Biblio

The problem of securely outsourcing computation has received widespread attention due to the development of cloud computing and mobile devices. In this paper, we first propose a secure verifiable outsourcing algorithm of single modular exponentiation based on the one-malicious model of two untrusted servers. The outsourcer could detect any failure with probability 1 if one of the servers misbehaves. We also present the other verifiable outsourcing algorithm for multiple modular exponentiations based on the same model. Compared with the state-of-the-art algorithms, the proposed algorithms improve both checkability and efficiency for the outsourcer. Finally, we utilize the proposed algorithms as two subroutines to achieve outsource-secure polynomial evaluation and ciphertext-policy attributed-based encryption (CP-ABE) scheme with verifiable outsourced encryption and decryption.

Modern mobile systems such as smartphones, tablets, and wearables contain a plethora of sensors such as camera, microphone, GPS, and accelerometer. Moreover, being mobile, these systems are with the user all the time, e.g., in user's purse or pocket. Therefore, mobile sensors can capture extremely sensitive and private information about the user including daily conversations, photos, videos, and visited locations. Such a powerful sensing capability raises important privacy concerns. To address these concerns, we believe that mobile systems must be equipped with trustworthy sensor notifications, which use indicators such as LED to inform the user unconditionally when the sensors are on. We present Viola, our design and implementation of trustworthy sensor notifications, in which we leverage two novel solutions. First, we deploy a runtime monitor in low-level system software, e.g., in the operating system kernel or in the hypervisor. The monitor intercepts writes to the registers of sensors and indicators, evaluates them against checks on sensor notification invariants, and rejects those that fail the checks. Second, we use formal verification methods to prove the functional correctness of the compilation of our invariant checks from a high-level language. We demonstrate the effectiveness of Viola on different mobile systems, such as Nexus 5, Galaxy Nexus, and ODROID XU4, and for various sensors and indicators, such as camera, microphone, LED, and vibrator. We demonstrate that Viola incurs almost no overhead to the sensor's performance and incurs only small power consumption overhead.

Cloud providers typically implement abstractions for network virtualization on the server, within the operating system that hosts the tenant virtual machines or containers. Despite being flexible and convenient, this approach has fundamental problems: incompatibility with bare-metal support, unnecessary performance overhead, and susceptibility to hypervisor breakouts. To solve these, we propose to offload the implementation of network-virtualization abstractions to the top-of-rack switch (ToR). To show that this is feasible and beneficial, we present VNToR, a ToR that takes over the implementation of the security-group abstraction. Our prototype combines commodity switching hardware with a custom software stack and is integrated in OpenStack Neutron. We show that VNToR can store tens of thousands of access rules, adapts to traffic-pattern changes in less than a millisecond, and significantly outperforms the state of the art.

Network flow classification is fundamental to network management and network security. However, it is challenging to classify network flows at very high line rates while simultaneously preserving user privacy. Machine learning based classification techniques utilize only meta-information of a flow and have been shown to be effective in identifying network flows. We analyze a group of widely used machine learning classifiers, and observe that the effectiveness of different classification models depends highly upon the protocol types as well as the flow features collected from network data.We propose vTC, a design of virtual network functions to flexibly select and apply the best suitable machine learning classifiers at run time. The experimental results show that the proposed NFV for flow classification can improve the accuracy of classification by up to 13%.

A novel approach is developed for analyzing power system vulnerability related to extraordinary events. Vulnerability analyses are necessary for identification of barriers to prevent such events and as a basis for the emergency preparedness. Identification of cause and effect relationships to reveal vulnerabilities related to extraordinary events is a complex and difficult task. In the proposed approach, the analysis starts by identifying the critical consequences. Then the critical contingencies and operating states, and which external threats and causes that may result in such severe consequences, are identified. This is opposed to the traditional risk and vulnerability analysis which starts by analyzing threats and what can happen as a chain of events. The vulnerability analysis methodology is tested and demonstrated on real systems.

