Biblio

This study builds a foundation for improving research for first responder communication and situational awareness technology in the future. In an online survey, we elicited the opinions of 250 U.S. first responders about effectiveness, security, and reliability of past, current, and future Internet of Things technology. The most desired features respondents identified were connectivity, reliability, interoperability, and affordability. The top barriers to technology adoption and use included restricted budgets/costs, interoperability, insufficient training resources, and insufficient interagency collaboration and communication. First responders in all job types indicated that technology has made first responder equipment more useful, and technology that supports situational awareness is particularly valued. As such, future Internet of Things capabilities, such as tapping into smart device data in residences and piggybacking onto alternative communication channels, could be valuable for future first responders. Potential areas for future investigation are suggested for technology development and research.

Our previous work, which can be referred to as EMPRESS 1.0, showed that rich metadata management provides a relatively low-overhead approach to facilitating insight from scale-up scientific applications. However, this system did not provide the functionality needed for a viable production system or address whether such a system could scale. Therefore, we have extended our previous work to create EMPRESS 2.0, which incorporates the features required for a useful production system. Through a discussion of EMPRESS 2.0, this paper explores how to incorporate rich query functionality, fault tolerance, and atomic operations into a scalable, storage system independent metadata management system that is easy to use. This paper demonstrates that such a system offers significant performance advantages over HDF5, providing metadata querying that is 150X to 650X faster, and can greatly accelerate post-processing. Finally, since the current implementation of EMPRESS 2.0 relies on an RDBMS, this paper demonstrates that an RDBMS is a viable technology for managing data-oriented metadata.

Binary particle swarm optimization (BPSO) is a technique widely used to solve combinatorial problems. In this paper, we propose a variant of BPSO to find most likely attack paths in an attack graph. The aim is to find an attack path with the highest attack probability and least path length. In such combinatorial optimization problem, the set of feasible solutions is usually discrete and an exhaustive search may lead to unnecessary examination of those segments of the search space, which are assured to not include a solution. The paper introduces the concept of bounding the solution space of BPSO. The minimum and maximum value of each objective called bound of the solution is computed. The search space of BPSO is restricted within these solution bounds and hence we name our approach as bounded binary particle swarm optimization (BBPSO). By bounding the solution space, those particles of BPSO which are guaranteed to be infeasible are not considered for feasibility check. Experimental results show that the proposed approach provide a 50 percent performance improvement as compared to the conventional BPSO.

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.

Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use sensor resources. In this work, we propose VirtSense, an ARM TrustZone based virtual sensing system, to provide each sensing application a virtual sensor instance, which further enables a safe, flexible and isolated sensing environment on the IoT devices. Our preliminary results show that VirtSense: 1) can provide virtual sensor instance for each sensing application so that the sensing needs of each application will be satisfied without affecting others; 2) is able to enforce access control policy even under an untrusted environment.

This paper is aimed at proposing a visual MapReduce program development environment called VMR for heterogeneous computing on Clouds. This development environment mainly has three advantages as follows. First, it allows users to drag and drop graphical blocks instead of text typing for editing programs. Therefore, users can save their effort and time spent on MapReduce programming especially when they analyze data on clouds through mobile devices. Second, it can automatically translate the blocks of users' MapReduce programs into three different versions including Java, C and CUDA of source codes, and select one of these three versions according to the processor architecture of allocated resources for execution. Consequently, users can transparently and effectively exploit heterogeneous resources in clouds for executing their MapReduce programs while they has no need to individually write programs for each of different processor architectures by themselves. Third, it can enable clouds to outsource the computation tasks of MapReduce programs to mobile devices in order for increasing job throughput or program performance.

Visual Text Analytics has been an active area of interdisciplinary research (http://textvis.lnu.se/). This interactive tutorial is designed to give attendees an introduction to the area of information visualization, with a focus on linguistic visualization. After an introduction to the basic principles of information visualization and visual analytics, this tutorial will give an overview of the broad spectrum of linguistic and text visualization techniques, as well as their application areas [3]. This will be followed by a hands-on session that will allow participants to design their own visualizations using tools (e.g., Tableau), libraries (e.g., d3.js), or applying sketching techniques [4]. Some sample datasets will be provided by the instructor. Besides general techniques, special access will be provided to use the VisArgue framework [1] for the analysis of selected datasets.

