Biblio

We initiate an effort to provide a rigorous, holistic and modular security analysis of OpenStack. OpenStack is the prevalent open-source, non-proprietary package for managing cloud services and data centers. It is highly complex and consists of multiple inter-related components which are developed by separate, loosely coordinated groups. All of these properties make the security analysis of OpenStack both a worthy mission and a challenging one. We base our modeling and security analysis in the universally composable (UC) security framework. This allows specifying and proving security in a modular way – a crucial feature when analyzing systems of such magnitude. Our analysis has the following key features: 1) It is user-centric: It stresses the security guarantees given to users of the system in terms of privacy, correctness, and timeliness of the services. 2) It considers the security of OpenStack even when some of the components are compromised. This departs from the traditional design approach of OpenStack, which assumes that all services are fully trusted. 3) It is modular: It formulates security properties for individual components and uses them to prove security properties of the overall system. Specifically, this work concentrates on the high-level structure of OpenStack, leaving the further formalization and more detailed analysis of specific OpenStack services to future work. Specifically, we formulate ideal functionalities that correspond to some of the core OpenStack modules, and then proves security of the overall OpenStack protocol given the ideal components. As demonstrated within, the main challenge in the high-level design is to provide adequately fine-grained scoping of permissions to access dynamically changing system resources. We demonstrate security issues with current mechanisms in case of failure of some components, propose alternative mechanisms, and rigorously prove adequacy of then new mechanisms within our modeling.

This paper provides an approach to the detection of information security breaches in automated process control systems (APCS), which consists in forecasting multivariate time series formed from the values of the operating parameters of the end system devices. Using an experimental model of water treatment, a comparison was made of the forecasting results for the parameters characterizing the operation of the entire model, and for the parameters characterizing the flow of individual subprocesses implemented by the model. For forecasting, GRU-neural network training was performed.

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.

As a modern power transmission network, smart grid connects abundant terminal devices and plays an important role in our daily life. However, along with its growth are the security threats. Different from the separated environment previously, an adversary nowadays can destroy the power system by attacking its terminal devices. As a result, it's critical to ensure the security and safety of terminal devices. To achieve it, detecting the pre-existing vulnerabilities in the terminal program and enhancing its security, are of great importance and necessity. In this paper, we introduce Cker, a novel vulnerability detection tool for smart grid devices, which generates an program model based on device sources and sets rules to perform model checking. We utilize the static analysis to extract necessary information and build corresponding program models. By further checking the model with pre-defined vulnerability patterns, we achieve security detection and error reporting. The evaluation results demonstrate that our method can effectively detect vulnerabilities in smart devices with an acceptable accuracy and false positive rate. In addition, as Cker is realized by pure python, it can be easily scaled to other platforms.

High frequency of network security incidents has also brought a lot of negative effects and even huge economic losses to countries, enterprises and individuals in recent years. Therefore, more and more attention has been paid to the problem of network security. In order to evaluate the newly included vulnerability text information accurately, and to reduce the workload of experts and the false negative rate of the traditional method. Multiple deep learning methods for vulnerability text classification evaluation are proposed in this paper. The standard Cross Site Scripting (XSS) vulnerability text data is processed first, and then classified using three kinds of deep neural networks (CNN, LSTM, TextRCNN) and one kind of traditional machine learning method (XGBoost). The dropout ratio of the optimal CNN network, the epoch of all deep neural networks and training set data were tuned via experiments to improve the fit on our target task. The results show that the deep learning methods evaluate vulnerability risk levels better, compared with traditional machine learning methods, but cost more time. We train our models in various training sets and test with the same testing set. The performance and utility of recurrent convolutional neural networks (TextRCNN) is highest in comparison to all other methods, which classification accuracy rate is 93.95%.

