Biblio

This paper proposes a novel deep two-view approach to learn features from both visible and thermal images and leverage the commonality among visible and thermal images for facial expression recognition from visible images. The thermal images are used as privileged information, which is required only during training to help visible images learn better features and classifier. Specifically, we first learn a deep model for visible images and thermal images respectively, and use the learned feature representations to train SVM classifiers for expression classification. We then jointly refine the deep models as well as the SVM classifiers for both thermal images and visible images by imposing the constraint that the outputs of the SVM classifiers from two views are similar. Therefore, the resulting representations and classifiers capture the inherent connections among visible facial image, infrared facial image and target expression labels, and hence improve the recognition performance for facial expression recognition from visible images during testing. Experimental results on the benchmark expression database demonstrate the effectiveness of our proposed method.

To ensure the authenticity and integrity, data are traditionally signed by digital signatures, which will be invalidated by any processing of the data. With the vast amount of data generated every day, it is however desirable to allow flexible processing of the signed data via applying computations or functions on them, without losing the authenticity. Signatures can also serve as credentials for access control, which appears in many aspects of life, ranging from unlocking security gates of buildings, to virtual access of data by computer programs. With the prolific use of Internet-of-Things (IoT), everything is getting connected together. There is an emerging need for more versatile credentials to secure new application scenarios, for instance, assigning different credentials to different devices, such that they can authenticate and cooperate with each other to jointly perform some computation tasks. To realize the above, we envision a general framework called functional credentials. Functional credentials allow multiple entities to (jointly) issue, combine, delegate, present, verify, escrow, and decrypt different forms of credentials, by operating on the associated "cryptographic objects" including secret keys, attributes, ciphertexts, and auxiliary data (e.g., pseudonym, expiry date, or policies for combination / delegation / revocation). Instantiating this framework with different functions can provide a spectrum of solutions for securing IoT. This talk covers both the practical applications and theoretic foundations. I will first motivate the versatility of functional credentials by case studies on IoT, which identify the need of new credential systems. I will then formulate the definition of functional credentials. Finally, I will share some initial ideas in realizing functional credentials, and discuss the obstacles ahead.

This paper presents the holistic approach to cyber resilience as a means of preparing for the "unknown unknowns". Principles of augmented cyber risks management and resilience management model at national level are presented, with elaboration on multi-stakeholder engagement and partnership for the implementation of national cyber resilience collaborative framework. The complementarity of governance, law, and business/industry initiatives is outlined, with examples of the collaborative resilience model for the Bulgarian national strategy and its multi-national engagements.

Mobile devices store a diverse set of private user data and have gradually become a hub to control users' other personal Internet-of-Things devices. Access control on mobile devices is therefore highly important. The widely accepted solution is to protect access by asking for a password. However, password authentication is tedious, e.g., a user needs to input a password every time she wants to use the device. Moreover, existing biometrics such as face, fingerprint, and touch behaviors are vulnerable to forgery attacks. We propose a new touch-based biometric authentication system that is passive and secure against forgery attacks. In our touch-based authentication, a user's touch behaviors are a function of some random "secret". The user can subconsciously know the secret while touching the device's screen. However, an attacker cannot know the secret at the time of attack, which makes it challenging to perform forgery attacks even if the attacker has already obtained the user's touch behaviors. We evaluate our touch-based authentication system by collecting data from 25 subjects. Results are promising: the random secrets do not influence user experience and, for targeted forgery attacks, our system achieves 0.18 smaller Equal Error Rates (EERs) than previous touch-based authentication.

This paper presents a framework for (1) generating variants of known attacks, (2) replaying attack variants in an isolated environment and, (3) validating detection capabilities of attack detection techniques against the variants. Our framework facilitates reproducible security experiments. We generated 648 variants of three real-world attacks (observed at the National Center for Supercomputing Applications at the University of Illinois). Our experiment showed the value of generating attack variants by quantifying the detection capabilities of three detection methods: a signature-based detection technique, an anomaly-based detection technique, and a probabilistic graphical model-based technique.