The central building block of secure and privacy-preserving Vehicular Communication (VC) systems is a Vehicular Public-Key Infrastructure (VPKI), which provides vehicles with multiple anonymized credentials, termed pseudonyms. These pseudonyms are used to ensure message authenticity and integrity while preserving vehicle (and thus passenger) privacy. In the light of emerging large-scale multi-domain VC environments, the efficiency of the VPKI and, more broadly, its scalability are paramount. In this extended abstract, we leverage the state-of-the-art VPKI system and enhance its functionality towards a highly-available and dynamically-scalable design; this ensures that the system remains operational in the presence of benign failures or any resource depletion attack, and that it dynamically scales out, or possibly scales in, according to the requests' arrival rate. Our full-blown implementation on the Google Cloud Platform shows that deploying a VPKI for a large-scale scenario can be cost-effective, while efficiently issuing pseudonyms for the requesters.

In order to make up for the shortcomings of traditional evaluation methods neglecting node difference, a vulnerability assessment method considering node heterogeneity for cyber physical power system (CPPS) is proposed. Based on the entropy of the power flow and complex network theory, we establish heterogeneity evaluation index system for CPPS, which considers the survivability of island survivability and short-term operation of the communication network. For mustration, hierarchical CPPS model and distributed CPPS model are established respectively based on partitioning characteristic and different relationships of power grid and communication network. Simulation results show that distributed system is more robust than hierarchical system of different weighting factor whether under random attack or deliberate attack and a hierarchical system is more sensitive to the weighting factor. The proposed method has a better recognition effect on the equilibrium of the network structure and can assess the vulnerability of CPPS more accurately.

Website defacements can inflict significant harm on the website owner through the loss of reputation, the loss of money, or the leakage of information. Due to the complexity and diversity of all kinds of web application systems, especially a lack of necessary security maintenance, website defacements increased year by year. In this paper, we focus on detecting whether the website has been defaced by extracting website features and website embedded trojan features. We use three kinds of classification learning algorithms which include Gradient Boosting Decision Tree (GBDT), Random Forest (RF) and Support Vector Machine (SVM) to do the classification experiments, and experimental results show that Support Vector Machine classifier performed better than two other classifiers. It can achieve an overall accuracy of 95%-96% in detecting website defacements.

Ciphertext-policy attribute-based encryption (CP-ABE) has been proposed to enable fine-grained access control on encrypted data for cloud storage service. In the context of CP-ABE, since the decryption privilege is shared by multiple users who have the same attributes, it is difficult to identify the original key owner when given an exposed key. This leaves the malicious cloud users a chance to leak their access credentials to outsourced data in clouds for profits without the risk of being caught, which severely damages data security. To address this problem, we add the property of traceability to the conventional CP-ABE. To catch people leaking their access credentials to outsourced data in clouds for profits effectively, in this paper, we first propose two kinds of non-interactive commitments for traitor tracing. Then we present a fully secure traceable CP-ABE system for cloud storage service from the proposed commitment. Our proposed commitments for traitor tracing may be of independent interest, as they are both pairing-friendly and homomorphic. We also provide extensive experimental results to confirm the feasibility and efficiency of the proposed solution.

Despite several attacks have been proposed, text-based CAPTCHAs are still being widely used as a security mechanism. One of the reasons for the pervasive use of text captchas is that many of the prior attacks are scheme-specific and require a labor-intensive and time-consuming process to construct. This means that a change in the captcha security features like a noisier background can simply invalid an earlier attack. This paper presents a generic, yet effective text captcha solver based on the generative adversarial network. Unlike prior machine-learning-based approaches that need a large volume of manually-labeled real captchas to learn an effective solver, our approach requires significantly fewer real captchas but yields much better performance. This is achieved by first learning a captcha synthesizer to automatically generate synthetic captchas to learn a base solver, and then fine-tuning the base solver on a small set of real captchas using transfer learning. We evaluate our approach by applying it to 33 captcha schemes, including 11 schemes that are currently being used by 32 of the top-50 popular websites including Microsoft, Wikipedia, eBay and Google. Our approach is the most capable attack on text captchas seen to date. It outperforms four state-of-the-art text-captcha solvers by not only delivering a significant higher accuracy on all testing schemes, but also successfully attacking schemes where others have zero chance. We show that our approach is highly efficient as it can solve a captcha within 0.05 second using a desktop GPU. We demonstrate that our attack is generally applicable because it can bypass the advanced security features employed by most modern text captcha schemes. We hope the results of our work can encourage the community to revisit the design and practical use of text captchas.

