Biblio

The use of Knuth's Rule and Bayesian Blocks constant piecewise models for characterization of RFID traffic has been proposed already. This study presents an evaluation of the application of those two modeling techniques for various RFID traffic patterns. The data sets used in this study consist of time series of binned RFID command counts. More specifically., we compare the shape of several empirical plots of raw data sets we obtained from experimental RIFD readings., against the constant piecewise graphs produced as an output of the two modeling algorithms. One issue limiting the applicability of modeling techniques to RFID traffic is the fact that there are a large number of various RFID applications available. We consider this phenomenon to present the main motivation for this study. The general expectation is that the RFID traffic traces from different applications would be sequences with different histogram shapes. Therefore., no modeling technique could be considered universal for modeling the traffic from multiple RFID applications., without first evaluating its model performance for various traffic patterns. We postulate that differences in traffic patterns are present if the histograms of two different sets of RFID traces form visually different plot shapes.

In this time of ubiquitous computing and the evolution of the Internet of Things (IoT), the deployment and development of Radio Frequency Identification (RFID) is becoming more extensive. Proving the simultaneous presence of a group of RFID tagged objects is a practical need in many application areas within the IoT domain. Security, privacy, and efficiency are central issues when designing such a grouping-proof protocol. This work is motivated by our serial-dependent and Sundaresan et al.'s grouping-proof protocols. In this paper, we propose a light, improved offline protocol: parallel-dependency grouping-proof protocol (PDGPP). The protocol focuses on security, privacy, and efficiency. PDGPP tackles the challenges of including robust privacy mechanisms and accommodates missing tags. It is scalable and complies with EPC C1G2.

Being able to describe a specific network as consistent is a large step towards resiliency. Next to the importance of security lies the necessity of consistency verification. Attackers are currently focusing on targeting small and crutial goals such as network configurations or flow tables. These types of attacks would defy the whole purpose of a security system when built on top of an inconsistent network. Advances in Artificial Intelligence (AI) are playing a key role in ensuring a fast responce to the large number of evolving threats. Software Defined Networking (SDN), being centralized by design, offers a global overview of the network. Robustness and adaptability are part of a package offered by programmable networking, which drove us to consider the integration between both AI and SDN. The general goal of our series is to achieve an Artificial Intelligence Resiliency System (ARS). The aim of this paper is to propose a new AI-based consistency verification system, which will be part of ARS in our future work. The comparison of different deep learning architectures shows that Convolutional Neural Networks (CNN) give the best results with an accuracy of 99.39% on our dataset and 96% on our consistency test scenario.

Cyber-Physical Systems (CPSs) are engineered systems seamlessly integrating computational algorithms and physical components. CPS advances offer numerous benefits to domains such as health, transportation, smart homes and manufacturing. Despite these advances, the overall cybersecurity posture of CPS devices remains unclear. In this paper, we provide knowledge on how to improve CPS resiliency by evaluating and comparing the accuracy, and scalability of two popular vulnerability assessment tools, Nessus and OpenVAS. Accuracy and suitability are evaluated with a diverse sample of pre-defined vulnerabilities in Industrial Control Systems (ICS), smart cars, smart home devices, and a smart water system. Scalability is evaluated using a large-scale vulnerability assessment of 1,000 Internet accessible CPS devices found on Shodan, the search engine for the Internet of Things (IoT). Assessment results indicate several CPS devices from major vendors suffer from critical vulnerabilities such as unsupported operating systems, OpenSSH vulnerabilities allowing unauthorized information disclosure, and PHP vulnerabilities susceptible to denial of service attacks.

A significant number of the generation, transmission and distribution facilities in the North America were designed and configured for serving electric loads and economic activities under certain reliability and resiliency requirements over 30 years ago. With the changing generation mix, the electric grid is tasked to deliver electricity made by fuel uncertain and energy limited resources. How adequate are the existing facilities to meet the industry expectations on reliability? What level of grid resiliency should be designed and built to sustain reliable electric services given the increasing exposure to frequent and lasting severe weather conditions? There is a need to review the modeling assumptions, operating and maintenance records before we can answer these questions.

