OpenRaiser/Intern-Atlas huggingface dataset:
Formal Black-Box Analysis of Routing Protocol Implementations
Venue: arXiv cs.CR 2017
Year: 2017

The Internet infrastructure relies entirely on open standards for its routing protocols. However, the majority of routers on the Internet are closed-source. Hence, there is no straightforward way to analyze them. Specifically, one cannot easily identify deviations of a router's routing functionality from the routing protocol's standard. Such deviations (either deliberate or inadvertent) are particularly important to identify since they may degrade the security or resiliency of the network. A model-based testing procedure is a technique that allows to systematically generate tests based on a model of the system to be tested; thereby finding deviations in the system compared to the model. However, applying such an approach to a complex multi-party routing protocol requires a prohibitively high number of tests to cover the desired functionality. We propose efficient and practical optimizations to the model-based testing procedure that are tailored to the analysis of routing protocols. These optimizations allow to devise a formal black-box method to unearth deviations in closed-source routing protocols' implementations. The method relies only on the ability to test the targeted protocol implementation and observe its output. Identification of the deviations is fully automatic. We evaluate our method against one of the complex and widely used routing protocols on the Internet -- OSPF. We search for deviations in the OSPF implementation of Cisco. Our evaluation identified numerous significant deviations that can be abused to compromise the security of a network. The deviations were confirmed by Cisco. We further employed our method to analyze the OSPF implementation of the Quagga Routing Suite. The analysis revealed one significant deviation. Subsequent to the disclosure of the deviations some of them were also identified by IBM, Lenovo and Huawei in their own products.
Oscillator-based Ising Machine
Venue: arXiv cs.ET 2017
Year: 2017

Many combinatorial optimization problems can be mapped to finding the ground states of the corresponding Ising Hamiltonians. The physical systems that can solve optimization problems in this way, namely Ising machines, have been attracting more and more attention recently. Our work shows that Ising machines can be realized using almost any nonlinear self-sustaining oscillators with logic values encoded in their phases. Many types of such oscillators are readily available for large-scale integration, with potentials in high-speed and low-power operation. In this paper, we describe the operation and mechanism of oscillator-based Ising machines. The feasibility of our scheme is demonstrated through several examples in simulation and hardware, among which a simulation study reports average solutions exceeding those from state-of-art Ising machines on a benchmark combinatorial optimization problem of size 2000.
Compact Environment-Invariant Codes for Robust Visual Place Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Robust visual place recognition (VPR) requires scene representations that are invariant to various environmental challenges such as seasonal changes and variations due to ambient lighting conditions during day and night. Moreover, a practical VPR system necessitates compact representations of environmental features. To satisfy these requirements, in this paper we suggest a modification to the existing pipeline of VPR systems to incorporate supervised hashing. The modified system learns (in a supervised setting) compact binary codes from image feature descriptors. These binary codes imbibe robustness to the visual variations exposed to it during the training phase, thereby, making the system adaptive to severe environmental changes. Also, incorporating supervised hashing makes VPR computationally more efficient and easy to implement on simple hardware. This is because binary embeddings can be learned over simple-to-compute features and the distance computation is also in the low-dimensional hamming space of binary codes. We have performed experiments on several challenging data sets covering seasonal, illumination and viewpoint variations. We also compare two widely used supervised hashing methods of CCAITQ and MLH and show that this new pipeline out-performs or closely matches the state-of-the-art deep learning VPR methods that are based on high-dimensional features extracted from pre-trained deep convolutional neural networks.
A Model for Enhancing Human Behaviour with Security Questions: A Theoretical Perspective
Venue: arXiv cs.CR 2017
Year: 2017

Security questions are one of the mechanisms used to recover passwords. Strong answers to security questions (i.e. high entropy) are hard for attackers to guess or obtain using social engineering techniques (e.g. monitoring of social networking profiles), but at the same time are difficult to remember. Instead, weak answers to security questions (i.e. low entropy) are easy to remember, which makes them more vulnerable to cyber-attacks. Convenience leads users to use the same answers to security questions on multiple accounts, which exposes these accounts to numerous cyber-threats. Hence, current security questions implementations rarely achieve the required security and memorability requirements. This research study is the first step in the development of a model which investigates the determinants that influence users' behavioural intentions through motivation to select strong and memorable answers to security questions. This research also provides design recommendations for novel security questions mechanisms.
Probability Prediction based Reliable Opportunistic (PRO) Routing Algorithm for VANETs
Venue: arXiv cs.NI 2017
Year: 2017

