Biblio

The popularity of Cloud computing has considerably increased during the last years. The increase of Cloud users and their interactions with the Cloud infrastructure raise the risk of resources faults. Such a problem can lead to a bad reputation of the Cloud environment which slows down the evolution of this technology. To address this issue, the dynamic and the complex architecture of the Cloud should be taken into account. Indeed, this architecture requires that resources protection and healing must be transparent and without external intervention. Unlike previous work, we suggest integrating the fundamental aspects of autonomic computing in the Cloud to deal with the self-healing of Cloud resources. Starting from the high degree of match between autonomic computing systems and multiagent systems, we propose to take advantage from the autonomous behaviour of agent technology to create an intelligent Cloud that supports autonomic aspects. Our proposed solution is a multi-agent system which interacts with the Cloud infrastructure to analyze the resources state and execute Checkpoint/Replication strategy or migration technique to solve the problem of failed resources.

In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66% with a large improvement of 16.19% with respect to the baseline.

Online conversations, such as blogs, provide rich amount of information and opinions about popular queries. Given a query, traditional blog sites return a set of conversations often consisting of thousands of comments with complex thread structure. Since the interfaces of these blog sites do not provide any overview of the data, it becomes very difficult for the user to explore and analyze such a large amount of conversational data. In this paper, we present MultiConVis, a visual text analytics system designed to support the exploration of a collection of online conversations. Our system tightly integrates NLP techniques for topic modeling and sentiment analysis with information visualizations, by considering the unique characteristics of online conversations. The resulting interface supports the user exploration, starting from a possibly large set of conversations, then narrowing down to the subset of conversations, and eventually drilling-down to the set of comments of one conversation. Our evaluations through case studies with domain experts and a formal user study with regular blog readers illustrate the potential benefits of our approach, when compared to a traditional blog reading interface.

We introduce the Nondeterministic Strong Exponential Time Hypothesis (NSETH) as a natural extension of the Strong Exponential Time Hypothesis (SETH). We show that both refuting and proving NSETH would have interesting consequences. In particular we show that disproving NSETH would give new nontrivial circuit lower bounds. On the other hand, NSETH implies non-reducibility results, i.e. the absence of (deterministic) fine-grained reductions from SAT to a number of problems. As a consequence we conclude that unless this hypothesis fails, problems such as 3-SUM, APSP and model checking of a large class of first-order graph properties cannot be shown to be SETH-hard using deterministic or zero-error probabilistic reductions.

Randomness extractors and error correcting codes are fundamental objects in computer science. Recently, there have been several natural generalizations of these objects, in the context and study of tamper resilient cryptography. These are seeded non-malleable extractors, introduced by Dodis and Wichs; seedless non-malleable extractors, introduced by Cheraghchi and Guruswami; and non-malleable codes, introduced by Dziembowski, Pietrzak and Wichs. Besides being interesting on their own, they also have important applications in cryptography, e.g, privacy amplification with an active adversary, explicit non-malleable codes etc, and often have unexpected connections to their non-tampered analogues. However, the known constructions are far behind their non-tampered counterparts. Indeed, the best known seeded non-malleable extractor requires min-entropy rate at least 0.49; while explicit constructions of non-malleable two-source extractors were not known even if both sources have full min-entropy, and was left as an open problem by Cheraghchi and Guruswami. In this paper we make progress towards solving the above problems and other related generalizations. Our contributions are as follows. (1) We construct an explicit seeded non-malleable extractor for polylogarithmic min-entropy. This dramatically improves all previous results and gives a simpler 2-round privacy amplification protocol with optimal entropy loss, matching the best known result. In fact, we construct more general seeded non-malleable extractors (that can handle multiple adversaries) which were used in the recent construction of explicit two-source extractors for polylogarithmic min-entropy. (2) We construct the first explicit non-malleable two-source extractor for almost full min-entropy thus resolving the open question posed by Cheraghchi and Guruswami. (3) We motivate and initiate the study of two natural generalizations of seedless non-malleable extractors and non-malleable codes, where the sources or the codeword may be tampered many times. By using the connection found by Cheraghchi and Guruswami and providing efficient sampling algorithms, we obtain the first explicit non-malleable codes with tampering degree t, with near optimal rate and error. We call these stronger notions one-many and many-manynon-malleable codes. This provides a stronger information theoretic analogue of a primitive known as continuous non-malleable codes. Our basic technique used in all of our constructions can be seen as inspired, in part, by the techniques previously used to construct cryptographic non-malleable commitments.