The speech emotion recognition accuracy of prosody feature and voice quality feature declines with the decrease of SNR (Signal to Noise Ratio) of speech signals. In this paper, we propose novel sub-band spectral centroid weighted wavelet packet cepstral coefficients (W-WPCC) for robust speech emotion recognition. The W-WPCC feature is computed by combining the sub-band energies with sub-band spectral centroids via a weighting scheme to generate noise-robust acoustic features. And Deep Belief Networks (DBNs) are artificial neural networks having more than one hidden layer, which are first pre-trained layer by layer and then fine-tuned using back propagation algorithm. The well-trained deep neural networks are capable of modeling complex and non-linear features of input training data and can better predict the probability distribution over classification labels. We extracted prosody feature, voice quality features and wavelet packet cepstral coefficients (WPCC) from the speech signals to combine with W-WPCC and fused them by Deep Belief Networks (DBNs). Experimental results on Berlin emotional speech database show that the proposed fused feature with W-WPCC is more suitable in speech emotion recognition under noisy conditions than other acoustics features and proposed DBNs feature learning structure combined with W-WPCC improve emotion recognition performance over the conventional emotion recognition method.

For single-owner multi-user wireless sensor networks, there is the demand to implement the user privacy-preserving access control protocol in WSNs. Firstly, we propose a new access control protocol based on an efficient attribute-based signature. In the protocol, users need to pay for query, and the protocol achieves fine-grained access control and privacy protection. Then, the protocol is analyzed in detail. Finally, the comparison of protocols indicates that our scheme is more efficient. Our scheme not only protects the privacy of users and achieves fine-grained access control, but also provides the query command validation with low overhead. The scheme can better satisfy the access control requirements of wireless sensor networks.

Computing similarity, especially Jaccard Similarity, between two datasets is a fundamental building block in big data analytics, and extensive applications including genome matching, plagiarism detection, social networking, etc. The increasing user privacy concerns over the release of has sensitive data have made it desirable and necessary for two users to evaluate Jaccard Similarity over their datasets in a privacy-preserving manner. In this paper, we propose two efficient and secure protocols to compute the Jaccard Similarity of two users' private sets with the help of an unfully-trusted server. Specifically, in order to boost the efficiency, we leverage Minhashing algorithm on encrypted data, where the output of our protocols is guaranteed to be a close approximation of the exact value. In both protocols, only an approximate similarity result is leaked to the server and users. The first protocol is secure against a semi-honest server, while the second protocol, with a novel consistency-check mechanism, further achieves result verifiability against a malicious server who cheats in the executions. Experimental results show that our first protocol computes an approximate Jaccard Similarity of two billion-element sets within only 6 minutes (under 256-bit security in parallel mode). To the best of our knowledge, our consistency-check mechanism represents the very first work to realize an efficient verification particularly on approximate similarity computation.

This work is motivated by the rapid increase of the number of attacks in computer networks and software engineering. In this paper we study identity snowball attacks and formally prove the correctness of suggested solutions to this type of attack (solutions that are based on the graph reachability reduction) using a proof assistant. We propose a model of an attack graph that captures technical informations about the calculation of reachability of the graph. The model has been implemented with the proof assistant PVS 6.0 (Prototype Verification System). It makes it possible to prove algorithms of reachability reduction such as Sparsest\_cut.

When encountering a packet flow for which it has no covering rule, a software-defined networking (SDN) switch requests an appropriate rule from its controller; this request delays the routing of the flow until the controller responds. We show that this delay gives rise to a timing side channel in which an attacker can test for the recent occurrence of a target flow by judiciously probing the switch with forged flows and using the delays they suffer to discern whether covering rules were previously installed in the switch. We develop a Markov model of an SDN switch to permit the attacker to select the best probe (or probes) to infer whether a target flow has recently occurred. Our model captures complexities related to rule evictions to make room for other rules; rule timeouts due to inactivity; the presence of multiple rules that apply to overlapping sets of flows; and rule priorities. We show that our model permits detection of target flows with considerable accuracy in many cases.