Industry 4.0 and the Internet of Production can increase efficiency and effectiveness of workflows in manufacturing companies and production networks. Despite ubiquitous automation, people are essential in socio-technical cyber-physical production systems due to unique cognitive capabilities, as final arbitrators, or for ethical and legal reasons. However, the design of interfaces between the human-in-the-loop and production systems poses challenges not yet been sufficiently elaborated in research and practice. We present two behavioural studies in the context of inter-company collaboration that show why considering the human-in-the-loop is crucial: The first study shows that information complexity and individual differences shape the overall decision quality. With increasing information complexity, the decision speed decreases and the decision accuracy descends. Consequently, a fine balance between necessary, abundant, and superfluous information must be found. The second experiment studies human decision making in complex environments using a business simulation. We found that correct decision aids can augment the human-in-the-loop's decision making and that these can increase usability, trust, and proft. Yet, incorrect decision support has the opposite effect. Guidelines for designing socio-technical cyber-physical production systems and a research agenda conclude this article.

We propose WristUnlock, a novel technique that uses a wrist wearable to unlock a smartphone in a secure and usable fashion. WristUnlock explores both the physical proximity and secure Bluetooth connection between the smartphone and wrist wearable. There are two modes in WristUnlock with different security and usability features. In the WristRaise mode, the user raises his smartphone in his natural way with the same arm carrying the wrist wearable; the smartphone gets unlocked if the acceleration data on the smartphone and wrist wearable satisfy an anticipated relationship specific to the user himself. In the WristTouch mode, the wrist wearable sends a random number to the smartphone through both the Bluetooth channel and a touch-based physical channel; the smartphone gets unlocked if the numbers received from both channels are equal. We thoroughly analyze the security of WristUnlock and confirm its high efficacy through detailed experiments.

Due to its anonymity, there has been a dramatic growth of underground drug markets hosted in the darknet (e.g., Dream Market and Valhalla). To combat drug trafficking (a.k.a. illicit drug trading) in the cyberspace, there is an urgent need for automatic analysis of participants in darknet markets. However, one of the key challenges is that drug traffickers (i.e., vendors) may maintain multiple accounts across different markets or within the same market. To address this issue, in this paper, we propose and develop an intelligent system named uStyle-uID leveraging both writing and photography styles for drug trafficker identification at the first attempt. At the core of uStyle-uID is an attributed heterogeneous information network (AHIN) which elegantly integrates both writing and photography styles along with the text and photo contents, as well as other supporting attributes (i.e., trafficker and drug information) and various kinds of relations. Built on the constructed AHIN, to efficiently measure the relatedness over nodes (i.e., traffickers) in the constructed AHIN, we propose a new network embedding model Vendor2Vec to learn the low-dimensional representations for the nodes in AHIN, which leverages complementary attribute information attached in the nodes to guide the meta-path based random walk for path instances sampling. After that, we devise a learning model named vIdentifier to classify if a given pair of traffickers are the same individual. Comprehensive experiments on the data collections from four different darknet markets are conducted to validate the effectiveness of uStyle-uID which integrates our proposed method in drug trafficker identification by comparisons with alternative approaches.

Wireless sensor networks consist of various sensors that are deployed to monitor the physical world. And many existing security schemes use traditional cryptography theory to protect message content and contextual information. However, we are concerned about location security of nodes. In this paper, we propose an anonymous routing strategy for preserving location privacy (ARPLP), which sets a proxy source node to hide the location of real source node. And the real source node randomly selects several neighbors as receivers until the packets are transmitted to the proxy source. And the proxy source is randomly selected so that the adversary finds it difficult to obtain the location information of the real source node. Meanwhile, our scheme sets a branch area around the sink, which can disturb the adversary by increasing the routing branch. According to the analysis and simulation experiments, our scheme can reduce traffic consumption and communication delay, and improve the security of source node and base station.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach

Code reuse attacks can bypass the DEP mechanism effectively. Meanwhile, because of the stealthy of the operation, it becomes one of the most intractable threats while securing the information system. Although the security solutions of code randomization and diversity can mitigate the threat at a certain extent, attackers can bypass these solutions due to the high cost and coarsely granularity, and the memory disclosure vulnerability is another magic weapon which can be used by attackers to bypass these solutions. After analyzing the principle of memory disclosure vulnerability, we propose a novel code pointer hiding method based on a resilient area. We expatiate how to create the resilient area and achieve code pointer hiding from four aspects, namely hiding return addresses in data pages, hiding function pointers in data pages, hiding target pointers of instruction JUMP in code pages, and hiding target pointers of instruction CALL in code pages. This method can stop attackers from reading and analyzing pages in memory, which is a critical stage in finding and creating ROP chains while executing a code reuse attack. Lastly, we test the method contrastively, and the results show that the method is feasible and effective while defending against ROP attacks.