Securing visible light communication (VLC) systems on the physical layer promises to prevent against a variety of attacks. Recent work shows that the adaption of existing legacy radio wave physical layer security (PLS) mechanisms is possible with minor changes. Yet, many adaptations open new vulnerabilities due to distinct propagation characteristics of visible light. A common understanding of threats arising from various attacker capabilities is missing. We specify a new attacker model for visible light physical layer attacks and evaluate the applicability of existing PLS approaches. Our results show that many attacks are not considered in current solutions.

We present a code- and input-sensitive sanitization synthesis approach for repairing string vulnerabilities that are common in web applications. The synthesized sanitization patch modifies the user input in an optimal way while guaranteeing that the repaired web application is not vulnerable. Given a web application, an input pattern and an attack pattern, we use automata-based static string analysis techniques to compute a sanitization signature that characterizes safe input values that obey the given input pattern and are safe with respect to the given attack pattern. Using the sanitization signature, we synthesize an optimal sanitization patch that converts malicious user inputs to benign ones with minimal editing. When the generated patch is added to the web application, it is guaranteed that the repaired web application is no longer vulnerable. We present refinements to previous sanitization synthesis algorithms that reduce the runtime sanitization cost significantly. We evaluate our approach on open source web applications using common input and attack patterns, demonstrating the effectiveness of our approach.

In this paper, we investigate the impact of information-theoretic secrecy constraint on the capacity and delay of mobile ad hoc networks (MANETs) with mobile legitimate nodes and static eavesdroppers whose location and channel state information (CSI) are both unknown. We assume n legitimate nodes move according to the fast i.i.d. mobility pattern and each desires to communicate with one randomly selected destination node. There are also nv static eavesdroppers located uniformly in the network and we assume the number of eavesdroppers is much larger than that of legitimate nodes, i.e., v textgreater 1. We propose a novel simple secure communication model, i.e., the secure protocol model, and prove its equivalence to the widely accepted secure physical model under a few technical assumptions. Based on the proposed model, a framework of analyzing the secrecy capacity and delay in MANETs is established. Given a delay constraint D, we find that the optimal secrecy throughput capacity is [EQUATION](W((D/n))(2/3), where W is the data rate of each link. We observe that: 1) the capacity-delay tradeoff is independent of the number of eavesdroppers, which indicates that adding more eavesdroppers will not degenerate the performance of the legitimate network as long as v textgreater 1; 2) the capacity-delay tradeoff of our paper outperforms the previous result Θ((1/nψe)) in [11], where ψe = nv–1 = ω(1) is the density of the eavesdroppers. Throughout this paper, for functions f(n) and G(n), we denote f(n) = o(g(n)) if limn→∞ (f(n)/g(n)) = 0; f(n) = ω(g(n)) if g(n) = o(f(n)); f(n) = O(g(n)) if there is a positive constant c such that f(n) ≤ cg(n) for sufficiently large n; f(n) = Ω(g(n))if g(n) = O(f(n)); f(n) = Θ(g(n) if both f(n) = O(g(n)) and f(n) = Omega;(g(n)) hold. Besides, the order notation [EQUATION] omits the polylogarithmic factors for better readability.

