Biblio

Along with the growing popularisation of Cloud Computing. Cloud storage technology has been paid more and more attention as an emerging network storage technology which is extended and developed by cloud computing concepts. Cloud computing environment depends on user services such as high-speed storage and retrieval provided by cloud computing system. Meanwhile, data security is an important problem to solve urgently for cloud storage technology. In recent years, There are more and more malicious attacks on cloud storage systems, and cloud storage system of data leaking also frequently occurred. Cloud storage security concerns the user's data security. The purpose of this paper is to achieve data security of cloud storage and to formulate corresponding cloud storage security policy. Those were combined with the results of existing academic research by analyzing the security risks of user data in cloud storage and approach a subject of the relevant security technology, which based on the structural characteristics of cloud storage system.

In this paper, we propose an autoencoder-based generative adversarial network (GAN) for automatic image generation, which is called "stylized adversarial autoencoder". Different from existing generative autoencoders which typically impose a prior distribution over the latent vector, the proposed approach splits the latent variable into two components: style feature and content feature, both encoded from real images. The split of the latent vector enables us adjusting the content and the style of the generated image arbitrarily by choosing different exemplary images. In addition, a multiclass classifier is adopted in the GAN network as the discriminator, which makes the generated images more realistic. We performed experiments on hand-writing digits, scene text and face datasets, in which the stylized adversarial autoencoder achieves superior results for image generation as well as remarkably improves the corresponding supervised recognition task.

Android privacy control is an important but difficult problem to solve. Previously, there was much research effort either focusing on extending the Android permission model with better policies or modifying the Android framework for fine-grained access control. In this work, we take an integral approach by designing and implementing SweetDroid, a calling-context-sensitive privacy policy enforcement framework. SweetDroid combines automated policy generation with automated policy enforcement. The automatically generated policies in SweetDroid are based on the calling contexts of privacy sensitive APIs; hence, SweetDroid is able to tell whether a particular API (e.g., getLastKnownLocation) under a certain execution path is leaking private information. The policy enforcement in SweetDroid is also fine-grained - it is at the individual API level, not at the permission level. We implement and evaluate the system based on thousands of Android apps, including those from a third-party market and malicious apps from VirusTotal. Our experiment results show that SweetDroid can successfully distinguish and enforce different privacy policies based on calling contexts, and the current design is both developer hassle-free and user transparent. SweetDroid is also efficient because it only introduces small storage and computational overhead.

Cyber-physical-social systems (CPSS), an emerging computing paradigm, have attracted intensive attentions from the research community and industry. We are facing various challenges in designing secure, reliable, and user-satisfied CPSS. In this article, we consider these design issues as a whole and propose a system-level design optimization framework for CPSS design where energy consumption, security-level, and user satisfaction requirements can be fulfilled while satisfying constraints for system reliability. Specifically, we model the constraints (energy efficiency, security, and reliability) as the penalty functions to be incorporated into the corresponding objective functions for the optimization problem. A smart office application is presented to demonstrate the feasibility and effectiveness of our proposed design optimization approach.

The wide-spreading mobile malware has become a dreadful issue in the increasingly popular mobile networks. Most of the mobile malware relies on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose TextDroid, an effective and automated malware detection method combining natural language processing and machine learning. TextDroid can extract distinguishable features (n-gram sequences) to characterize malware samples. A malware detection model is then developed to detect mobile malware using a Support Vector Machine (SVM) classifier. The trained SVM model presents a superior performance on two different data sets, with the malware detection rate reaching 96.36% in the test set and 76.99% in an app set captured in the wild, respectively. In addition, we also design a flow header visualization method to visualize the highlighted texts generated during the apps' network interactions, which assists security researchers in understanding the apps' complex network activities.

With the fast development of autonomous driving and vehicular communication technologies, intelligent transportation systems that are based on VANET (Vehicular Ad-Hoc Network) have shown great promise. For instance, through V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure) communication, intelligent intersections allow more fine-grained control of vehicle crossings and significantly enhance traffic efficiency. However, the performance and safety of these VANET-based systems could be seriously impaired by communication delays and packet losses, which may be caused by network congestion or by malicious attacks that target communication timing behavior. In this paper, we quantitatively model and analyze some of the timing and security issues in transportation networks with VANET-based intelligent intersections. In particular, we demonstrate how communication delays may affect the performance and safety of a single intersection and of multiple interconnected intersections, and present our delay-tolerant intersection management protocols. We also discuss the issues of such protocols when the vehicles are non-cooperative and how they may be addressed with game theory.

Data assurance and resilience are crucial security issues in cloud-based IoT applications. With the widespread adoption of drones in IoT scenarios such as warfare, agriculture and delivery, effective solutions to protect data integrity and communications between drones and the control system have been in urgent demand to prevent potential vulnerabilities that may cause heavy losses. To secure drone communication during data collection and transmission, as well as preserve the integrity of collected data, we propose a distributed solution by utilizing blockchain technology along with the traditional cloud server. Instead of registering the drone itself to the blockchain, we anchor the hashed data records collected from drones to the blockchain network and generate a blockchain receipt for each data record stored in the cloud, reducing the burden of moving drones with the limit of battery and process capability while gaining enhanced security guarantee of the data. This paper presents the idea of securing drone data collection and communication in combination with a public blockchain for provisioning data integrity and cloud auditing. The evaluation shows that our system is a reliable and distributed system for drone data assurance and resilience with acceptable overhead and scalability for a large number of drones.