Software security techniques rely on correct execution by the hardware. Securing hardware components has been challenging due to their complexity and the proportionate attack surface they present during their design, manufacture, deployment, and operation. Recognizing that external communication represents one of the greatest threats to a system's security, this paper introduces the TrustGuard containment architecture. TrustGuard contains malicious and erroneous behavior using a relatively simple and pluggable gatekeeping hardware component called the Sentry. The Sentry bridges a physical gap between the untrusted system and its external interfaces. TrustGuard allows only communication that results from the correct execution of trusted software, thereby preventing the ill effects of actions by malicious hardware or software from leaving the system. The simplicity and pluggability of the Sentry, which is implemented in less than half the lines of code of a simple in-order processor, enables additional measures to secure this root of trust, including formal verification, supervised manufacture, and supply chain diversification with less than a 15% impact on performance.

With the widespread of cloud computing, the delegation of storage and computing is becoming a popular trend. Concerns on data integrity, security, user privacy as well as the correctness of execution are highlighted due to the untrusted remote data manipulation. Most of existing proposals solve the integrity checking and verifiable computation problems by challenge-response model, but are lack of scalability and reusability. Via blockchain, we achieve efficient and transparent public verifiable delegation for both storage and computing. Meanwhile, the smart contract provides API for request handling and secure data query. The security and privacy issues of data opening are settled by applying cryptographic algorithms all through the delegations. Additionally, any access to the outsourced data requires the owner's authentication, so that the dat transference and utilization are under control.

Bitcoin is popular not only with consumers, but also with cybercriminals (e.g., in ransomware and online extortion, and commercial online child exploitation). Given the potential of Bitcoin to be involved in a criminal investigation, the need to have an up-to-date and in-depth understanding on the forensic acquisition and analysis of Bitcoins is crucial. However, there has been limited forensic research of Bitcoin in the literature. The general focus of existing research is on postmortem analysis of specific locations (e.g. wallets on mobile devices), rather than a forensic approach that combines live data forensics and postmortem analysis to facilitate the identification, acquisition, and analysis of forensic traces relating to the use of Bitcoins on a system. Hence, the latter is the focus of this paper where we present an open source tool for live forensic and postmortem analysing automatically. Using this open source tool, we describe a list of target artifacts that can be obtained from a forensic investigation of popular Bitcoin clients and Web Wallets on different web browsers installed on Windows 7 and Windows 10 platforms.

This paper explores using chaos-based cryptography for transmitting multimedia data, mainly speech and voice messages, over public communication channels, such as the internet. The secret message to be transmitted is first converted into a one-dimensional time series, that can be cast in a digital/binary format. The main feature of the proposed technique is mapping the two levels of every corresponding bit of the time series into different multiple chaotic orbits, using a simple encryption function. This one-to-many mapping robustifies the encryption technique and makes it resilient to crypto-analysis methods that rely on associating the energy level of the signal into two binary levels, using return map attacks. A chaotic nonautonomous Duffing oscillator is chosen to implement the suggested technique, using three different parameters that are assumed unknown at the receiver side. Synchronization between the transmitter and the receiver and reconstructing the secret message, at the receiver side, is done using a Lyapunov-based adaptive technique. Achieving stable operation, tuning the required control gains, as well as effective utilization of the bandwidth of the public communication channel are investigated. Two different case studies are presented; the first one deals with text that can be expressed as 8-bit ASCII code, while the second one corresponds to an analog acoustic signal that corresponds to the voice associated with pronouncing a short sentence. Advantages and limitation of the proposed technique are highlighted, while suggesting extensions to other multimedia signals, along with their required additional computational effort.