In the Vehicular ad hoc networks (VANETs), due to the high mobility of vehicles, the network parameters change frequently and the information which the sender maintains may outdate when it wants to transmit data packet to the receiver, so for improving the routing effective, we propose the probability prediction based reliable (PRO) opportunistic routing for VANETs. The PRO routing algorithm can predict the variation of Signal to Interference plus Noise Ratio (SINR) and packet queue length (PQL) in the receiver. The prediction results are used to determine the utility of each relaying vehicle in the candidate set. The calculation of the vehicle utility is weight based algorithm and the weights are the variances of SINR and PQL of the candidate relaying vehicles. The relaying priority of each relaying vehicle is determined by the value of the utility. By these innovations, the PRO can achieve better routing performance (such as the packet delivery ratio, the end-to-end delay, and the network throughput) than the SRPE, ExOR (street-centric), and GPSR routing algorithms.
A novel recommendation system to match college events and groups to students
Venue: arXiv cs.IR 2017
Year: 2017

With the recent increase in data online, discovering meaningful opportunities can be time-consuming and complicated for many individuals. To overcome this data overload challenge, we present a novel text-content-based recommender system as a valuable tool to predict user interests. To that end, we develop a specific procedure to create user models and item feature-vectors, where items are described in free text. The user model is generated by soliciting from a user a few keywords and expanding those keywords into a list of weighted near-synonyms. The item feature-vectors are generated from the textual descriptions of the items, using modified tf-idf values of the users' keywords and their near-synonyms. Once the users are modeled and the items are abstracted into feature vectors, the system returns the maximum-similarity items as recommendations to that user. Our experimental evaluation shows that our method of creating the user models and item feature-vectors resulted in higher precision and accuracy in comparison to well-known feature-vector-generating methods like Glove and Word2Vec. It also shows that stemming and the use of a modified version of tf-idf increase the accuracy and precision by 2% and 3%, respectively, compared to non-stemming and the standard tf-idf definition. Moreover, the evaluation results show that updating the user model from usage histories improves the precision and accuracy of the system. This recommender system has been developed as part of the Agnes application, which runs on iOS and Android platforms and is accessible through the Agnes website.
Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection
Venue: arXiv cs.CV 2017
Year: 2017

While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and \break hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis.
Interdependence of clusters measures and distance distribution in compact metric spaces
Venue: arXiv cs.DM 2017
Year: 2017

A compact metric space $(X, ρ)$ is given. Let $μ$ be a Borel measure on $X$. By $r$-cluster we mean a measurable subset of $X$ with diameter at most $r$. A family of $k$ $2r$-clusters is called a $r$-cluster structure of order $k$ if any two clusters from the family are separated by a distance at least $r$. By measure of a cluster structure we mean a sum of clusters measures from the cluster structure. Using the Blaschke selection theorem one can prove that there exists a cluster structure $\mathcal{X}^*$ of maximum measure. We study dependence $μ(\mathcal{X}^*)$ on distance distribution. The main issue is to find restrictions for distance distribution which guarantee that $μ(\mathcal{X}^*)$ is close to $μ(X)$. We propose a discretization of distance distribution and in terms of this discretization obtain a lower bound for $μ(\mathcal{X}^*)$.
By Hook or by Crook: Exposing the Diverse Abuse Tactics of Technical Support Scammers
Venue: arXiv cs.CR 2017
Year: 2017