Personal information is typically fragmented across multiple, heterogeneous, distributed sources and saved as small, heterogeneous data objects, or traces. The DigitalSelf project at Rutgers University focuses on developing tools and techniques to manage (organize, search, summarize, make inferences on and personalize) such heterogeneous collections of personal digital traces. We propose to demonstrate YourDigitalSelf, a mobile phone-based personal information organization application developed as part of the DigitalSelf project. The demonstration will use a sample user data set to show how several disparate data traces can be integrated and combined to create personal narratives, or coherent episodes, of the user's activities. Conference attendees will be given the option to install YourDigitalSelf on their own devices to interact with their own data.

Today's rapidly growing document volumes pose pressing challenges to modern document analytics frameworks, in both space usage and processing time. Recently, a promising method, called text analytics directly on compressed data (TADOC), was proposed for improving both the time and space efficiency of text analytics. The main idea of the technique is to enable direct document analytics on compressed data. This paper focuses on the programming challenges for developing efficient TADOC programs. It presents Zwift, the first programming framework for TADOC, which consists of a Domain Specific Language, a compiler and runtime, and a utility library. Experiments show that Zwift significantly improves programming productivity, while effectively unleashing the power of TADOC, producing code that reduces storage usage by 90.8% and execution time by 41.0% on six text analytics problems.

3D modeling usually refers to be the use of 3D software to build production through the virtual 3D space model with 3D data. At present, most 3D modeling software such as 3dmax, FLAC3D and Midas all need adjust models to get a satisfactory model or by coding a precise modeling. There are many matters such as complicated steps, strong profession, the high modeling cost. Aiming at this problem, the paper presents a new 3D modeling methods which is based on Deep Belief Networks (DBN) and Interactive Evolutionary Algorithm (IEA). Following this method, firstly, extract characteristic vectors from vertex, normal, surfaces of the imported model samples. Secondly, use the evolution strategy, to extract feature vector for stochastic evolution by artificial grading control the direction of evolution, and in the process to extract the characteristics of user preferences. Then, use evolution function matrix to establish the fitness approximation evaluation model, and simulate subjective evaluation. Lastly, the user can control the whole machine simulation evaluation process at any time, and get a satisfactory model. The experimental results show that the method in this paper is feasible.

In this work, an approach for the automatic analysis of people trajectories is presented, using a multi-camera and card reader system. Data is first extracted from surveillance cameras and card readers to create trajectories which are sequences of paths and activities. A distance model is proposed to compare sequences and calculate similarities. The popular unsupervised model One-Class Support Vector Machine (One-Class SVM) is used to train a detector. The proposed method classifies trajectories as normal or abnormal and can be used in two modes: off-line and real-time. Experiments are based on data simulation corresponding to an attack scenario proposed by a security expert. Results show that the proposed method successfully detects the abnormal sequences in the scenario with very low false alarm rate.

The analysis of security-related event logs is an important step for the investigation of cyber-attacks. It allows tracing malicious activities and lets a security operator find out what has happened. However, since IT landscapes are growing in size and diversity, the amount of events and their highly different representations are becoming a Big Data challenge. Unfortunately, current solutions for the analysis of security-related events, so called Security Information and Event Management (SIEM) systems, are not able to keep up with the load. In this work, we propose a distributed SIEM platform that makes use of highly efficient distributed normalization and persists event data into an in-memory database. We implement the normalization on common distribution frameworks, i.e. Spark, Storm, Trident and Heron, and compare their performance with our custom-built distribution solution. Additionally, different tuning options are introduced and their speed advantage is presented. In the end, we show how the writing into an in-memory database can be tuned to achieve optimal persistence speed. Using the proposed approach, we are able to not only fully normalize, but also persist more than 20 billion events per day with relatively small client hardware. Therefore, we are confident that our approach can handle the load of events in even very large IT landscapes.