This paper explores using chaos-based cryptography for transmitting multimedia data, mainly speech and voice messages, over public communication channels, such as the internet. The secret message to be transmitted is first converted into a one-dimensional time series, that can be cast in a digital/binary format. The main feature of the proposed technique is mapping the two levels of every corresponding bit of the time series into different multiple chaotic orbits, using a simple encryption function. This one-to-many mapping robustifies the encryption technique and makes it resilient to crypto-analysis methods that rely on associating the energy level of the signal into two binary levels, using return map attacks. A chaotic nonautonomous Duffing oscillator is chosen to implement the suggested technique, using three different parameters that are assumed unknown at the receiver side. Synchronization between the transmitter and the receiver and reconstructing the secret message, at the receiver side, is done using a Lyapunov-based adaptive technique. Achieving stable operation, tuning the required control gains, as well as effective utilization of the bandwidth of the public communication channel are investigated. Two different case studies are presented; the first one deals with text that can be expressed as 8-bit ASCII code, while the second one corresponds to an analog acoustic signal that corresponds to the voice associated with pronouncing a short sentence. Advantages and limitation of the proposed technique are highlighted, while suggesting extensions to other multimedia signals, along with their required additional computational effort.

In this paper, we present a chaos-based information rotated polar coding scheme for enhancing the reliability and security of visible light communication (VLC) systems. In our scheme, we rotate the original information, wherein the rotation principle is determined by two chaotic sequences. Then the rotated information is encoded by secure polar coding scheme. After the channel polarization achieved by the polar coding, we could identify the bit-channels providing good transmission conditions for legitimate users and the bit-channels with bad conditions for eavesdroppers. Simulations are performed over the visible light wiretap channel. The results demonstrate that compared with existing schemes, the proposed scheme can achieve better reliability and security even when the eavesdroppers have better channel conditions.

This paper proposes a compensation control scheme against DoS attack for nonlinear cyber-physical systems (CPSs). The dynamical process of the nonlinear CPSs are described by T-S fuzzy model that regulated by the corresponding fuzzy rules. The communication link between the controller and the actuator under consideration may be unreliable, where Denialof-Service (DoS) attack is supposed to invade the communication link randomly. To compensate the negative effect caused by DoS attack, a compensation control scheme is designed to maintain the stability of the closed-loop system. With the aid of the Lyapunov function theory, a sufficient condition is established to ensure the stochastic stability and strict dissipativity of the closed-loop system. Finally, an iterative linearization algorithm is designed to determine the controller gain and the effectiveness of the proposed approach is evaluated through simulations.

In the vertical multi-level public network, the ciphertext video needs to support the distribution of key in the whole network when it is shared across different security domains horizontally. However, the traditional key management mode faces great pressure on the security classification and encryption efficiency, and especially, it cannot fully ensure the security of content and key when sharing across-domains. Based on the above analysis, this paper proposes a cross domain video security sharing solution based on white box encryption theory to improve the security of video data sharing. In this solution, the white box encryption technology is adopted to establish the data sharing background trust mechanism based on the key management center, and we study the white box key protection technology of the video terminal to support the mass level key distribution in the network and the online security generation and replacement of the white box password module, so that the safe and fast cross domain exchange and sharing of video data are realized.

In this paper, we present an overview of the problems associated with the cross-site scripting (XSS) in the graphical content of web applications. The brief analysis of vulnerabilities for graphical files and factors responsible for making SVG images vulnerable to XSS attacks are discussed. XML treatment methods and their practical testing are performed. As a result, the set of rules for protecting the graphic content of the websites and prevent XSS vulnerabilities are proposed.

