Biblio

Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.

We propose and demonstrate an indoor optical bi-directional communication system employing near-infrared (NIR) and visible light as carriers. Such a communication technology is attractive wherein red color could be deployed for down streaming purpose via, for instance, LiFi (light fidelity) system, and NIR color for up streaming purpose. This system concept is implemented over a simultaneous bidirectional audio signal transmission and reception over 0.6m indoor wireless channel. Besides, designing the transceiver circuits from off the shelf components, frequency scrambling encryption and decryption technique is also integrated in the system for security purpose. The communication system is optically characterized in terms of line-of-sight laser misalignment and communication distance.

Video retrieval technology faces a series of challenges with the tremendous growth in the number of videos. In order to improve the retrieval performance in efficiency and accuracy, a novel deep hash method for video data hierarchical retrieval is proposed in this paper. The approach first uses cluster-based method to extract key frames, which reduces the workload of subsequent work. On the basis of this, high-level semantical features are extracted from VGG16, a widely used deep convolutional neural network (deep CNN) model. Then we utilize a hierarchical retrieval strategy to improve the retrieval performance, roughly can be categorized as coarse search and fine search. In coarse search, we modify simHash to learn hash codes for faster speed, and in fine search, we use the Euclidean distance to achieve higher accuracy. Finally, we compare our approach with other two methods through practical experiments on two videos, and the results demonstrate that our approach has better retrieval effect.

The goal of content-based recommendation system is to retrieve and rank the list of items that are closest to the query item. Today, almost every e-commerce platform has a recommendation system strategy for products that customers can decide to buy. In this paper we describe our work on creating a Generative Adversarial Network based image retrieval system for e-commerce platforms to retrieve best similar images for a given product image specifically for shoes. We compare state-of-the-art solutions and provide results for the proposed deep learning network on a standard data set.

The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.

In order to protect power grid network, the security assessment techniques which include both cyber side and the physical side should be considered. In this paper, we present a method for evaluating the dynamic vulnerability of cyber-physical power system (CPPS) considering the power grid infrastructures failure. First, according to the functional characteristics of different components, the impact of a single component function failure on CPPS operation is analyzed and quantified, such as information components, communication components and power components; then, the dynamic vulnerability of multiple components synchronization function failure is calculated, and the full probability evaluation formula of CPPS operational dynamic vulnerability is built; Thirdly, from an attacker's perspective to identify the most hazardous component combinations for CPPS multi-node collaborative attack; Finally, a local CPPS model is established based on the IEEE-9 bus system to quantify its operational dynamic vulnerability, and the effectiveness of proposed method is verified.

Searchable encryption is a cryptographic primitive that allows users to search for keywords on encrypted data. It allows users to search in archives stored on cloud servers. Among searchable encryption schemes, those supporting multiuser settings are more suitable for daily application scenarios and more practical. However, since the cloud server is semi-trusted, the result set returned by the server is undefined, and most existing multi-user searchable encryption schemes rely heavily on trusted third parties to manage user permission. To address these problems, verifiable multi-user searchable encryption schemes with dynamic management of user search permissions, weak trust on trusted third parties and are desirable. In this paper, we propose such a scheme. Our scheme manages user permission and key distribution without a trusted third party. User search permission and user access permission matrices are generated separately to manage user permissions dynamically. In addition, our scheme can verify the result set returned by the cloud server. We also show that our scheme is index and trapdoor indistinguishable under chosen keyword attacks in the random oracle model. Finally, a detailed comparison experiment is made by using the actual document data set, and the results show that our scheme is efficient and practical.

The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.

The XML-based data format AutomationML enables vendor-independent exchange of design data between discipline-specific design tools. It is based on Computer Aided Engineering Exchange (CAEX) and hence, compatible with the W3C standards XMLEnc (XML encryption) and XMLDsig (XML signatures). However, despite the importance of protecting engineering data, so far no concept has been presented to ensure and control on a fine-grained level the confidentiality, authenticity and accessibility of information stored in AutomationML files. In this paper, we introduce a basic access control scheme for AutomationML that enables to define user read and write access for each component. Furthermore, the scheme supports non-repudiation based on a change history and so-called "signature chains". It is also capable of supporting views and restricted access to components. The scheme is based on cryptographic measures – i.e. cryptographic hashing, symmetric encryption, signatures, and asymmetric encryption – and enforces its access control mechanisms through encryption to protect against unauthorized reading, and through signature chains to protect against unauthorized manipulation and to ensure non-repudiation. This approach has the benefit to be independent of the underlying file and operating system, storage location, etc., and it keeps full CAEX-conformity by extending AutomationML.This concept can serve as basis for software tools that support AutomationML and want to integrate access control features directly into AutomationML.