This paper presents an analytical method for predicting the electromagnetic performance in permanent magnet (PM) machine with the spoke-type rotor (STR) and a proposed hybrid rotor structure (HRS), respectively. The key of this method is to combine magnetic field analysis model (MFAM) with the magnetic equivalent circuit model. The influence of the irregular PM shape is considered by the segmentation calculation. To obtain the boundary condition in the MFAM, respectively, two equivalent methods on the rotor side are proposed. In the STR, the average flux density of the rotor core outer-surface is calculated to solve the Laplace's equation with considering for the rotor core outer-surface eccentric. In the HRS, based on the Thevenin's theorem, the equivalent parameters of PM remanence BreB and thickness hpme are obtained as a given condition, which can be utilized to compute the air-gap flux density by conventional classic magnetic field analysis model of surface-mounted PMs with air-gap region. Finally, the proposed analytical models are verified by the finite element analysis (FEA) with comparisons of the air-gap flux density, flux linkage, back-EMF and electromagnetic torque, respectively. Furthermore, the performance that the machine with the proposed hybrid structure rotor can improve the torque density as explained.

The understanding of measured jitter is improved in three ways. First, it is shown that the measured jitter is not only governed by written-in jitter and the reader resolution along the cross-track direction but by remanence noise in the vicinity of transitions and the down-track reader resolution as well. Second, a novel data analysis scheme is introduced that allows for an unambiguous separation of these two contributions. Third, based on data analyses involving the first two learnings and micro-magnetic simulations, we identify and explain the root causes for variations of jitter with write current (WC) (write field), WC overshoot amplitude (write-field rise time), and linear disk velocity measured for heat-assisted magnetic recording.

Cloud storage brokerage systems abstract cloud storage complexities by mediating technical and business relationships between cloud stakeholders, while providing value-added services. This however raises security challenges pertaining to the integration of disparate components with sometimes conflicting security policies and architectural complexities. Assessing the security risks of these challenges is therefore important for Cloud Storage Brokers (CSBs). In this paper, we present a threat modeling schema to analyze and identify threats and risks in cloud brokerage brokerage systems. Our threat modeling schema works by generating attack trees, attack graphs, and data flow diagrams that represent the interconnections between identified security risks. Our proof-of-concept implementation employs the Common Configuration Scoring System (CCSS) to support the threat modeling schema, since current schemes lack sufficient security metrics which are imperatives for comprehensive risk assessments. We demonstrate the efficiency of our proposal by devising CCSS base scores for two attacks commonly launched against cloud storage systems: Cloud sStorage Enumeration Attack and Cloud Storage Exploitation Attack. These metrics are then combined with CVSS based metrics to assign probabilities in an Attack Tree. Thus, we show the possibility combining CVSS and CCSS for comprehensive threat modeling, and also show that our schemas can be used to improve cloud security.

Cloud storage backends such as Amazon S3 are a potential storage solution to enterprises. However, to couple enterprises with these backends, at least two problems must be solved: first, how to make these semi-trusted backends as secure as on-premises storage; and second, how to selectively retrieve files as easy as on-premises storage. A security proxy can address both the problems by building a local index from keywords in files before encrypting and uploading files to these backends. But, if the local index is built in plaintext, file content is still vulnerable to local malicious staff. Searchable Encryption (SE) can get rid of this vulnerability by making index into ciphertext; however, its known constructions often require modifications to index database, and, to support wildcard queries, they are not efficient at all. In this paper, we present a security proxy that, based on our wildcard SE construction, can securely and efficiently couple enterprises with these backends. In particular, since our SE construction can work directly with existing database systems, it incurs only a little overhead, and when needed, permits the security proxy to run with constantly small storage footprint by readily out-sourcing all built indices to existing cloud databases.