The RFID technology has attracted considerable attention in recent years, and brings convenience to supply chain management. In this paper, we concentrate on designing path-checking protocols to check the valid paths in supply chains. By entering a valid path, the check reader can distinguish whether the tags have gone through the path or not. Based on modified schnorr signature scheme, we provide a path-checking method to achieve multi-signatures and final verification. In the end, we conduct security and privacy analysis to the scheme.

Many standard optimization methods for segmentation and reconstruction compute ML model estimates for appearance or geometry of segments, e.g. Zhu-Yuille [23], Torr [20], Chan-Vese [6], GrabCut [18], Delong et al. [8]. We observe that the standard likelihood term in these formu-lations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a strong bias to clusters of equal size [11], which we express as a penalty for KL divergence from a uniform distribution of cardinalities. However, this volumetric bias has been mostly ignored in computer vision. We demonstrate signif- icant artifacts in standard segmentation and reconstruction methods due to this bias. Moreover, we propose binary and multi-label optimization techniques that either (a) remove this bias or (b) replace it by a KL divergence term for any given target volume distribution. Our general ideas apply to continuous or discrete energy formulations in segmenta- tion, stereo, and other reconstruction problems.

Because of poor performance of heuristic algorithms on virtual machine placement problem in cloud environments, a multi-objective constraint optimization model of virtual machine placement is presented, which taking energy consumption and resource wastage as the objective. We solve the model based on the proposed discrete firefly algorithm. It takes firefly's location as the placement result, brightness as the objective value. Its movement strategy makes darker fireflies move to brighter fireflies in solution space. The continuous position after movement is discretized by the proposed discrete strategy. In order to speed up the search for solution, the local search mechanism for the optimal solution is introduced. The experimental results in OpenStack cloud platform show that the proposed algorithm makes less energy consumption and resource wastage compared with other algorithms.

In the pursuit of cyber security for organizations, there are tens of thousands of tools, guidelines, best practices, forensics, platforms, toolkits, diagnostics, and analytics available. However according to the Verizon 2014 Data Breach Report: “after analysing 10 years of data... organizations cannot keep up with cyber crime-and the bad guys are winning.” Although billions are expended worldwide on cyber security, organizations struggle with complexity, e.g., the NISTIR 7628 guidelines for cyber-physical systems are over 600 pages of text. And there is a lack of information visibility. Organizations must bridge the gap between technical cyber operations and the business/social priorities since both sides are essential for ensuring cyber security. Identifying visual structures for information synthesis could help reduce the complexity while increasing information visibility within organizations. This paper lays the foundation for investigating such visual structures by first identifying where current visual structures are succeeding or failing. To do this, we examined publicly available analyses related to three types of security issues: 1) epidemic, 2) cyber attacks on an industrial network, and 3) threat of terrorist attack. We found that existing visual structures are largely inadequate for reducing complexity and improving information visibility. However, based on our analysis, we identified a range of different visual structures, and their possible trade-offs/limitation is framing strategies for cyber policy. These structures form the basis of evolving visualization to support information synthesis for policy actions, which has rarely been done but is promising based on the efficacy of existing visualizations for cyber incident detection, attacks, and situation awareness.

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity. Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata. We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. It significantly outperforms the state of the art represented by the rule-based Wikidata Abuse Filter (0.865 ROC-AUC) and a prototypical vandalism detector recently introduced by Wikimedia within the Objective Revision Evaluation Service (0.859 ROC-AUC).

Privacy analysis is essential in the society. Data privacy preservation for access control, guaranteed service in wireless sensor networks are important parts. In programs' verification, we not only consider about these kinds of safety and liveness properties but some security policies like noninterference, and observational determinism which have been proposed as hyper properties. Fairness is widely applied in verification for concurrent systems, wireless sensor networks and embedded systems. This paper studies verification and analysis for proving security-relevant properties and hyper properties by proposing deductive proof rules under fairness requirements (constraints).