This paper focuses on how to provide virtual network (VN) with the survivability of node failure. In the SVNE that responds to node failures, the backup mechanism provided by the VN initial mapping method should be as flexible as possible, so that backup resources can be shared among the VNs, thereby providing survivability support for the most VNs with the least backup overhead, which can improve The utilization of backup resources can also improve the survivability of VN to deal with multi-node failures. For the remapping method of virtual networks, it needs to be higher because it involves both remapping of virtual nodes and remapping of related virtual links. The remapping efficiency, so as to restore the affected VN to a normal state as soon as possible, to avoid affecting the user's business experience. Considering that the SVNE method that actively responds to node failures always has a certain degree of backup resource-specific phenomenon, this section provides a SVNE method that passively responds to node failures. This paper mainly introduces the survivability virtual network initial mapping method based on physical node recoverability in this method.

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.

Information, not just data, is key to today's global challenges. To solve these challenges requires not only advancing geospatial and big data analytics but requires new analysis and decision-making environments that enable reliable decisions from trustable, understandable information that go beyond current approaches to machine learning and artificial intelligence. These environments are successful when they effectively couple human decision making with advanced, guided spatial analytics in human-computer collaborative discourse and decision making (HCCD). Our HCCD approach builds upon visual analytics, natural scale templates, traceable information, human-guided analytics, and explainable and interactive machine learning, focusing on empowering the decisionmaker through interactive visual spatial analytic environments where non-digital human expertise and experience can be combined with state-of-the-art and transparent analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. I'll describe how we have applied these techniques to challenges in sustainability, security, resiliency, public safety, and disaster management.

Digital signature over elliptic curve is one of the most important applications of security because it is effective. Recently, it has been developed and defined in the various standard of security. The application of the digital signature are signer authentication, data integrity, and non-repudiation. Currently, the requirements to implement authentication process on a computer hardware with limited resource such as energy, memory and computing power are increasing. The developer should consider these factors along with security factor for the effective implement on the computer hardware with limited resource. In this paper, we propose the Schnorr signature scheme using Koblitz curve over a field of characteristic two. The advantage of Schnorr signature scheme is a good combination with Koblitz curve over a field of characteristic two, therefore its arithmetic can be performed in any computer. Moreover, we use Double-and-Add scalar multiplication to reduce time in the process of systems. In addition, this paper shows a result of time in the process of the system to compare the performance of the Schnorr signature scheme on Koblitz curve using Double-andAdd scalar multiplication with the Schnorr signature scheme on Koblitz curve using typical scalar multiplication. The result of this study is that both systems working correctly. However, the Schnorr signature scheme on Koblitz curve using Double-andAdd performs better in time efficiency than of Schnorr signature scheme on Koblitz curve using typical scalar multiplication.

Although virtual reality hardware is now widely available, the uptake of real walking is hindered by the fact that it requires often impractically large amounts of physical space. To address this, we present VirtualSpace, a novel system that allows overloading multiple users immersed in different VR experiences into the same physical space. VirtualSpace accomplishes this by containing each user in a subset of the physical space at all times, which we call tiles; app-invoked maneuvers then shuffle tiles and users across the entire physical space. This allows apps to move their users to where their narrative requires them to be while hiding from users that they are confined to a tile. We show how this enables VirtualSpace to pack four users into 16m2. In our study we found that VirtualSpace allowed participants to use more space and to feel less confined than in a control condition with static, pre-allocated space.

Dynamic data race detectors are valuable tools for testing and validating concurrent software, but to achieve good performance they are typically implemented using sophisticated concurrent algorithms. Thus, they are ironically prone to the exact same kind of concurrency bugs they are designed to detect. To address these problems, we have developed VerifiedFT, a clean slate redesign of the FastTrack race detector [19]. The VerifiedFT analysis provides the same precision guarantee as FastTrack, but is simpler to implement correctly and efficiently, enabling us to mechanically verify an implementation of its core algorithm using CIVL [27]. Moreover, VerifiedFT provides these correctness guarantees without sacrificing any performance over current state-of-the-art (but complex and unverified) FastTrack implementations for Java.

