Biblio

Augmented Reality (AR) is a technique which seamlessly integrate virtual 3D models into the image of the real scenario in real time. Using the QR code as the identification mark, an algorithm is proposed to extract the virtual straight line of QR code and to locate and track the camera based on the hybrid features, thus it avoids the possibility of failure when locating and tracking only by feature points. The experimental results show that the method of combining straight lines with feature points is better than that of using only straight lines or feature points. Further, an AR (Augmented Reality) system is developed.

We demonstrate 5G network slicing as a unique deployment opportunity for information centric networking (ICN), by using a generic service orchestration framework that operates on commodity compute, storage, and bandwidth resource pools to realize ICN service slices. In this demo, we specifically propose a service slice for the IoT Edge network. ICN has often been considered pertinent for IoT use due to its benefits like simpler stacks on resource constrained devices, in-network caching, and in-built data provenance. We use a lightweight ICN stack on IoT devices connected with sensors and actuators to build a network, where clients can set realistic policies using their legacy hand-held devices. We employ name based authentication protocols between the service end-points and IoT devices to allow secure onboarding. The IoT slice co-exists with other service slices that cater to different classes of applications (e.g., bandwidth intensive applications, such as video conferencing) allowing resource management flexibility. Our design creates orchestrated service Edge functions to which the clients connect, and these can in turn utilize in-network stateless functions to perform tasks, such as decision making and analytics using the available compute resources efficiently.

Access to information and data is becoming an essential part of nearly every aspect of modern business operation. Unfortunately, accessing information systems comes with increased chances of intrusion and unauthorized access. Acquiring and maintaining evidence from a computer or networks in the current high-tech world is essential in any comprehensive forensic investigation. Software and hardware tools are used to easily manage the evidence and view all relevant files. In an effort to enhance computer access security, keystroke authentication, is one of the biometric solutions that were proposed as a solution for enhancing users' identification. This research proposes using user's keystroke errors to determine guilt during forensics investigations, where it was found that individuals keystroke patters are repeatable and variant from those of others, and that keystroke patterns are impossible to steal or imitate. So, in this paper, we investigate the effectiveness of relying on ``user's mistakes'' as another behavioral biometric keystroke dynamic.

High-level C++ source code abstractions such as classes and methods greatly assist human analysts and automated algorithms alike when analyzing C++ programs. Unfortunately, these abstractions are lost when compiling C++ source code, which impedes the understanding of C++ executables. In this paper, we propose a system, OOAnalyzer, that uses an innovative new design to statically recover detailed C++ abstractions from executables in a scalable manner. OOAnalyzer's design is motivated by the observation that many human analysts reason about C++ programs by recognizing simple patterns in binary code and then combining these findings using logical inference, domain knowledge, and intuition. We codify this approach by combining a lightweight symbolic analysis with a flexible Prolog-based reasoning system. Unlike most existing work, OOAnalyzer is able to recover both polymorphic and non-polymorphic C++ classes. We show in our evaluation that OOAnalyzer assigns over 78% of methods to the correct class on our test corpus, which includes both malware and real-world software such as Firefox and MySQL. These recovered abstractions can help analysts understand the behavior of C++ malware and cleanware, and can also improve the precision of program analyses on C++ executables.

As mobile devices and location-based services become increasingly ubiquitous, the privacy of mobile users' location traces continues to be a major concern. Traditional privacy solutions rely on perturbing each position in a user's trace and replacing it with a fake location. However, recent studies have shown that such point-based perturbation of locations is susceptible to inference attacks and suffers from serious utility losses, because it disregards the moving trajectory and continuity in full location traces. In this paper, we argue that privacy-preserving synthesis of complete location traces can be an effective solution to this problem. We present AdaTrace, a scalable location trace synthesizer with three novel features: provable statistical privacy, deterministic attack resilience, and strong utility preservation. AdaTrace builds a generative model from a given set of real traces through a four-phase synthesis process consisting of feature extraction, synopsis learning, privacy and utility preserving noise injection, and generation of differentially private synthetic location traces. The output traces crafted by AdaTrace preserve utility-critical information existing in real traces, and are robust against known location trace attacks. We validate the effectiveness of AdaTrace by comparing it with three state of the art approaches (ngram, DPT, and SGLT) using real location trace datasets (Geolife and Taxi) as well as a simulated dataset of 50,000 vehicles in Oldenburg, Germany. AdaTrace offers up to 3-fold improvement in trajectory utility, and is orders of magnitude faster than previous work, while preserving differential privacy and attack resilience.