The concept of Internet of Things (IoT) has received considerable attention and development in recent years. There have been significant studies on access control models for IoT in academia, while companies have already deployed several cloud-enabled IoT platforms. However, there is no consensus on a formal access control model for cloud-enabled IoT. The access-control oriented (ACO) architecture was recently proposed for cloud-enabled IoT, with virtual objects (VOs) and cloud services in the middle layers. Building upon ACO, operational and administrative access control models have been published for virtual object communication in cloud-enabled IoT illustrated by a use case of sensing speeding cars as a running example. In this paper, we study AWS IoT as a major commercial cloud-IoT platform and investigate its suitability for implementing the afore-mentioned academic models of ACO and VO communication control. While AWS IoT has a notion of digital shadows closely analogous to VOs, it lacks explicit capability for VO communication and thereby for VO communication control. Thus there is a significant mismatch between AWS IoT and these academic models. The principal contribution of this paper is to reconcile this mismatch by showing how to use the mechanisms of AWS IoT to effectively implement VO communication models. To this end, we develop an access control model for virtual objects (shadows) communication in AWS IoT called AWS-IoT-ACMVO. We develop a proof-of-concept implementation of the speeding cars use case in AWS IoT under guidance of this model, and provide selected performance measurements. We conclude with a discussion of possible alternate implementations of this use case in AWS IoT.

Outsourcing the decryption of attribute-based encryption (ABE) ciphertext is a promising way to tackle the question of how users can perform decryption efficiently. However, existing solutions require the type of the target ciphertext to be determined at the setup of the outsourcing scheme. As such, making the target cryptosystems (or the clients) to be versatile becomes an issue that warrants investigations. In this paper, the problem we wish to tackle is to transform an ABE ciphertext to any client who is using the same, or possibly different, public-key encryption (PKE) system with the sender. The problem is of practical interest since it is hard to require all clients to use the same PKE, especially in the case of remote and cross-system data sharing. In addition, we also consider whether robust client-side decryption scheme can be adopted. This feature is not supported in the existing ABE with outsourcing. We introduce cross-system proxy re-encryptions (CS-PRE), a new re-encryption paradigm in which a semi-trusted proxy converts a ciphertext of a source cryptosystem (\$\textparagraphi\_0\$) into a ciphertext for a target cryptosystem (\$\textparagraphi\$). We formalize CS-PRE and present a construction that performs well in the following aspects. (1)Versatility: \$\textparagraphi\_0\$ can be any attribute-based encryption (ABE) within Attrapadung's pair encoding framework. \$\textparagraphi\$ can be any public-key encryption. Furthermore, the keys and public parameters can be generated independently. (2) Compatibility: CS-PRE does not modify the public parameters and keys of \$\textparagraphi\_0\$ and \$\textparagraphi\$. Besides, input for the conversion is an ordinary ciphertext of \$\textparagraphi\_0\$. (3) Efficiency: The computational cost for re-encryption and decryption of the re-encrypted ciphertext are roughly the same as a decryption in \$\textparagraphi\_0\$ and \$\textparagraphi\$ respectively. We prove that our construction is fully secure assuming \$\textparagraphi\_0\$ is secure in Attrapadung's framework and \$\textparagraphi\$ is IND-CPA secure. Furthermore, it remains secure when there are multiple target cryptosystems. As with other proxy re-encryption, CS-PRE enables flexible sharing of cloud data, as the owner can instruct the cloud server to re-encrypt his ciphertext to those for the intended recipient. In addition, it allows lightweight devices to enjoy access to remote data encrypted under powerful but possibly costly encryption, such as functional encryption, by utilizing the server's power in converting the ciphertext to a simpler encryption, such as RSA. Finally, instances of CS-PRE can be viewed as new proxy re-encryption schemes, such as a PRE supporting ABE for regular language to Hierarchical IBE or Doubly Spatial Encryption to lattice-based encryptions (e.g. NTRUCCA).