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.

The intelligent power grid is composed of a large number of industrial control equipment, and most of the industrial control equipment has security holes, which are vulnerable to malicious attacks and affect the normal operation of the power grid. By analyzing the security vulnerability of the firmware of industrial control equipment, the vulnerability can be detected in advance and the power grid's ability to resist attack can be improved. In this paper, a kind of industrial control device firmware protocol vulnerabilities associated technology, through the technology of information extraction from the mass grid device firmware device attributes and extract the industrial control system, the characteristics of the construction of industrial control system device firmware and published vulnerability information correlation, faster in the industrial control equipment safety inspection found vulnerabilities.

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.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

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.

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.

Nowadays, most vendors apply the same open source code to their products, which is dangerous. In addition, when manufacturers release patches, they generally hide the exact location of the vulnerabilities. So, identifying vulnerabilities in binaries is crucial. However, just searching source program has a lower identifying accuracy of vulnerability, which requires operators further to differentiate searched results. Under this context, we propose VMPBL to enhance identifying the accuracy of vulnerability with the help of patch files. VMPBL, compared with other proposed schemes, uses patched functions according to its vulnerable functions in patch file to further distinguish results. We establish a prototype of VMPBL, which can effectively identify vulnerable function types and get rid of safe functions from results. Firstly, we get the potential vulnerable-patched functions by binary comparison technique based on K-Trace algorithm. Then we combine the functions with vulnerability and patch knowledge database to classify these function pairs and identify the possible vulnerable functions and the vulnerability types. Finally, we test some programs containing real-world CWE vulnerabilities, and one of the experimental results about CWE415 shows that the results returned from only searching source program are about twice as much as the results from VMPBL. We can see that using VMPBL can significantly reduce the false positive rate of discovering vulnerabilities compared with analyzing source files alone.

Dynamic Fuzzy Rule Interpolation (D-FRI) offers a dynamic rule base for fuzzy systems which is especially useful for systems with changing requirements and limited prior knowledge. This suggests a possible application of D-FRI in the area of network security due to the volatility of the traffic. A honeypot is a valuable tool in the field of network security for baiting attackers and collecting their information. However, typically designed with fewer resources they are not considered as a primary security tool for use in network security. Consequently, such honeypots can be vulnerable to many security attacks. One such attack is a spoofing attack which can cause severe damage to the honeypot, making it inefficient. This paper presents a vigilant dynamic honeypot based on the D-FRI approach for use in predicting and alerting of spoofing attacks on the honeypot. First, it proposes a technique for spoofing attack identification based on the analysis of simulated attack data. Then, the paper employs the identification technique to develop a D-FRI based vigilant dynamic honeypot, allowing the honeypot to predict and alert that a spoofing attack is taking place in the absence of matching rules. The resulting system is capable of learning and maintaining a dynamic rule base for more accurate identification of potential spoofing attacks with respect to the changing traffic conditions of the network.

In big data environments with big number of users and high volume of data, we need to manage the corresponding huge number of security policies. Due to the distributed management of these policies, they may contain several anomalies, such as conflicts and redundancies, which may lead to both safety and availability problems. The distributed systems guided by such security policies produce a huge number of access logs. Due to potential security breaches, the access logs may show the presence of non-allowed accesses. This may also be a consequence of conflicting rules in the security policies. In this paper, we present an ongoing work on developing an environment for verifying and correcting security policies. To make the approach efficient, an access log is used as input to determine suspicious parts of the policy that should be considered. The approach is also made efficient by clustering the policy and the access log and considering separately the obtained clusters. The clustering technique and the use of access log significantly reduces the complexity of the suggested approach, making it scalable for large amounts of data.

The identification of transmission sections is used to improve the efficiency of monitoring the operation of the power grid. In order to test the validity of transmission sections identified, an assessment process is necessary. In addition, Transmission betweenness, an index for finding the key transmission lines in the power grid, should also be verified. In this paper, chain attack is assumed to check the weak links in the grid, thus verifying the transmission betweenness implemented for the system. Moreover, the line outage distribution factors (LODFs) are used to quantify the change of power flow when the leading line in transmission sections breaks down, so that the validity of transmission sections can be proved. Case studies based on IEEE 39 and IEEE 118 -bus system proved the effectiveness of the proposed method.