Current BLE transmitters are susceptible to selective jamming due to long dwell times in a channel. To mitigate these attacks, we propose physical-layer security through an ultra-fast bit-level frequency-hopping (FH) scheme by exploiting the frequency agility of bulk acoustic wave resonators (BAW). Here we demonstrate the first integrated bit-level FH transmitter (TX) that hops at 1$μ$s period and uses data-driven random dynamic channel selection to enable secure wireless communications with additional data encryption. This system consists of a time-interleaved BAW-based TX implemented in 65nm CMOS technology with 80MHz coverage in the 2.4GHz ISM band and a measured power consumption of 10.9mW from 1.1V supply.

With the frequent use of Wi-Fi and hotspots that provide a wireless Internet environment, awareness and threats to wireless AP (Access Point) security are steadily increasing. Especially when using unauthorized APs in company, government and military facilities, there is a high possibility of being subjected to various viruses and hacking attacks. It is necessary to detect unauthorized Aps for protection of information. In this paper, we use RTT (Round Trip Time) value data set to detect authorized and unauthorized APs in wired / wireless integrated environment, analyze them using machine learning algorithms including SVM (Support Vector Machine), C4.5, KNN (K Nearest Neighbors) and MLP (Multilayer Perceptron). Overall, KNN shows the highest accuracy.

Deprecation is a language feature that allows API producers to mark a feature as obsolete. We aim to gain a deep understanding of the needs of API producers and consumers alike regarding deprecation. To that end, we investigate why API producers deprecate features, whether they remove deprecated features, how they expect consumers to react, and what prompts an API consumer to react to deprecation. To achieve this goal we conduct semi-structured interviews with 17 third-party Java API producers and survey 170 Java developers. We observe that the current deprecation mechanism in Java and the proposal to enhance it does not address all the needs of a developer. This leads us to propose and evaluate three further enhancements to the deprecation mechanism.

Transition noise and remanence noise are the two most important types of media noise in heat-assisted magnetic recording. We examine two methods (spatial splitting and principal components analysis) to distinguish them: both techniques show similar trends with respect to applied field and grain pitch (GP). It was also found that PW50can be affected by GP and reader design, but is almost independent of write field and bit length (larger than 50 nm). Interestingly, our simulation shows a linear relationship between jitter and PW50NSRrem, which agrees qualitatively with experimental results.

The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application—Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages ($\backslash$textasciitilde20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.

This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea.

This article examines Usage of Game Theory in The Internet Wide Scan. There is compiled model of “Network Scanning” game. There is described process of players interaction in the coalition antagonistic and network games. The concept of target system's cost is suggested. Moreover, there is suggested its application in network scanning, particularly, when detecting honeypot/honeynet systems.

Program analyses necessarily make approximations that often lead them to report true alarms interspersed with many false alarms. We propose a new approach to leverage user feedback to guide program analyses towards true alarms and away from false alarms. Our approach associates each alarm with a confidence value by performing Bayesian inference on a probabilistic model derived from the analysis rules. In each iteration, the user inspects the alarm with the highest confidence and labels its ground truth, and the approach recomputes the confidences of the remaining alarms given this feedback. It thereby maximizes the return on the effort by the user in inspecting each alarm. We have implemented our approach in a tool named Bingo for program analyses expressed in Datalog. Experiments with real users and two sophisticated analyses–-a static datarace analysis for Java programs and a static taint analysis for Android apps–-show significant improvements on a range of metrics, including false alarm rates and number of bugs found.