In stream-based programming, data sources are abstracted as a stream of values that can be manipulated via callback functions. Stream-based programming is exploding in popularity, as it provides a powerful and expressive paradigm for handling asynchronous data sources in interactive software. However, high-level stream abstractions can also make it difficult for developers to reason about control- and data-flow relationships in their programs. This is particularly impactful when asynchronous stream-based code interacts with thread-limited features such as UI frameworks that restrict UI access to a single thread, since the threading behavior of streaming constructs is often non-intuitive and insufficiently documented. In this paper, we present a type-based approach that can statically prove the thread-safety of UI accesses in stream-based software. Our key insight is that the fluent APIs of stream-processing frameworks enable the tracking of threads via type-refinement, making it possible to reason automatically about what thread a piece of code runs on – a difficult problem in general. We implement the system as an annotation-based Java typechecker for Android programs built upon the popular ReactiveX framework and evaluate its efficacy by annotating and analyzing 8 open-source apps, where we find 33 instances of unsafe UI access while incurring an annotation burden of only one annotation per 186 source lines of code. We also report on our experience applying the typechecker to two much larger apps from the Uber Technologies, Inc. codebase, where it currently runs on every code change and blocks changes that introduce potential threading bugs.

In this paper, we propose a novel visual secret sharing (VSS) scheme for color QR code (VSSCQR) with (n, n) threshold based on high capacity, admirable visual effects and popularity of color QR code. By splitting and encoding a secret image into QR codes and then fusing QR codes to generate color QR code shares, the scheme can share the secret among a certain number of participants. However, less than n participants cannot reveal any information about the secret. The embedding amount and position of the secret image bits generated by VSS are in the range of the error correction ability of the QR code. Each color share is readable, which can be decoded and thus may not come into notice. On one hand, the secret image can be reconstructed by first decomposing three QR codes from each color QR code share and then stacking the corresponding QR codes based on only human visual system without computational devices. On the other hand, by decomposing three QR codes from each color QR code share and then XORing the three QR codes respectively, we can reconstruct the secret image losslessly. The experiment results display the effect of our scheme.

Quick Response (QR) codes are rapidly becoming pervasive in our daily life because of its fast readability and the popularity of smartphones with a built-in camera. However, recent researches raise security concerns because QR codes can be easily sniffed and decoded which can lead to private information leakage or financial loss. To address the issue, we present mQRCode which exploit patterns with specific spatial frequency to camouflage QR codes. When the targeted receiver put a camera at the designated position (e.g., 30cm and 0° above the camouflaged QR code), the original QR code is revealed due to the Moiré phenomenon. Malicious adversaries will only see camouflaged QR code at any other position. Our experiments show that the decoding rate of mQR codes is 95% or above within 0.83 seconds. When the camera is 10cm or 15° away from the designated location, the decoding rate drops to 0 so it's secure from attackers.

The process of information and transformation of society has become a habit in modem people. We are accustomed to using the mobile phone for all kinds of operations, such as: sweep code to order meals, buy tickets and payment, thanks to the popularity of QR code technology in our country. There are many applications in the market with the function of scanning QR code, however, some QR codes can only be parsed by the specified application software. For instance, it can not work when using Alipay scanning QR code which configured by WeChat payment certificate Web program. The user will not be able to pay for such operations. For a product or service provider, different QR codes need to be created for different applications; for a user, a certain business operation needs to face multiple QR codes to select corresponding software in the device. The integration of QR code technology has become a key breakthrough point to improve the competitiveness of enterprises.

In a research community, the provenance sharing of scientific workflows can enhance distributed research cooperation, experiment reproducibility verification and experiment repeatedly doing. Considering that scientists in such a community are often in a loose relation and distributed geographically, traditional centralized provenance sharing architectures have shown their disadvantages in poor trustworthiness, reliabilities and efficiency. Additionally, they are also difficult to protect the rights and interests of data providers. All these have been largely hindering the willings of distributed scientists to share their workflow provenance. Considering the big advantages of blockchain in decentralization, trustworthiness and high reliability, an approach to sharing scientific workflow provenance based on blockchain in a research community is proposed. To make the approach more practical, provenance is handled on-chain and original data is delivered off-chain. A kind of block structure to support efficient provenance storing and retrieving is designed, and an algorithm for scientists to search workflow segments from provenance as well as an algorithm for experiments backtracking are provided to enhance the experiment result sharing, save computing resource and time cost by avoiding repeated experiments as far as possible. Analyses show that the approach is efficient and effective.