The huge amount of generated data offers special advantages mainly in dynamic and scalable systems. In fact, the data generator entities need to share the generated data with each other which leads to the use of cloud services. A cloud server is considered as an untrusted entity that offers many advantages such as large storing space, computation speed... etc. Hence, there is a need to cope with how to protect the stored data in the cloud server by proposing adaptive solutions. The main objective is how to provide an encryption scheme allowing the user to maintains some functions such as addition, multiplication and to preserve the order on the encrypted cloud data. Many algorithms and techniques are designed to manipulate the stored encrypted cloud data. This paper presents an adaptive and efficient fully homomorphic encryption technique to protect the user's data stored in the cloud, where the cloud server executes simple operations.

Attackers create new threats and constantly change their behavior to mislead security systems. In this paper, we propose an adaptive threat detection architecture that trains its detection models in real time. The major contributions of the proposed architecture are: i) gather data about zero-day attacks and attacker behavior using honeypots in the network; ii) process data in real time and achieve high processing throughput through detection schemes implemented with stream processing technology; iii) use of two real datasets to evaluate our detection schemes, the first from a major network operator in Brazil and the other created in our lab; iv) design and development of adaptive detection schemes including both online trained supervised classification schemes that update their parameters in real time and learn zero-day threats from the honeypots, and online trained unsupervised anomaly detection schemes that model legitimate user behavior and adapt to changes. The performance evaluation results show that proposed architecture maintains an excellent trade-off between threat detection and false positive rates and achieves high classification accuracy of more than 90%, even with legitimate behavior changes and zero-day threats.

Image hiding is the important tools to protect the ownership rights of digital multimedia contents. To reduce the interference effect of the host signal in the popular Spread Spectrum (SS) image hiding algorithm, this paper proposes an Improved Additive Spread Spectrum (IASS) image hiding algorithm. The proposed IASS image hiding algorithm maintains the simple decoder of the Additive Spread Spectrum (ASS) image hiding algorithm. This paper makes the comparative experiments with the ASS image hiding algorithm and Correlation-and-bit-Aware Spread Spectrum (CASS) image hiding algorithm. For the noise-free scenario, the proposed IASS image hiding algorithm could yield error-free decoding performance in theory. For the noise scenario, the experimental results show that the proposed IASS image hiding algorithm could significantly reduce the host effect in data hiding and improve the watermark decoding performance remarkably.

As a new research hotspot in the field of artificial intelligence, deep reinforcement learning (DRL) has achieved certain success in various fields such as robot control, computer vision, natural language processing and so on. At the same time, the possibility of its application being attacked and whether it have a strong resistance to strike has also become a hot topic in recent years. Therefore, we select the representative Deep Q Network (DQN) algorithm in deep reinforcement learning, and use the robotic automatic pathfinding application as a countermeasure application scenario for the first time, and attack DQN algorithm against the vulnerability of the adversarial samples. In this paper, we first use DQN to find the optimal path, and analyze the rules of DQN pathfinding. Then, we propose a method that can effectively find vulnerable points towards White-Box Q table variation in DQN pathfinding training. Finally, we build a simulation environment as a basic experimental platform to test our method, through multiple experiments, we can successfully find the adversarial examples and the experimental results show that the supervised method we proposed is effective.

Machine learning algorithms including Deep Neural Networks (DNNs) have shown great success in many different areas. However, they are frequently susceptible to adversarial examples, which are maliciously crafted inputs to fool machine learning classifiers. On the other hand, humans cannot distinguish between non-adversarial and adversarial inputs. In this work, we focus on creating adversarial examples to change the polarity of positive and negative reviews with Amazon product review dataset. We introduce a simple heuristics algorithm to construct adversarial product reviews by replacing words with semantically and synthetically similar synonyms. We evaluate our approach against the state-of-the-art CNN-BLSTM classifier. Our preliminary results show the performance drop of the classifier against the adversarial examples. We also present the defense mechanism using adversarial training.