Immersive technologies have been touted as empathetic mediums. This capability has yet to be fully explored through machine learning integration. Our demo seeks to explore proxemics in mixed-reality (MR) human-human interactions. The author developed a system, where spatial features can be manipulated in real time by identifying emotions corresponding to unique combinations of facial micro-expressions and tonal analysis. The Magic Leap One is used as the interactive interface, the first commercial spatial computing head mounted (virtual retinal) display (HUD). A novel spatial user interface visualization element is prototyped that leverages the affordances of mixed-reality by introducing both a spatial and affective component to interfaces.

In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.

In recent years, new and devastating cyber attacks amplify the need for robust cybersecurity practices. Preventing novel cyber attacks requires the invention of Intrusion Detection Systems (IDSs), which can identify previously unseen attacks. Many researchers have attempted to produce anomaly - based IDSs, however they are not yet able to detect malicious network traffic consistently enough to warrant implementation in real networks. Obviously, it remains a challenge for the security community to produce IDSs that are suitable for implementation in the real world. In this paper, we propose a new approach using a Deep Belief Network with a combination of supervised and unsupervised machine learning methods for port scanning attacks detection - the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. Our proposed approach will be tested with network security datasets and compared with previously existing methods.

This article shows the analogy between natural language texts and quantum-like systems on the example of the Bell test calculating. The applicability of the well-known Bell test for texts in Russian is investigated. The possibility of using this test for the text separation on the topics corresponding to the user query in information retrieval system is shown.

Moving target defense (MTD) is becoming popular with the advancements in Software Defined Networking (SDN) technologies. With centralized management through SDN, changing the network attributes such as routes to escape from attacks is simple and fast. Yet, the available alternate routes are bounded by the network topology, and a persistent attacker that continuously perform the reconnaissance can extract the whole link-map of the network. To address this issue, we propose to use virtual shadow networks (VSNs) by applying Network Function Virtualization (NFV) abilities to the network in order to deceive attacker with the fake topology information and not reveal the actual network topology and characteristics. We design this approach under a formal framework for Internet Service Provider (ISP) networks and apply it to the recently emerged indirect DDoS attacks, namely Crossfire, for evaluation. The results show that attacker spends more time to figure out the network behavior while the costs on the defender and network operations are negligible until reaching a certain network size.

NIST, in collaboration with Vanderbilt University, has assembled an open-source tool set for designing and implementing federated, collaborative and interactive experiments with cyber-physical systems (CPS). These capabilities are used in our research on CPS at scale for Smart Grid, Smart Transportation, IoT and Smart Cities. This tool set, "Universal CPS Environment for Federation (UCEF)," includes a virtual machine (VM) to house the development environment, a graphical experiment designer, a model repository, and an initial set of integrated tools including the ability to compose Java, C++, MATLABTM, OMNeT++, GridLAB-D, and LabVIEWTM based federates into consolidated experiments. The experiments themselves are orchestrated using a ‘federation manager federate,’ and progressed using courses of action (COA) experiment descriptions. UCEF utilizes a method of uniformly wrapping federates into a federation. The UCEF VM is an integrated toolset for creating and running these experiments and uses High Level Architecture (HLA) Evolved to facilitate the underlying messaging and experiment orchestration. Our paper introduces the requirements and implementation of the UCEF technology and indicates how we intend to use it in CPS Measurement Science.

Accountability is the property of a system that enables the uncovering of causes for events and helps understand who or what is responsible for these events. Definitions and interpretations of accountability differ; however, they are typically expressed in natural language that obscures design decisions and the impact on the overall system. This paper presents a formal model to express the accountability properties of cyber-physical systems. To illustrate the usefulness of our approach, we demonstrate how three different interpretations of accountability can be expressed using the proposed model and describe the implementation implications through a case study. This formal model can be used to highlight context specific-elements of accountability mechanisms, define their capabilities, and express different notions of accountability. In addition, it makes design decisions explicit and facilitates discussion, analysis and comparison of different approaches.

