory access patterns. We adapt the classic Gale-Shapley algorithm for use in such a context, and show experimentally that our modifications yield a lower asymptotic complexity and more than an order of magnitude in practical cost improvement over previous techniques. Our main improvements stem from designing new oblivious data structures that exploit the properties of the matching algorithms. We apply a similar strategy to scale the Roth-Peranson instability chaining algorithm, currently in use by the National Resident Matching Program. The resulting protocol is efficient enough to be useful at the scale required for matching medical residents nationwide, taking just over 18 hours to complete an execution simulating the 2016 national resident match with more than 35,000 participants and 30,000 residency slots.

Processes to automate the selection of appropriate algorithms for various matrix computations are described. In particular, processes to check for, and certify, various matrix properties of black-box matrices are presented. These include sparsity patterns and structural properties that allow "superfast" algorithms to be used in place of black-box algorithms. Matrix properties that hold generically, and allow the use of matrix preconditioning to be reduced or eliminated, can also be checked for and certified –- notably including in the small-field case, where this presently has the greatest impact on the efficiency of the computation.

In this paper we describe and share with the research community, a significant smartphone dataset obtained from an ongoing long-term data collection experiment. The dataset currently contains 10 billion data records from 30 users collected over a period of 1.6 years and an additional 20 users for 6 months (totaling 50 active users currently participating in the experiment). The experiment involves two smartphone agents: SherLock and Moriarty. SherLock collects a wide variety of software and sensor data at a high sample rate. Moriarty perpetrates various attacks on the user and logs its activities, thus providing labels for the SherLock dataset. The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. To demonstrate possible uses of the dataset, we perform a basic malware analysis and evaluate a method of continuous user authentication.

The explosion in Internet-connected household devices, such as light-bulbs, smoke-alarms, power-switches, and webcams, is creating new vectors for attacking "smart-homes" at an unprecedented scale. Common perception is that smart-home IoT devices are protected from Internet attacks by the perimeter security offered by home routers. In this paper we demonstrate how an attacker can infiltrate the home network via a doctored smart-phone app. Unbeknownst to the user, this app scouts for vulnerable IoT devices within the home, reports them to an external entity, and modifies the firewall to allow the external entity to directly attack the IoT device. The ability to infiltrate smart-homes via doctored smart-phone apps demonstrates that home routers are poor protection against Internet attacks and highlights the need for increased security for IoT devices.

Recommender systems have become quite popular recently. However, such systems are vulnerable to several types of attacks that target user ratings. One such attack is the Sybil attack where an entity masquerades as several identities with the intention of diverting user ratings. In this work, we propose evolutionary game theory as a possible solution to the Sybil attack in recommender systems. After modeling the attack, we use replicator dynamics to solve for evolutionary stable strategies. Our results show that under certain conditions that are easily achievable by a system administrator, the probability of an attack strategy drops to zero implying degraded fitness for Sybil nodes that eventually die out.