Remote data possession is becoming an increasingly important issue in cloud storage. It enables users to verify if their outsourced data have remained intact while in cloud storage. The existing remote data audit (RDA) protocols were designed with the public key infrastructure (PKI) system. However, this incurs considerable costs when users need to frequently access data from the cloud service provider with PKI. This study proposes a protocol, called identity-based RDA (ID-RDA) that addresses this problem without the need for users’ certificates. This study outperforms existing RDA protocols in computation and communication.

The Internet of Things (IoT) consists of embedded devices that sense and manage our environment in a growing range of applications. Large-scale IoT systems such as smart cities require significant investment in both equipment and personnel. To maximize return on investment, IoT platforms should support multiple third-party applications and adaptation of infrastructure over time. Realizing the vision of shared IoT platforms demands strong security guarantees. That is particularly challenging considering the limited capability and resource constraints of many IoT devices. In this paper, we present SPEED, an approach to secure erasure with verifiability in IoT. Secure erasure is a fundamental property when it comes to share an IoT platform with other users which guarantees the cleanness of a device's memory at the beginning of the application deployment as well as at the time of releasing the underlying IoT device. SPEED relies on two security primitives: memory isolation and distance bounding protocol. We evaluate the performance of SPEED by implementing it on a simple bare-metal IoT device belongs to Class-1. Our evaluation results show a limited overhead in terms of memory footprint, time, and energy consumption.

With the rapid development of information technology, software systems' scales and complexity are showing a trend of expansion. The users' needs for the software security, software security reliability and software stability are growing increasingly. At present, the industry has applied machine learning methods to the fields of defect detection to repair and improve software defects through the massive data intelligent semantic analysis or code scanning. The model in machine learning is faced with big difficulty of model building, understanding, and the poor visualization in the field of traditional software defect detection. In view of the above problems, we present a point of view that intelligent semantic analysis technology based on massive data, and using the trusted behavior decision tree model to analyze the soft behavior by layered detection technology. At the same time, it is equipped related test environment to compare the tested software. The result shows that the defect detection technology based on intelligent semantic analysis of massive data is superior to other techniques at the cost of building time and error reported ratio.

The target of security protection of the power distribution automation system (the distribution system for short) is to ensure the security of communication between the distribution terminal (terminal for short) and the distribution master station (master system for short). The encryption and authentication gateway (VPN gateway for short) for distribution system enhances the network layer communication security between the terminal and the VPN gateway. The distribution application layer encryption authentication device (master cipher machine for short) ensures the confidentiality and integrity of data transmission in application layer, and realizes the identity authentication between the master station and the terminal. All these measures are used to prevent malicious damage and attack to the master system by forging terminal identity, replay attack and other illegal operations, in order to prevent the resulting distribution network system accidents. Based on the security protection scheme of the power distribution automation system, this paper carries out the development of multi-chip encapsulation, develops IPSec Protocols software within the security chip, and realizes dual encryption and authentication function in IP layer and application layer supporting the national cryptographic algorithm.

Conventional SDN-based MTD techniques have been mainly developed with a single SDN controller which exposes a single point of failure as well as raises a scalability issue for large-scale networks in achieving both security and performance. The use of multiple SDN controllers has been proposed to ensure both performance and security of SDN-based MTD systems for large-scale networks; however, the effect of using multiple SDN controllers has not been investigated in the state-of-the-art research. In this paper, we propose the SDN based MTD architecture using multiple SDN controllers and validate their security effect (i.e., attack success probability) by implementing an IP shuffling MTD in a testbed using ONOS SDN controllers.