In online payment, customers must transmit their personal and financial information through the website to conclude their purchase and pay the services or items selected. They may face possible fears from online transactions raised by their risk perception about financial or privacy loss. They may have concerns over the payment decision with the possible negative behaviors such as shopping cart abandonment. Therefore, customers have three major players that need to be addressed in online payment: the online seller, the payment page, and their own perception. However, few studies have explored these three players in an online purchasing environment. In this paper, we focus on the customer concerns and examine the antecedents of trust, payment security perception as well as their joint effect on two fundamentally important customers' aspects privacy concerns and financial fear perception. A total of 392 individuals participated in an online survey. The results highlight the importance, of the seller website's components (such as ease of use, security signs, and quality information) and their impact on the perceived payment security as well as their impact on customer's trust and financial fear perception. The objective of our study is to design a research model that explains the factors contributing to an online payment decision.

This paper aims to discover the characteristics of acceptance of mobile government systems by elderly. Several initiatives and projects offer various governmental services for them, like information sharing, alerting and mHealth services. All of them carry important benefits for this user group, but these can only be utilized if the user acceptance is at a certain level. This is a requirement in order for the users to perceive the services as a benefit and not as hindrance. The key aspects for high acceptance are usability and user-friendliness, which will lead to successful-government systems designed for the target group. We have applied a combination of qualitative and quantitative research methods including an m-Government prototype to explore the key acceptance factors. Research approach utilizes the IGUAN framework, which is a user-driven method. We collected and analysed data guided by IGUAN framework about the acceptance of e-government services by elderly. The target group was recruited from Germany and Hungary. Our findings draw the attention to perceived security and perceived usability of an application; these are decisive factors for this target group.

Attackers can leverage several techniques to compromise computer networks, ranging from sophisticated malware to DDoS (Distributed Denial of Service) attacks that target the application layer. Application layer DDoS attacks, such as Slow Read, are implemented with just enough traffic to tie up CPU or memory resources causing web and application servers to go offline. Such attacks can mimic legitimate network requests making them difficult to detect. They also utilize less volume than traditional DDoS attacks. These low volume attack methods can often go undetected by network security solutions until it is too late. In this paper, we explore the use of machine learners for detecting Slow Read DDoS attacks on web servers at the application layer. Our approach uses a generated dataset based upon Netflow data collected at the application layer on a live network environment. Our Netflow data uses the IP Flow Information Export (IPFIX) standard providing significant flexibility and features. These Netflow features can process and handle a growing amount of traffic and have worked well in our previous DDoS work detecting evasion techniques. Our generated dataset consists of real-world network data collected from a production network. We use eight different classifiers to build Slow Read attack detection models. Our wide selection of learners provides us with a more comprehensive analysis of Slow Read detection models. Experimental results show that the machine learners were quite successful in identifying the Slow Read attacks with a high detection and low false alarm rate. The experiment demonstrates that our chosen Netflow features are discriminative enough to detect such attacks accurately.

This paper describes a method which combines Topological Vulnerability Analysis (TVA) with cyber, electromagnetic, and physical/kinetic attack types, attack sources, cross-domain effects, graph analytics, and Bayesian analysis in order to enable systems engineers and cyber defense experts to comprehensively perform vulnerability analysis of Cyber-Physical Systems (CPS) as well as information and communications technology (ICT) supply chains.

Multi-Objective Recommender Systems (MO-RS) consider several objectives to produce useful recommendations. Besides accuracy, other important quality metrics include novelty and diversity of recommended lists of items. Previous research up to this point focused on naive combinations of objectives. In this paper, we present a new and adaptable strategy for prioritizing objectives focused on users' preferences. Our proposed strategy is based on meta-features, i.e., characteristics of the input data that are influential in the final recommendation. We conducted a series of experiments on three real-world datasets, from which we show that: (i) the use of meta-features leads to the improvement of the Pareto solution set in the search process; (ii) the strategy is effective at making choices according to the specificities of the users' preferences; and (iii) our approach outperforms state-of-the-art methods in MO-RS.