OpenRaiser/Intern-Atlas huggingface dataset:
On Perception and Reality in Wireless Air Traffic Communications Security
Venue: arXiv cs.CR 2016
Year: 2016

More than a dozen wireless technologies are used by air traffic communication systems during different flight phases. From a conceptual perspective, all of them are insecure as security was never part of their design. Recent contributions from academic and hacking communities have exploited this inherent vulnerability to demonstrate attacks on some of these technologies. However, not all of these contributions have resonated widely within aviation circles. At the same time, the security community lacks certain aviation domain knowledge, preventing aviation authorities from giving credence to their findings. In this paper, we aim to reconcile the view of the security community and the perspective of aviation professionals concerning the safety of air traffic communication technologies. To achieve this, we first provide a systematization of the applications of wireless technologies upon which civil aviation relies. Based on these applications, we comprehensively analyze vulnerabilities, attacks, and countermeasures. We categorize the existing research on countermeasures into approaches that are applicable in the short term and research of secure new technologies deployable in the long term. Since not all of the required aviation knowledge is codified in academic publications, we additionally examine existing aviation standards and survey 242 international aviation experts. Besides their domain knowledge, we also analyze the awareness of members of the aviation community concerning the security of wireless systems and collect their expert opinions on the potential impact of concrete attack scenarios using these technologies.
Range-based argumentation semantics as 2-valued models
Venue: arXiv cs.LO 2016
Year: 2016

Characterizations of semi-stable and stage extensions in terms of 2-valued logical models are presented. To this end, the so-called GL-supported and GL-stage models are defined. These two classes of logical models are logic programming counterparts of the notion of range which is an established concept in argumentation semantics.
Extending Finite Memory Determinacy to Multiplayer Games
Venue: arXiv cs.GT 2016
Year: 2016

We show that under some general conditions the finite memory determinacy of a class of two-player win/lose games played on finite graphs implies the existence of a Nash equilibrium built from finite memory strategies for the corresponding class of multi-player multi-outcome games. This generalizes a previous result by Brihaye, De Pril and Schewe. We provide a number of example that separate the various criteria we explore. Our proofs are generally constructive, that is, provide upper bounds for the memory required, as well as algorithms to compute the relevant Nash equilibria.
Dynamic Channel Allocation for Interference Mitigation in Relay-assisted Wireless Body Networks
Venue: arXiv cs.NI 2016
Year: 2016

We focus on interference mitigation and energy conservation within a single wireless body area network (WBAN). We adopt two-hop communication scheme supported by the the IEEE 802.15.6 standard (2012). In this paper, we propose a dynamic channel allocation scheme, namely DCAIM to mitigate node-level interference amongst the coexisting regions of a WBAN. At the time, the sensors are in the radius communication of a relay, they form a relay region (RG) coordinated by that relay using time division multiple access (TDMA). In the proposed scheme, each RG creates a table consisting of interfering sensors which it broadcasts to its neighboring sensors. This broadcast allows each pair of RGs to create an interference set (IS). Thus, the members of IS are assigned orthogonal sub-channels whereas other sonsors that do not belong to IS can transmit using the same time slots. Experimental results show that our proposal mitigates node-level interference and improves node and WBAN energy savings. These results are then compared to the results of other schemes. As a result, our scheme outperforms in all cases. Node-level signal to interference and noise ratio (SINR) improved by 11dB whilst, the energy consumption decreased significantly. We further present a probabilistic method and analytically show the outage probability can be effectively reduced to the minimal.
On the Practicality of Cryptographically Enforcing Dynamic Access Control Policies in the Cloud (Extended Version)
Venue: arXiv cs.CR 2016
Year: 2016

The ability to enforce robust and dynamic access controls on cloud-hosted data while simultaneously ensuring confidentiality with respect to the cloud itself is a clear goal for many users and organizations. To this end, there has been much cryptographic research proposing the use of (hierarchical) identity-based encryption, attribute-based encryption, predicate encryption, functional encryption, and related technologies to perform robust and private access control on untrusted cloud providers. However, the vast majority of this work studies static models in which the access control policies being enforced do not change over time. This is contrary to the needs of most practical applications, which leverage dynamic data and/or policies. In this paper, we show that the cryptographic enforcement of dynamic access controls on untrusted platforms incurs computational costs that are likely prohibitive in practice. Specifically, we develop lightweight constructions for enforcing role-based access controls (i.e., $\mathsf{RBAC}_0$) over cloud-hosted files using identity-based and traditional public-key cryptography. This is done under a threat model as close as possible to the one assumed in the cryptographic literature. We prove the correctness of these constructions, and leverage real-world $\mathsf{RBAC}$ datasets and recent techniques developed by the access control community to experimentally analyze, via simulation, their associated computational costs. This analysis shows that supporting revocation, file updates, and other state change functionality is likely to incur prohibitive overheads in even minimally-dynamic, realistic scenarios. We identify a number of bottlenecks in such systems, and fruitful areas for future work that will lead to more natural and efficient constructions for the cryptographic enforcement of dynamic access controls.
Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm
Venue: arXiv cs.AI 2016
Year: 2016