Security issues in a Software Defined Network (SDN) environment like system vulnerabilities and intrusion attempts can pose a security risk for multi-tenant network managed by SDN. In this research work, Moving target defense (MTD)technique based on shuffle strategy - port hopping has been employed to increase the difficulty for the attacker trying to exploit the cloud network. Our research workMASON, considers the problem of multi-stage attacks in a network managed using SDN. SDN controller can be used to dynamically reconfigure the network and render attacker»s knowledge in multi-stage attacks redundant. We have used a threat score based on vulnerability information and intrusion attempts to identify Virtual Machines (VMs) in systems with high-security risk and implement MTD countermeasures port hopping to assess threat score reduction in a cloud network.

While Moving Target Defenses (MTDs) have been increasingly recognized as a promising direction for cyber security, quantifying the effects of MTDs remains mostly an open problem. Each MTD has its own set of advantages and disadvantages. No single MTD provides an effective defense against the entire range of possible threats. One of the challenges facing MTD quantification efforts is predicting the cumulative effect of implementing multiple MTDs. We present a scenario where two MTDs are deployed in an experimental testbed created to model a realistic use case. This is followed by a probabilistic analysis of the effectiveness of both MTDs against a multi-step attack, along with the MTDs' impact on availability to legitimate users. Our work is essential to providing decision makers with the knowledge to make informed choices regarding cyber defense.

Modern operating systems, such as iOS, use multiple access control policies to define an overall protection system. However, the complexity of these policies and their interactions can hide policy flaws that compromise the security of the protection system. We propose iOracle, a framework that logically models the iOS protection system such that queries can be made to automatically detect policy flaws. iOracle models policies and runtime context extracted from iOS firmware images, developer resources, and jailbroken devices, and iOracle significantly reduces the complexity of queries by modeling policy semantics. We evaluate iOracle by using it to successfully triage executables likely to have policy flaws and comparing our results to the executables exploited in four recent jailbreaks. When applied to iOS 10, iOracle identifies previously unknown policy flaws that allow attackers to modify or bypass access control policies. For compromised system processes, consequences of these policy flaws include sandbox escapes (with respect to read/write file access) and changing the ownership of arbitrary files. By automating the evaluation of iOS access control policies, iOracle provides a practical approach to hardening iOS security by identifying policy flaws before they are exploited.

Currently, due to improvements in defensive systems network covert channels are increasingly drawing attention of cybercriminals and malware developers as they can provide stealthiness of the malicious communication and thus to bypass existing security solutions. On the other hand, the utilized data hiding methods are getting increasingly sophisticated as the attackers, in order to stay under the radar, distribute the covert data among many connections, protocols, etc. That is why, the detection of such threats becomes a pressing issue. In this paper we make an initial step in this direction by presenting a data mining-based detection of such advanced threats which relies on pattern discovery technique. The obtained, initial experimental results indicate that such solution has potential and should be further investigated.

Recent research has proposed middleware to enable efficient distributed apps over mobile-cloud platforms. This paper presents a Context-Aware File Discovery Service (CAFDS) that allows distributed mobile-cloud applications to find and access files of interest shared by collaborating users. CAFDS enables programmers to search for files defined by context and content features, such as location, creation time, or the presence of certain object types within an image file. CAFDS provides low-latency through a cloud-based metadata server, which uses a decision tree to locate the nearest files that satisfy the context and content features requested by applications. We implemented CAFDS in Android and Linux. Experimental results show CAFDS achieves substantially lower latency than peer-to-peer solutions that cannot leverage context information.

Secure multiparty computation (SMC) is a promising technology for privacy-preserving collaborative computation. In the last years several feasibility studies have shown its practical applicability in different fields. However, it is recognized that administration, and management overhead of SMC solutions are still a problem. A vital next step is the incorporation of SMC in the emerging fields of the Internet of Things and (smart) dynamic environments. In these settings, the properties of these contexts make utilization of SMC even more challenging since some vital premises for its application regarding environmental stability and preliminary configuration are not initially fulfilled. We bridge this gap by providing FlexSMC, a management and orchestration framework for SMC which supports the discovery of nodes, supports a trust establishment between them and realizes robustness of SMC session by handling nodes failures and communication interruptions. The practical evaluation of FlexSMC shows that it enables the application of SMC in dynamic environments with reasonable performance penalties and computation durations allowing soft real-time and interactive use cases.