Code review is known to be an efficient quality assurance technique. Many software companies today use it, usually with a process similar to the patch review process in open source software development. However, there is still a large fraction of companies performing almost no code reviews at all. And the companies that do code reviews have a lot of variation in the details of their processes. For researchers trying to improve the use of code reviews in industry, it is important to know the reasons for these process variations. We have performed a grounded theory study to clarify process variations and their rationales. The study is based on interviews with software development professionals from 19 companies. These interviews provided insights into the reasons and influencing factors behind the adoption or non-adoption of code reviews as a whole as well as for different process variations. We have condensed these findings into seven hypotheses and a classification of the influencing factors. Our results show the importance of cultural and social issues for review adoption. They trace many process variations to differences in development context and in desired review effects.

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

Function Secret Sharing (FSS), introduced by Boyle et al. (Eurocrypt 2015), provides a way for additively secret-sharing a function from a given function family F. More concretely, an m-party FSS scheme splits a function f : \0, 1\n -textgreater G, for some abelian group G, into functions f1,...,fm, described by keys k1,...,km, such that f = f1 + ... + fm and every strict subset of the keys hides f. A Distributed Point Function (DPF) is a special case where F is the family of point functions, namely functions f\_\a,b\ that evaluate to b on the input a and to 0 on all other inputs. FSS schemes are useful for applications that involve privately reading from or writing to distributed databases while minimizing the amount of communication. These include different flavors of private information retrieval (PIR), as well as a recent application of DPF for large-scale anonymous messaging. We improve and extend previous results in several ways: * Simplified FSS constructions. We introduce a tensoring operation for FSS which is used to obtain a conceptually simpler derivation of previous constructions and present our new constructions. * Improved 2-party DPF. We reduce the key size of the PRG-based DPF scheme of Boyle et al. roughly by a factor of 4 and optimize its computational cost. The optimized DPF significantly improves the concrete costs of 2-server PIR and related primitives. * FSS for new function families. We present an efficient PRG-based 2-party FSS scheme for the family of decision trees, leaking only the topology of the tree and the internal node labels. We apply this towards FSS for multi-dimensional intervals. We also present a general technique for extending FSS schemes by increasing the number of parties. * Verifiable FSS. We present efficient protocols for verifying that keys (k*/1,...,k*/m ), obtained from a potentially malicious user, are consistent with some f in F. Such a verification may be critical for applications that involve private writing or voting by many users.

Mobile code distribution relies on digital signatures to guarantee code authenticity. Unfortunately, standard signature schemes are not well suited for use in conjunction with program transformation techniques, such as aspect-oriented programming. With these techniques, code development is performed in sequence by multiple teams of programmers. This is fundamentally different from traditional single-developer/ single-user models, where users can verify end-to-end (i.e., developer-to-user) authenticity of the code using digital signatures. To address this limitation, we introduce FLEX, a flexible code authentication framework for mobile applications. FLEX allows semi-trusted intermediaries to modify mobile code without invalidating the developer's signature, as long as the modification complies with a "contract" issued by the developer. We introduce formal definitions for secure code modification, and show that our instantiation of FLEX is secure under these definitions. Although FLEX can be instantiated using any language, we design AMJ–a novel programming language that supports code annotations–and implement a FLEX prototype based on our new language.

We address the known problem of detecting a previous compression in JPEG images, focusing on the challenging case of high and very high quality factors (textgreater= 90) as well as repeated compression with identical or nearly identical quality factors. We first revisit the approaches based on Benford–Fourier analysis in the DCT domain and block convergence analysis in the spatial domain. Both were originally conceived for specific scenarios. Leveraging decision tree theory, we design a combined approach complementing the discriminatory capabilities. We obtain a set of novel detectors targeted to high quality grayscale JPEG images.

Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature. We introduce a new similarity function for heterogeneous graphs that compares two graphs based on their relative frequency of local substructures, represented as short strings. This function lends itself to a vector representation of a graph, which is (a) fast to compute, and (b) amenable to a sketched version with bounded size that preserves similarity. StreamSpot exhibits desirable properties that a streaming application requires: it is (i) fully-streaming; processing the stream one edge at a time as it arrives, (ii) memory-efficient; requiring constant space for the sketches and the clustering, (iii) fast; taking constant time to update the graph sketches and the cluster summaries that can process over 100,000 edges per second, and (iv) online; scoring and flagging anomalies in real time. Experiments on datasets containing simulated system-call flow graphs from normal browser activity and various attack scenarios (ground truth) show that StreamSpot is high-performance; achieving above 95% detection accuracy with small delay, as well as competitive time and memory usage.  

Language-integrated query is an embedding of database queries into a host language to code queries at a higher level than the all-to-common concatenation of strings of SQL fragments. The eventually produced SQL is ensured to be well-formed and well-typed, and hence free from the embarrassing (security) problems. Language-integrated query takes advantage of the host language's functional and modular abstractions to compose and reuse queries and build query libraries. Furthermore, language-integrated query systems like T-LINQ generate efficient SQL, by applying a number of program transformations to the embedded query. Alas, the set of transformation rules is not designed to be extensible. We demonstrate a new technique of integrating database queries into a typed functional programming language, so to write well-typed, composable queries and execute them efficiently on any SQL back-end as well as on an in-memory noSQL store. A distinct feature of our framework is that both the query language as well as the transformation rules needed to generate efficient SQL are safely user-extensible, to account for many variations in the SQL back-ends, as well for domain-specific knowledge. The transformation rules are guaranteed to be type-preserving and hygienic by their very construction. They can be built from separately developed and reusable parts and arbitrarily composed into optimization pipelines. With this technique we have embedded into OCaml a relational query language that supports a very large subset of SQL including grouping and aggregation. Its types cover the complete set of intricate SQL behaviors.

Lattice-based cryptography offers some of the most attractive primitives believed to be resistant to quantum computers. Following increasing interest from both companies and government agencies in building quantum computers, a number of works have proposed instantiations of practical post-quantum key exchange protocols based on hard problems in ideal lattices, mainly based on the Ring Learning With Errors (R-LWE) problem. While ideal lattices facilitate major efficiency and storage benefits over their non-ideal counterparts, the additional ring structure that enables these advantages also raises concerns about the assumed difficulty of the underlying problems. Thus, a question of significant interest to cryptographers, and especially to those currently placing bets on primitives that will withstand quantum adversaries, is how much of an advantage the additional ring structure actually gives in practice. Despite conventional wisdom that generic lattices might be too slow and unwieldy, we demonstrate that LWE-based key exchange is quite practical: our constant time implementation requires around 1.3ms computation time for each party; compared to the recent NewHope R-LWE scheme, communication sizes increase by a factor of 4.7x, but remain under 12 KiB in each direction. Our protocol is competitive when used for serving web pages over TLS; when partnered with ECDSA signatures, latencies increase by less than a factor of 1.6x, and (even under heavy load) server throughput only decreases by factors of 1.5x and 1.2x when serving typical 1 KiB and 100 KiB pages, respectively. To achieve these practical results, our protocol takes advantage of several innovations. These include techniques to optimize communication bandwidth, dynamic generation of public parameters (which also offers additional security against backdoors), carefully chosen error distributions, and tight security parameters.

Fast and accurate identification of active recursive domain name servers (RDNS) is a fundamental step to evaluate security risk degrees of DNS systems. Much identification work have been proposed based on network traffic measurement technology. Even though identifying RDNS accurately, they waste huge network resources, and fail to obtain host activity and distinguish between direct and indirect RDNS. In this paper, we proposed an approach to identify direct and forward RDNS based on our three key insights on their request-response behaviors, and proposed an approach to identify indirect RDNS based on CNAME redirect behaviors. To work in high-speed backbone networks, we further proposed an online connectivity estimation algorithm to obtain estimated values used in our identification approaches. According to our experiments, we can identify RDNS with a high accuracy by selecting the reasonable thresholds. The accuracy of identifying direct and forward RDNS can reach 89%.The accuracy of identifying indirect RDNS can reach 90%.Moreover, our work is capable of real-time analyzing high speed backbone traffics.