Simple connectivity and data requirements together with high lifetime of battery are the main issues for the machine-to-machine (M2M) communications. 3GPP focuses on three main licensed standardizations based on Long Term Evolution (LTE), GSM and clean-slate technologies. The paper considers the last one and proposes a modified slotted-Aloha method to increase the capability of supporting a massive number of low-throughput devices. The proposed method increases the access rate of users belonging to each class considered in the clean-slate standard and consequently the total throughput offered by the system. To derive the mean access rate per class, we use the Markov chain approach and simulation results are provided for scenarios with different data rate and also in terms of cell average delay.

Simple connectivity and data requirements together with high lifetime of battery are the main issues for the machine-to-machine (M2M) communications. 3GPP focuses on three main licensed standardizations based on Long Term Evolution (LTE), GSM and clean-slate technologies. The paper considers the last one and proposes a modified slotted-Aloha method to increase the capability of supporting a massive number of low-throughput devices. The proposed method increases the access rate of users belonging to each class considered in the clean-slate standard and consequently the total throughput offered by the system. To derive the mean access rate per class, we use the Markov chain approach and simulation results are provided for scenarios with different data rate and also in terms of cell average delay.

Many mobile services consist of two components: a server providing an API, and an application running on smartphones and communicating with the API. An unresolved problem in this design is that it is difficult for the server to authenticate which app is accessing the API. This causes many security problems. For example, the provider of a private network API has to embed secrets in its official app to ensure that only this app can access the API; however, attackers can uncover the secret by reverse-engineering. As another example, malicious apps may send automatic requests to ad servers to commit ad fraud. In this work, we propose a system that allows network API to authenticate the mobile app that sends each request so that the API can make an informed access control decision. Our system, the Mobile Trusted-Origin Policy, consists of two parts: 1) an app provenance mechanism that annotates outgoing HTTP(S) requests with information about which app generated the network traffic, and 2) a code isolation mechanism that separates code within an app that should have different app provenance signatures into mobile origin. As motivation for our work, we present two previously-unknown families of apps that perform click fraud, and examine how the lack of mobile origin information enables the attacks. Based on our observations, we propose Trusted Cross-Origin Requests to handle point (1), which automatically includes mobile origin information in outgoing HTTP requests. Servers may then decide, based on the mobile origin data, whether to process the request or not. We implement a prototype of our system for Android and evaluate its performance, security, and deployability. We find that our system can achieve our security and utility goals with negligible overhead.

We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.

Chang-Chen-Wang's (3,n) Secret grayscale image Sharing between n grayscale cover images method with participant Authentication and damaged pixels Repairing (SSAR) properties is analyzed; it restores the secret image from any three of the cover images used. We show that SSAR may fail, is not able fake participant recognizing, and has limited by 62.5% repairing ability. We propose SSAR (4,n) enhancement, SSAR-E, allowing 100% exact restoration of a corrupted pixel using any four of n covers, and recognizing a fake participant with the help of cryptographic hash functions with 5-bit values that allows better (vs. 4 bits) error detection. Using a special permutation with only one loop including all the secret image pixels, SSAR-E is able restoring all the secret image damaged pixels having just one correct pixel left. SSAR-E allows restoring the secret image to authorized parties only contrary to SSAR. The performance and size of cover images for SSAR-E are the same as for SSAR.

Chang-Chen-Wang's (3,n) Secret grayscale image Sharing between n grayscale cover images method with participant Authentication and damaged pixels Repairing (SSAR) properties is analyzed; it restores the secret image from any three of the cover images used. We show that SSAR may fail, is not able fake participant recognizing, and has limited by 62.5% repairing ability. We propose SSAR (4,n) enhancement, SSAR-E, allowing 100% exact restoration of a corrupted pixel using any four of n covers, and recognizing a fake participant with the help of cryptographic hash functions with 5-bit values that allows better (vs. 4 bits) error detection. Using a special permutation with only one loop including all the secret image pixels, SSAR-E is able restoring all the secret image damaged pixels having just one correct pixel left. SSAR-E allows restoring the secret image to authorized parties only contrary to SSAR. The performance and size of cover images for SSAR-E are the same as for SSAR.