A robust appearance model is usually required in visual tracking, which can handle pose variation, illumination variation, occlusion and many other interferences occurring in video. So far, a number of tracking algorithms make use of image samples in previous frames to update appearance models. There are many limitations of that approach: 1) At the beginning of tracking, there exists no sufficient amount of data for online update because these adaptive models are data-dependent and 2) in many challenging situations, robustly updating the appearance models is difficult, which often results in drift problems. In this paper, we proposed a tracking algorithm based on compressive sensing theory and particle filter framework. Features are extracted by random projection with data-independent basis. Particle filter is employed to make a more accurate estimation of the target location and make much of the updated classifier. The robustness and the effectiveness of our tracker have been demonstrated in several experiments.

This paper presents the Bit Error Rate (BER) performance of the wireless communication system. The complexity of modern wireless communication system are increasing at fast pace. It becomes challenging to design the hardware of wireless system. The proposed system consists of MIMO transmitter and MIMO receiver along with the along with a realistic fading channel. To make the data transmission more secure when the data are passed into channel Crypto-System with Embedded Error Control (CSEEC) is used. The system supports data security and reliability using forward error correction codes (FEC). Security is provided through the use of a new symmetric encryption algorithm, and reliability is provided by the use of FEC codes. The system aims at speeding up the encryption and encoding operations and reduces the hardware dedicated to each of these operations. The proposed system allows users to achieve more security and reliable communication. The proposed BER measurement communication system consumes low power compared to existing systems. Advantage of VLSI based BER measurement it that they can be used in the Real time applications and it provides single chip solution.

The video streaming between the sender and the receiver involves multiple unsecured hops where the video data can be illegally copied if the nodes run malicious forwarding logic. This paper introduces a novel method to stream video data through dual channels using dual data paths. The frames' pixels are also scrambled. The video frames are divided into two frame streams. At the receiver side video is re-constructed and played for a limited time period. As soon as small chunk of merged video is played, it is deleted from video buffer. The approach has been tried to formalize and initial simulation has been done over MATLAB. Preliminary results are optimistic and a refined approach may lead to a formal designing of network layer routing protocol with corrections in transport layer.

Networks are complex and prone to bugs. Existing tools that check configuration files and data-plane state operate offline at timescales of seconds to hours, and cannot detect or prevent bugs as they arise. Is it possible to check network-wide invariants in real time, as the network state evolves? The key challenge here is to achieve extremely low latency during the checks so that network performance is not affected. In this paper, we present a preliminary design, VeriFlow, which suggests that this goal is achievable. VeriFlow is a layer between a software-defined networking controller and network devices that checks for network-wide invariant violations dynamically as each forwarding rule is inserted. Based on an implementation using a Mininet OpenFlow network and Route Views trace data, we find that VeriFlow can perform rigorous checking within hundreds of microseconds per rule insertion.

Effective reasoning about the impact of security policy decisions requires understanding how human users actually behave, rather than assuming desirable but incorrect behavior. Simulation could help with this reasoning, but it requires building computational models of the relevant human behavior and validating that these models match what humans actually do. In this paper we describe our progress on building agent-based models of human behavior with passwords, and we demonstrate how these models reproduce phenomena
shown in the empirical literature.
 

Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.

The advancement of software-defined networking (SDN) technology is highly dependent on the successful transformations from in-house research ideas to real-life products. To enable such transformations, a testbed offering scalable and high fidelity networking environment for testing and evaluating new/existing designs is extremely valuable. Mininet, the most popular SDN emulator by far, is designed to achieve both accuracy and scalability by running unmodified code of network applications in lightweight Linux Containers. How- ever, Mininet cannot guarantee performance fidelity under high workloads, in particular when the number of concurrent active events is more than the number of parallel cores. In this project, we develop a lightweight virtual time system in Linux container and integrate the system with Mininet, so that all the containers have their own virtual clocks rather than using the physical system clock which reflects the se- rialized execution of multiple containers. With the notion of virtual time, all the containers perceive virtual time as if they run independently and concurrently. As a result, inter- actions between the containers and the physical system are artificially scaled, making a network appear to be ten times faster from the viewpoint of applications within the contain- ers than it actually is. We also design an adaptive virtual time scheduling subsystem in Mininet, which is responsible to balance the experiment speed and fidelity. Experimen- tal results demonstrate that embedding virtual time into Mininet significantly enhances its performance fidelity, and therefore, results in a useful platform for the SDN community to conduct scalable experiments with high fidelity.