Route choice in multimodal networks shows a considerable variation between different individuals as well as the current situational context. Personalization of recommendation algorithms are already common in many areas, e.g., online retail. However, most online routing applications still provide shortest distance or shortest travel-time routes only, neglecting individual preferences as well as the current situation. Both aspects are of particular importance in a multimodal setting as attractivity of some transportation modes such as biking crucially depends on personal characteristics and exogenous factors like the weather. This paper introduces the FAVourite rOUte Recommendation (FAVOUR) approach to provide personalized, situation-aware route proposals based on three steps: first, at the initialization stage, the user provides limited information (home location, work place, mobility options, sociodemographics) used to select one out of a small number of initial profiles. Second, based on this information, a stated preference survey is designed in order to sharpen the profile. In this step a mass preference prior is used to encode the prior knowledge on preferences from the class identified in step one. And third, subsequently the profile is continuously updated during usage of the routing services. The last two steps use Bayesian learning techniques in order to incorporate information from all contributing individuals. The FAVOUR approach is presented in detail and tested on a small number of survey participants. The experimental results on this real-world dataset show that FAVOUR generates better-quality recommendations w.r.t. alternative learning algorithms from the literature. In particular the definition of the mass preference prior for initialization of step two is shown to provide better predictions than a number of alternatives from the literature.
Throughput and Coverage for a Mixed Full and Half Duplex Small Cell Network
Venue: arXiv cs.IT 2016
Year: 2016

Recent advances in self-interference cancellation enable radios to transmit and receive on the same frequency at the same time. Such a full duplex radio is being considered as a potential candidate for the next generation of wireless networks due to its ability to increase the spectral efficiency of wireless systems. In this paper, the performance of full duplex radio in small cellular systems is analyzed by assuming full duplex capable base stations and half duplex user equipment. However, using only full duplex base stations increases interference leading to outage. We therefore propose a mixed multi-cell system, composed of full duplex and half duplex cells. A stochastic geometry based model of the proposed mixed system is provided, which allows us to derive the outage and area spectral efficiency of such a system. The effect of full duplex cells on the performance of the mixed system is presented under different network parameter settings. We show that the fraction of cells that have full duplex base stations can be used as a design parameter by the network operator to target an optimal tradeoff between area spectral efficiency and outage in a mixed system.
A complexity dichotomy for poset constraint satisfaction
Venue: arXiv cs.CC 2016
Year: 2016

In this paper we determine the complexity of a broad class of problems that extends the temporal constraint satisfaction problems. To be more precise we study the problems Poset-SAT($Φ$), where $Φ$ is a given set of quantifier-free $\leq$-formulas. An instance of Poset-SAT($Φ$) consists of finitely many variables $x_1,\ldots,x_n$ and formulas $ϕ_i(x_{i_1},\ldots,x_{i_k})$ with $ϕ_i \in Φ$; the question is whether this input is satisfied by any partial order on $x_1,\ldots,x_n$ or not. We show that every such problem is NP-complete or can be solved in polynomial time, depending on $Φ$. All Poset-SAT problems can be formalized as constraint satisfaction problems on reducts of the random partial order. We use model-theoretic concepts and techniques from universal algebra to study these reducts. In the course of this analysis we establish a dichotomy that we believe is of independent interest in universal algebra and model theory.
PROMETHEE is Not Quadratic: An O(qn log(n)) Algorithm
Venue: arXiv cs.DS 2016
Year: 2016

It is generally believed that the preference ranking method PROMETHEE has a quadratic time complexity. In this paper, however, we present an exact algorithm that computes PROMETHEE's net flow scores in time O(qn log(n)), where q represents the number of criteria and n the number of alternatives. The method is based on first sorting the alternatives after which the unicriterion flow scores of all alternatives can be computed in one scan over the sorted list of alternatives while maintaining a sliding window. This method works with the linear and level criterion preference functions. The algorithm we present is exact and, due to the sub-quadratic time complexity, vastly extends the applicability of the PROMETHEE method. Experiments show that with the new algorithm, PROMETHEE can scale up to millions of tuples.
Crashing Modulus Attack on Modular Squaring for Rabin Cryptosystem
Venue: arXiv cs.CR 2016
Year: 2016