We propose a method for classifying Wi-Fi enabled mobile handheld devices (smartphones) and non-handheld devices (laptops) in a completely passive way, that is resorting neither to traffic probes on network edge devices nor to deep packet inspection techniques to read application layer information. Instead, classification is performed starting from probe requests Wi-Fi frames, which can be sniffed with inexpensive commercial hardware. We extract distinctive features from probe request frames (how many probe requests are transmitted by each device, how frequently, etc.) and take a machine learning approach, training four different classifiers to recognize the two types of devices. We compare the performance of the different classifiers and identify a solution based on a Random Decision Forest that correctly classify devices 95% of the times. The classification method is then used as a pre-processing stage to analyze network traffic traces from the wireless network of a university building, with interesting considerations on the way different types of devices uses the network (amount of data exchanged, duration of connections, etc.). The proposed methodology finds application in many scenarios related to Wi-Fi network management/optimization and Wi-Fi based services.

Virtual Reality and immersive experiences, which allow players to share the same virtual environment as the characters of a virtual world, have gained more and more interest recently. In order to conceive these immersive virtual worlds, one of the challenges is to give to the characters that populate them the ability to express behaviors that can support the immersion. In this work, we propose a model capable of controlling and simulating a conversational group of social agents in an immersive environment. We describe this model which has been previously validated using a regular screen setting and we present a study for measuring whether users recognized the attitudes expressed by virtual agents through the realtime generated animations of nonverbal behavior in an immersive setting. Results mirrored those of the regular screen setting thus providing further insights for improving players experiences by integrating them into immersive simulated group conversations with characters that express different interpersonal attitudes.

In this paper we present work-in-progress toward a vision of personalized views of visual analytics interfaces in the context of collaborative analytics in immersive spaces. In particular, we are interested in the sense of immersion, responsiveness, and personalization afforded by gaze-based input. Through combining large screen visual analytics tools with eye-tracking, a collaborative visual analytics system can become egocentric while not disrupting the collaborative nature of the experience. We present a prototype system and several ideas for real-time personalization of views in visual analytics.

Kernel rootkits often hide associated malicious processes by altering reported task struct information to upper layers and applications such as ps and top. Virtualized settings offer a unique opportunity to mitigate this behavior using dynamic virtual machine introspection (VMI). For known kernels, VMI can be deployed to search for kernel objects and identify them by using unique data structure "signatures". In existing work, VMI-detected data structure signatures are based on values and structural features which must be (often exactly) present in memory snapshots taken, for accurate detection. This features a certain brittleness and rootkits can escape detection by simply temporarily "un-tangling" the corresponding structures when not running. Here we introduce a new paradigm, that defeats such behavior by training for and observing signatures of timing access patterns to any and all kernel-mapped data regions, including objects that are not directly linked in the "official" list of tasks. The use of timing information in training detection signatures renders the defenses resistant to attacks that try to evade detection by removing their corresponding malicious processes before scans. KXRay successfully detected processes hidden by four traditional rootkits.

This paper presents a new type of online password guessing attack called "WiPING" (Wi-Fi signal-based PIN Guessing attack) to guess a victim's PIN (Personal Identification Number) within a small number of unlock attempts. WiPING uses wireless signal patterns identified from observing sequential finger movements involved in typing a PIN to unlock a mobile device. A list of possible PIN candidates is generated from the wireless signal patterns, and is used to improve performance of PIN guessing attacks. We implemented a proof-of-concept attack to demonstrate the feasibility of WiPING. Our results showed that WiPING could be practically effective: while pure guessing attacks failed to guess all 20 PINs, WiPING successfully guessed two PINs.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the \$d\$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using \$\textbackslashtextsf\Python\\$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.