Realistic and scalable testing systems are critical to evaluate network applications and protocols to ensure successful real system deployments. Container-based network emula- tion is attractive because of the combination of many desired features of network simulators and physical testbeds . The success of Mininet, a popular software- defined networking (SDN) emulation testbed, demonstrates the value of such approach that we can execute unmodified binary code on a large- scale emulated network with lightweight OS-level vir- tualization techniques. However, an ordinary network em- ulator uses the system clock across all the containers even if a container is not being scheduled to run. This leads to the issue of temporal fidelity, especially with high workloads. Virtual time sheds the light on the issue of preserving tem- poral fidelity for large-scale emulation. The key insight is to trade time with system resources via precisely scaling the time of interactions between containers and physical devices by a factor of n, hence, making an emulated network ap- pear to be n times faster from the viewpoints of applications in the container. In this paper, we develop a lightweight Linux-container-based virtual time system and integrate the system to Mininet for fidelity and scalability enhancement. We also design an adaptive time dilation scheduling mod- ule for balancing speed and accuracy. Experimental results demonstrate that (1) with virtual time, Mininet is able to accurately emulate a network n times larger in scale, where n is the scaling factor, with the system behaviors closely match data obtained from a physical testbed; and (2) with the adaptive time dilation scheduling, we reduce the running time by 46% with little accuracy loss. Finally, we present a case study using the virtual-time-enabled Mininet to evalu- ate the limitations of equal-cost multi-path (ECMP) routing in a data center network.

In this paper, we present a formal model for the verification of the DNSsec Protocol in the interactive theorem prover Isabelle/HOL. Relying on the inductive approach to security protocol verification, this formal analysis provides a more expressive representation than the widely accepted model checking analysis. Our mechanized model allows to represent the protocol, all its possible traces and the attacker and his knowledge. The fine grained model allows to show origin authentication, and replay attack prevention. Most prominently, we succeed in expressing Delegation Signatures and proving their authenticity formally.

Autonomic networks and services are exposed to a large variety of security risks. The vulnerability management process plays a crucial role for ensuring their safe configurations and preventing security attacks. We focus in this survey on the assessment of vulnerabilities in autonomic environments. In particular, we analyze current methods and techniques contributing to the discovery, the description and the detection of these vulnerabilities. We also point out important challenges that should be faced in order to fully integrate this process into the autonomic management plane.
 

To improve comprehensive performance of denoising range images, an impulsive noise (IN) denoising method with variable windows is proposed in this paper. Founded on several discriminant criteria, the principles of dropout IN detection and outlier IN detection are provided. Subsequently, a nearest non-IN neighbors searching process and an Index Distance Weighted Mean filter is combined for IN denoising. As key factors of adapatablity of the proposed denoising method, the sizes of two windows for outlier INs detection and INs denoising are investigated. Originated from a theoretical model of invader occlusion, variable window is presented for adapting window size to dynamic environment of each point, accompanying with practical criteria of adaptive variable window size determination. Experiments on real range images of multi-line surface are proceeded with evaluations in terms of computational complexity and quality assessment with comparison analysis among a few other popular methods. It is indicated that the proposed method can detect the impulsive noises with high accuracy, meanwhile, denoise them with strong adaptability with the help of variable window.
 

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

One of the important direction of research in situational awareness is implementation of visual analytics techniques which can be efficiently applied when working with big security data in critical operational domains. The paper considers a visual analytics technique for displaying a set of security metrics used to assess overall network security status and evaluate the efficiency of protection mechanisms. The technique can assist in solving such security tasks which are important for security information and event management (SIEM) systems. The approach suggested is suitable for displaying security metrics of large networks and support historical analysis of the data. To demonstrate and evaluate the usefulness of the proposed technique we implemented a use case corresponding to the Olympic Games scenario.