The Rabin cryptosystem has been proposed protect the unique ID (UID) in radio-frequency identification tags. The Rabin cryptosystem is a type of lightweight public key system that is theoretetically quite secure; however it is vulnerable to several side-channel attacks. In this paper, a crashing modulus attack is presented as a new fault attack on modular squaring during Rabin encryption. This attack requires only one fault in the public key if its perturbed public key can be factored. Our simulation results indicate that the attack is more than 50\% successful with several faults in practical time. A complicated situation arises when reconstrucing the message, including the UID, from ciphertext, i.e., the message and the perturbed public key are not relatively prime. We present a complete and mathematically rigorous message reconstruction algorithm for such a case. Moreover, we propose an exact formula to obtain a number of candidate messages. We show that the number is not generally equal to a power of two.
Platform Autonomous Custom Scalable Service using Service Oriented Cloud Computing Architecture
Venue: arXiv cs.DC 2016
Year: 2016

The global economic recession and the shrinking budget of IT projects have led to the need of development of integrated information systems at a lower cost. Today, the emerging phenomenon of cloud computing aims at transforming the traditional way of computing by providing both software applications and hardware resources as a service. With the rapid evolution of Information Communication Technology (ICT) governments, organizations and businesses are looking for solutions to improve their services and integrate their IT infrastructures. In recent years advanced technologies such as SOA and Cloud computing have been evolved to address integration problems. The Clouds enormous capacity with comparable low cost makes it an ideal platform for SOA deployment. This paper deals with the combined approach of Cloud and Service Oriented Architecture along with a Case Study and a review.
Storm Detection by Visual Learning Using Satellite Images
Venue: arXiv cs.CV 2016
Year: 2016

Computers are widely utilized in today's weather forecasting as a powerful tool to leverage an enormous amount of data. Yet, despite the availability of such data, current techniques often fall short of producing reliable detailed storm forecasts. Each year severe thunderstorms cause significant damage and loss of life, some of which could be avoided if better forecasts were available. We propose a computer algorithm that analyzes satellite images from historical archives to locate visual signatures of severe thunderstorms for short-term predictions. While computers are involved in weather forecasts to solve numerical models based on sensory data, they are less competent in forecasting based on visual patterns from satellite images. In our system, we extract and summarize important visual storm evidence from satellite image sequences in the way that meteorologists interpret the images. In particular, the algorithm extracts and fits local cloud motion from image sequences to model the storm-related cloud patches. Image data from the year 2008 have been adopted to train the model, and historical thunderstorm reports in continental US from 2000 through 2013 have been used as the ground-truth and priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing more accurate thunderstorm forecasts.
Protecting suppliers' private information: the case of stock levels and the impact of correlated items
Venue: arXiv cs.CR 2016
Year: 2016

A marketplace is defined where the private data of suppliers (e.g., prosumers) are protected, so that neither their identity nor their level of stock is made known to end customers, while they can sell their products at a reduced price. A broker acts as an intermediary, which takes care of providing the items missing to meet the customers' demand and allows end customers to take advantages of reduced prices through the subscription of option contracts. Formulas are provided for the option price under three different probability models for the availability of items. Option pricing allows the broker to partially transfer its risk on end customers.
Finding the Nucleoli of Large Cooperative Games: A Disproof with Counter-Example
Venue: arXiv cs.GT 2016
Year: 2016

Nguyen and Thomas (2016) claimed that they have found a method to compute the nucleoli of games with more than $50$ players using nested linear programs (LP). Unfortunately, this claim is false. They incorrectly applied the indirect proof by "$A \land \neg B$ implies $A \land \neg A$" to conclude that "if $A$ then $B$" is valid. In fact, they prove that a truth implies a falsehood. As established by Meinhardt (2015a), this is a wrong statement. Therefore, instead of giving a proof of their main Theorem 4b, they give a disproof. It comes as no surprise to us that the flow game example presented by these authors to support their arguments is obviously a counter-example of their algorithm. We show that the computed solution by this algorithm is neither the nucleolus nor a core element of the flow game. Moreover, the stopping criterion of all proposed methods is wrong, since it does not satisfy one of Kohlberg's properties (cf. Kohlberg (1971)). As a consequence, none of these algorithms is robust.
Caching Incentive Design in Wireless D2D Networks: A Stackelberg Game Approach
Venue: arXiv cs.IT 2016
Year: 2016