Technical Support Scams (TSS), which combine online abuse with social engineering over the phone channel, have persisted despite several law enforcement actions. The tactics used by these scammers have evolved over time and they have targeted an ever increasing number of technology brands. Although recent research has provided insights into TSS, these scams have now evolved to exploit ubiquitously used online services such as search and sponsored advertisements served in response to search queries. We use a data-driven approach to understand search-and-ad abuse by TSS to gain visibility into the online infrastructure that facilitates it. By carefully formulating tech support queries with multiple search engines, we collect data about both the support infrastructure and the websites to which TSS victims are directed when they search online for tech support resources. We augment this with a DNS-based amplification technique to further enhance visibility into this abuse infrastructure. By analyzing the collected data, we demonstrate that tech support scammers are (1) successful in getting major as well as custom search engines to return links to websites controlled by them, and (2) they are able to get ad networks to serve malicious advertisements that lead to scam pages. Our study period of 8 months uncovered over 9,000 TSS domains, of both passive and aggressive types, with minimal overlap between sets that are reached via organic search results and sponsored ads. Also, we found over 2,400 support domains which aid the TSS domains in manipulating organic search results. Moreover, we found little overlap with domains that are reached via abuse of domain parking and URL-shortening services which was investigated previously. Thus, investigation of search-and-ad abuse provides new insights into TSS tactics and helps detect previously unknown abuse infrastructure that facilitates these scams.
Generating Functionally Equivalent Programs Having Non-Isomorphic Control-Flow Graphs
Venue: arXiv cs.CR 2017
Year: 2017

One of the big challenges in program obfuscation consists in modifying not only the program's straight-line code (SLC) but also the program's control flow graph (CFG). Indeed, if only SLC is modified, the program's CFG can be extracted and analyzed. Usually, the CFG leaks a considerable amount of information on the program's structure. In this work we propose a method allowing to re-write a code P into a functionally equivalent code P' such that CFG{P} and CFG{P'} are radically different.
Node Primitives: an open end-user programming platform for social robots
Venue: arXiv cs.RO 2017
Year: 2017

With the expected adoption of robots able to seamlessly and intuitively interact with people in real-world scenarios, the need arises to provide non-technically-skilled users with easy-to-understand paradigms for customising robot behaviors. In this paper, we present an interaction design robot programming platform for enabling multidisciplinary social robot research and applications. This platform is referred to Node Primitives (NEP) and consists of two main parts. On the one hand, a ZeroMQ and Python-based distributed software framework has been developed to provide inter-process communication and robot behavior specification mechanisms. On the other hand, a web-based end-user programming (EUP) interface has been developed to allow for an easy and intuitive way of programming and executing robot behaviors. In order to evaluate NEP, we discuss the development of a human-robot interaction application using arm gestures to control robot behaviors. A usability test for the proposed EUP interface is also presented.
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep deterministic policy gradient (DDPG) algorithm, proven to be very successful in simple simulation environments. The algorithm works in an end-to-end fashion in order to control the continuous position of the flippers. This end-to-end approach makes it easy to apply the controller to a wide array of circumstances, but the huge flexibility comes to the cost of an increased difficulty of solution. The complexity of the task is enlarged even more by the fact that real multi-terrain robots move in partially observable environments. Notwithstanding these complications, being able to smoothly control a multi-terrain robot can produce huge benefits in impaired people daily lives or in search and rescue situations.
Dynamic Provable Data Possession Protocols with Public Verifiability and Data Privacy
Venue: arXiv cs.CR 2017
Year: 2017

Cloud storage services have become accessible and used by everyone. Nevertheless, stored data are dependable on the behavior of the cloud servers, and losses and damages often occur. One solution is to regularly audit the cloud servers in order to check the integrity of the stored data. The Dynamic Provable Data Possession scheme with Public Verifiability and Data Privacy presented in ACISP'15 is a straightforward design of such solution. However, this scheme is threatened by several attacks. In this paper, we carefully recall the definition of this scheme as well as explain how its security is dramatically menaced. Moreover, we proposed two new constructions for Dynamic Provable Data Possession scheme with Public Verifiability and Data Privacy based on the scheme presented in ACISP'15, one using Index Hash Tables and one based on Merkle Hash Trees. We show that the two schemes are secure and privacy-preserving in the random oracle model.
J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation
Venue: arXiv cs.LG 2017
Year: 2017