Advanced metering infrastructure (AMI) is a key component in the smart grid. Transmitting data robustly and reliably between the tremendous smart meters in the AMI is one of the most crucial tasks for providing various services in smart grid. Among the many efforts for designing practical routing protocols for the AMI, the Routing Protocol for Low-Power and Lossy Networks (RPL) proposed by the IETF ROLL working group is considered the most consolidated candidate. Resent research has shown cyber attacks such as blackhole attack and version number attack can seriously damage the performance of the network implementing RPL. The main reason that RPL is vulnerable to these kinds of attacks is the lack an authentication mechanism. In this paper, we study the impact of blackhole attacks on the performance of the AMI network and proposed a new blackhole attack that can bypass the existing defense mechanism. Then, we propose a cuckoo filter based RPL to defend the AMI network from blackhole attacks. We also give the security analysis of the proposed method.

A database is an organized collection of data. Though a number of techniques, such as encryption and electronic signatures, are currently available for the protection of data when transmitted across sites. Database security refers to the collective measures used to protect and secure a database or database management software from illegitimate use and malicious threats and attacks. In this paper, we create 6 types of method for more secure ways to store and retrieve database information that is both convenient and efficient. Confidentiality, integrity, and availability, also known as the CIA triad, is a model designed to guide policies for information security within the database. There are many cryptography techniques available among them, ECC is one of the most powerful techniques. A user wants to the data stores or request, the user needs to authenticate. When a user who is authenticated, he will get key from a key generator and then he must be data encrypt or decrypt within the database. Every keys store in a key generator and retrieve from the key generator. We use 256 bits of AES encryption for rows level encryption, columns level encryption, and elements level encryption for the database. Next two method is encrypted AES 256 bits random key by using 521 bits of ECC encryption and signature for rows level encryption and column level encryption. Last method is most secure method in this paper, which method is element level encryption with AES and ECC encryption for confidentiality and ECC signature use for every element within the database for integrity. As well as encrypting data at rest, it's also important to ensure confidential data are encrypted in motion over our network to protect against database signature security. The advantages of elements level are difficult for attack because the attacker gets a key that is lose only one element. The disadvantages need to thousands or millions of keys to manage.

A frequent problem of Internet services are Sybil attacks, i.e., malicious users create numerous fake identities for themselves. To avoid this, many services employ obstacles like Captchas to force (potentially malicious) users to invest human attention in creating new identities for the service. However, this only makes it more difficult but not impossible to create fake identities. Sybil attacks are especially encountered as a problem in decentralized systems since no single trust anchor is available to judge new users as honest or malicious. The avoidance of a single centralized trust-anchor, however, is desirable in many cases. As a consequence, various decentralized Sybil detection approaches have been proposed. The most promising ones are based on leveraging the trust relationships embedded within social graphs. While most of these approaches are focusing on detecting large existing groups of Sybil identities, our approach Detasyr instead restricts the creation of numerous Sybil identities. For that, tickets are distributed through the social graph and have to be collected, allowing for decentralized and privacy preserving authorization. Additionally, it offers a proof of authorization to users that are considered to be honest, allowing them to display their authorization towards others.

As a modern power transmission network, smart grid connects plenty of terminal devices. However, along with the growth of devices are the security threats. Different from the previous separated environment, an adversary nowadays can destroy the power system by attacking these devices. Therefore, it's critical to ensure the security and safety of terminal devices. To achieve this goal, detecting the pre-existing vulnerabilities of the device program and enhance the terminal security, are of great importance and necessity. In this paper, we propose a novel approach that detects existing buffer-overflow vulnerabilities of terminal devices via automatic static analysis (ASA). We utilize the static analysis to extract the device program information and build corresponding program models. By further matching the generated program model with pre-defined vulnerability patterns, we achieve vulnerability detection and error reporting. The evaluation results demonstrate that our method can effectively detect buffer-overflow vulnerabilities of smart terminals with a high accuracy and a low false positive rate.

This article shows the possibility of detection of the hidden information in images. This is the approach to steganalysis than the basic data about the image and the information about the hiding method of the information are unknown. The architecture of the convolutional neural network makes it possible to detect small changes in the image with high probability.