Caching in wireless device-to-device (D2D) networks can be utilized to offload data traffic during peak times. However, the design of incentive mechanisms is challenging due to the heterogeneous preference and selfish nature of user terminals (UTs). In this paper, we propose an incentive mechanism in which the base station (BS) rewards those UTs that share contents with others using D2D communication. We study the cost minimization problem for the BS and the utility maximization problem for each UT. In particular, the BS determines the rewarding policy to minimize his total cost, while each UT aims to maximize his utility by choosing his caching policy. We formulate the conflict among UTs and the tension between the BS and the UTs as a Stackelberg game. We show the existence of the equilibrium and propose an iterative gradient algorithm (IGA) to obtain the Stackelberg Equilibrium. Extensive simulations are carried out to evaluate the performance of the proposed caching scheme and comparisons are drawn with several baseline caching schemes with no incentives. Numerical results show that the caching scheme under our incentive mechanism outperforms other schemes in terms of the BS serving cost and the utilities of the UTs.
A Nonlinear Adaptive Filter Based on the Model of Simple Multilinear Functionals
Venue: arXiv cs.SY 2016
Year: 2016

Nonlinear adaptive filtering allows for modeling of some additional aspects of a general system and usually relies on highly complex algorithms, such as those based on the Volterra series. Through the use of the Kronecker product and some basic facts of tensor algebra, we propose a simple model of nonlinearity, one that can be interpreted as a product of the outputs of K FIR linear filters, and compute its cost function together with its gradient, which allows for some analysis of the optimization problem. We use these results it in a stochastic gradient framework, from which we derive an LMS-like algorithm and investigate the problems of multi-modality in the mean-square error surface and the choice of adequate initial conditions. Its computational complexity is calculated. The new algorithm is tested in a system identification setup and is compared with other polynomial algorithms from the literature, presenting favorable convergence and/or computational complexity.
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision. While excellent performance was achieved for image classification when large amounts of labeled visual data are available, their success for un-supervised tasks such as image retrieval has been moderate so far. Our paper focuses on this latter setting and explores several methods for learning patch descriptors without supervision with application to matching and instance-level retrieval. To that effect, we propose a new family of convolutional descriptors for patch representation , based on the recently introduced convolutional kernel networks. We show that our descriptor, named Patch-CKN, performs better than SIFT as well as other convolutional networks learned by artificially introducing supervision and is significantly faster to train. To demonstrate its effectiveness, we perform an extensive evaluation on standard benchmarks for patch and image retrieval where we obtain state-of-the-art results. We also introduce a new dataset called RomePatches, which allows to simultaneously study descriptor performance for patch and image retrieval.
Repairing Conflicts among MVCC Transactions
Venue: arXiv cs.DB 2016
Year: 2016

The optimistic variants of MVCC (Multi-Version Concurrency Control) avoid blocking concurrent transactions at the cost of having a validation phase. Upon failure in the validation phase, the transaction is usually aborted and restarted from scratch. The "abort and restart" approach becomes a performance bottleneck for the use cases with high contention objects or long running transactions. In addition, restarting from scratch creates a negative feedback loop in the system, because the system incurs additional overhead that may create even further conflicts. In this paper, we propose a novel approach for conflict resolution in MVCC for in-memory databases. This low overhead approach summarizes the transaction programs in the form of a dependency graph. The dependency graph also contains the constructs used in the validation phase of the MVCC algorithm. Then, in the case of encountering conflicts among transactions, the conflict locations in the program are quickly detected, and the conflicting transactions are partially re-executed. This approach maximizes the reuse of the computations done in the initial execution round, and increases the transaction processing throughput.
Optimal Scheduling for Incentive WiFi Offloading under Energy Constraint
Venue: arXiv cs.NI 2016
Year: 2016

WiFi offloading, where mobile device users (e.g., smart phone users) transmit packets through WiFi networks rather than cellular networks, is a promising solution to alleviating the heavy traffic burden of cellular networks due to data explosion. However, since WiFi networks are intermittently available, a mobile device user in WiFi offloading usually needs to wait for WiFi connection and thus experiences longer delay of packet transmission. To motivate users to participate in WiFi offloading, cellular network operators give incentives (rewards like coupons, e-coins) to users who wait for WiFi connection and transmit packets through WiFi networks. In this paper, we aim at maximizing users' rewards while meeting constraints on queue stability and energy consumption. However, we face scheduling challenges from random packet arrivals, intermittent WiFi connection and time varying wireless link states. To address these challenges, we first formulate the problem as a stochastic optimization problem. We then propose an optimal scheduling policy, named Optimal scheduling Policy under Energy Constraint (OPEC), which makes online decisions as to when to delay packet transmission to wait for WiFi connection and which wireless link (WiFi link or cellular link) to transmit packets on. OPEC automatically adapts to random packet arrivals and time varying wireless link states, not requiring a priori knowledge of packet arrival and wireless link probabilities. As verified by simulations, OPEC scheduling policy can achieve the maximum rewards while keeping queue stable and meeting energy consumption constraint.