In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches either rely on Visual SLAM systems or on depth estimation models to build 3D maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multi task architectures to both perform scene understanding and depth estimation. We follow their track and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to exploit the joint information of the obstacle detection task, that produces more reliable bounding boxes, with the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD$^{2}$. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multi task methods that jointly perform semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments where a MAV explores an unknown scenario and plans safe trajectories by using our detection model.
On the Beamformed Broadcast Signaling for Millimeter Wave Cell Discovery: Performance Analysis and Design Insight
Venue: arXiv cs.IT 2017
Year: 2017

Availability of abundant spectrum has enabled millimeter wave (mm-wave) as a prominent candidate solution for the next generation cellular networks. Highly directional transmissions are essential for exploitation of mm-wave bands to compensate high propagation loss and attenuation. The directional transmission, nevertheless, necessitates a specific design for mm-wave initial cell discovery, as conventional omni-directional broadcast signaling may fail in delivering the cell discovery information. To address this issue, this paper provides an analytical framework for mm-wave beamformed cell discovery based on an information theoretical approach. Design options are compared considering four fundamental and representative broadcast signaling schemes to evaluate discovery latency and signaling overhead. The schemes are then simulated under realistic system parameters. Analytical and simulation results reveals four key findings: (i) For cell discovery without knowledge of beacon timing, analog/hybrid beamforming performs as well as digital beamforming in terms of cell discovery latency; (ii) Single beam exhaustive scan optimize the latency, however leads to overhead penalty; (iii) Multi-beam simultaneous scan can significantly reduce the overhead, and provide the flexibility to achieve trade-off between the latency and the overhead; (iv) The latency and the overhead are relatively insensitive to extreme low block error rates.
Resource provisioning in Science Clouds: Requirements and challenges
Venue: arXiv cs.DC 2017
Year: 2017

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications.
Intensional Constructed Numbers: Towards Formalizing the Notion of Algorithm
Venue: arXiv cs.LO 2017
Year: 2017

This work is meant to be a step towards the formal definition of the notion of algorithm, in the sense of an equivalence class of programs working "in a similar way". But instead of defining equivalence transformations directly on programs, we look at the computation for each particular argument and give it a structure. This leads to the notion of constructed number: the result of the computation is a constructed number whose constructors (0, successor) carry a history condition (or trace) of their computation. There are equivalence relations on these conditions and on constructed numbers. Two programs are equivalent if they produce equivalent constructed numbers for each argument.
The Benefits of Low Operating Voltage Devices to the Energy Efficiency of Parallel Systems
Venue: arXiv cs.DC 2017
Year: 2017

Programmable circuits such as general-purpose processors or FPGAs have their end-user energy efficiency strongly dependent on the program that they execute. Ultimately, it is the programmer's ability to code and, in the case of general purpose processors, the compiler's ability to translate source code into a sequence of native instructions that make the circuit deliver the expected performance to the end user. This way, the benefits of energy-efficient circuits build upon energy-efficient devices could be obfuscated by poorly written software. Clearly, having well-written software running on conventional circuits is no better in terms of energy efficiency than having poorly written software running on energy-efficient circuits. Therefore, to get the most out of the energy-saving capabilities of programmable circuits that support low voltage operating modes, it is necessary to address software issues that might work against the benefits of operating in such modes.
Detecting Censor Detection
Venue: arXiv cs.CR 2017
Year: 2017

Our goal is to empirically discover how censors react to the introduction of new proxy servers that can be used to circumvent their information controls. We examine a specific case, that of obfuscated Tor bridges, and conduct experiments designed to discover how long it takes censors to block them (if they do block at all). Through a year's worth of active measurements from China, Iran, Kazakhstan, and other countries, we learn when bridges become blocked. In China we found the most interesting behavior, including long and varying delays before blocking, frequent failures during which blocked bridges became reachable, and an advancement in blocking technique midway through the experiment. Throughout, we observed surprising behavior by censors, not in accordance with what we would have predicted, calling into question our assumptions and suggesting potential untapped avenues for circumvention.