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.

Current post-mortem cyber-forensic techniques may cause significant disruption to the evidence gathering process by breaking active network connections and unmounting encrypted disks. Although newer live forensic analysis tools can preserve active state, they may taint evidence by leaving footprints in memory. To help address these concerns we present Forenscope, a framework that allows an investigator to examine the state of an active system without the effects of taint or forensic blurriness caused by analyzing a running system. We show how Forenscope can fit into accepted workflows to improve the evidence gathering process. Forenscope preserves the state of the running system and allows running processes, open files, encrypted filesystems and open network sockets to persist during the analysis process. Forenscope has been tested on live systems to show that it does not operationally disrupt critical processes and that it can perform an analysis in less than 15 seconds while using only 125 KB of memory. We show that Forenscope can detect stealth rootkits, neutralize threats and expedite the investigation process by finding evidence in memory.

This paper addresses the problem of distributed multi-agent optimization in which each agent i has a local cost function hi(x), and the goal is to optimize a global cost function that aggregates the local cost functions. Such optimization problems are of interest in many contexts, including distributed machine learning, distributed resource allocation, and distributed robotics. We consider the distributed optimization problem in the presence of faulty agents. We focus primarily on Byzantine failures, but also briey discuss some results for crash failures. For the Byzantine fault-tolerant optimization problem, the ideal goal is to optimize the average of local cost functions of the non-faulty agents. However, this goal also cannot be achieved. Therefore, we consider a relaxed version of the fault-tolerant optimization problem. The goal for the relaxed problem is to generate an output that is an optimum of a global cost function formed as a convex combination of local cost functions of the non-faulty agents. More precisely, there must exist weights αi for i∈N such that αi ≥ 0 and ∑i≥ Nαi=1, and the output is an optimum of the cost function ∑i≥ N αihi(x). Ideally, we would like αi=1/textbarNtextbar for all i≥ N, however, this cannot be guaranteed due to the presence of faulty agents. In fact, the maximum number of nonzero weights (αi's) that can be guaranteed is textbarNtextbar-f, where f is the maximum number of Byzantine faulty agents. We present an iterative distributed algorithm that achieves optimal fault-tolerance. Specifically, it ensures that at least textbarNtextbar-f agents have weights that are bounded away from 0 (in particular, lower bounded by 1/2textbarNtextbar-f\vphantom\\). The proposed distributed algorithm has a simple iterative structure, with each agent maintaining only a small amount of local state. We show that the iterative algorithm ensures two properties as time goes to ∞: consensus (i.e., output of non-faulty agents becomes identical in the time limit), and optimality (in the sense that the output is the optimum of a suitably defined global cost function).

We construct the first fully succinct garbling scheme for RAM programs, assuming the existence of indistinguishability obfuscation for circuits and one-way functions. That is, the size, space requirements, and runtime of the garbled program are the same as those of the input program, up to poly-logarithmic factors and a polynomial in the security parameter. The scheme can be used to construct indistinguishability obfuscators for RAM programs with comparable efficiency, at the price of requiring sub-exponential security of the underlying primitives. In particular, this opens the door to obfuscated computations that are sublinear in the length of their inputs. The scheme builds on the recent schemes of Koppula-Lewko-Waters and Canetti-Holmgren-Jain-Vaikuntanathan [STOC 15]. A key technical challenge here is how to combine the fixed-prefix technique of KLW, which was developed for deterministic programs, with randomized Oblivious RAM techniques. To overcome that, we develop a method for arguing about the indistinguishability of two obfuscated randomized programs that use correlated randomness. Along the way, we also define and construct garbling schemes that offer only partial protection. These may be of independent interest.