Traditional Intrusion Detection Systems (IDSes) are generally implemented on vendor proprietary appliances or middleboxes, which usually lack a general programming interface, and their versatility and flexibility are also very poor. Emerging Network Function Virtualization (NFV) technology can virtualize IDSes and elastically scale them to deal with attack traffic variations. However, existing NFV solutions treat a virtualized IDS as a monolithic piece of software, which could lead to inflexibility and significant waste of resources. In this paper, we propose a novel approach to virtualize IDSes as microservices where the virtualized IDSes can be customized on demand, and the underlying microservices could be shared and scaled independently. We also conduct experiments, which demonstrate that virtualizing IDSes as microservices can gain greater flexibility and resource efficiency.

Random number generator is an important building block for many cryptographic primitives and protocols. Random numbers are used to initialize key bits, nonces and initialization vectors and seed pseudo-random number generators. Physical Unclonable Functions (PUFs) are a popular security primitive in cryptographic systems used for authentication, secure key storage and so on. PUFs have nature properties of unpredictability and uniqueness which is very suitable to be served as a source of randomness. In this paper we propose a new design of a true random number generator based on ring oscillator PUFs. It utilizes a self-feedback mechanism between the response and challenge of PUFs and some simple operations, mainly addition, rotation and xor, on the output of PUFs to generate truly random bits. Our design is very simple and easy to be implemented while achieving good randomness. Experiment results verified the good quality of bits generated by our design.

Over the years a lot of effort has been put on solving extensibility problems, while retaining important software engineering properties such as modular type-safety and separate compilation. Most previous work focused on operations that traverse and process extensible Abstract Syntax Tree (AST) structures. However, there is almost no work on operations that build such extensible ASTs, including parsing. This paper investigates solutions for the problem of modular parsing. We focus on semantic modularity and not just syntactic modularity. That is, the solutions should not only allow complete parsers to be built out of modular parsing components, but also enable the parsing components to be modularly type-checked and separately compiled. We present a technique based on parser combinators that enables modular parsing. Interestingly, the modularity requirements for modular parsing rule out several existing parser combinator approaches, which rely on some non-modular techniques. We show that Packrat parsing techniques, provide solutions for such modularity problems, and enable reasonable performance in a modular setting. Extensibility is achieved using multiple inheritance and Object Algebras. To evaluate the approach we conduct a case study based on the “Types and Programming Languages” interpreters. The case study shows the effectiveness at reusing parsing code from existing interpreters, and the total parsing code is 69% shorter than an existing code base using a non-modular parsing approach.

The advancement in technology has changed how people work and what software and hardware people use. From conventional personal computer to GPU, hardware technology and capability have dramatically improved so does the operating systems that come along. Unfortunately, current industry practice to compare OS is performed with single perspective. It is either benchmark the hardware level performance or performs penetration testing to check the security features of an OS. This rigid method of benchmarking does not really reflect the true performance of an OS as the performance analysis is not comprehensive and conclusive. To illustrate this deficiency, the study performed hardware level and operational level benchmarking on Windows XP, Windows 7 and Windows 8 and the results indicate that there are instances where Windows XP excels over its newer counterparts. Overall, the research shows Windows 8 is a superior OS in comparison to its predecessors running on the same hardware. Furthermore, the findings also show that the automated benchmarking tools are proved less efficient benchmark systems that run on Windows XP and older OS as they do not support DirectX 11 and other advanced features that the hardware supports. There lies the need to have a unified benchmarking approach to compare other aspects of OS such as user oriented tasks and security parameters to provide a complete comparison. Therefore, this paper is proposing a unified approach for Operating System (OS) comparisons with the help of a Windows OS case study. This unified approach includes comparison of OS from three aspects which are; hardware level, operational level performance and security tests.

Due to the increasing complexity of design process, outsourcing, and use of third-party blocks, it becomes harder and harder to prevent Trojan insertion and other malicious design modifications. In this paper, we propose to deploy security invariant as carried proof to prevent and detect Trojans and malicious attacks and to ensure the security of hardware design. Non-interference with down-grading policy is checked for confidentiality. Contrary to existing approaches by type checking, we develop a method to model-check a simple safety property on a composed machine. Down-grading is handled in a better way in model-checking and the effectiveness of our approach is demonstrated on various Verilog benchmarks.

As the adaptive steganography selects edge and texture area for loading, the theoretical analysis is limited by modeling difficulty. This paper introduces a novel method to study pixel-value difference (PVD) embedding scheme. First, the difference histogram values of cover image are used as parameters, and a variance formula for PVD stego noise is obtained. The accuracy of this formula has been verified through analysis with standard pictures. Second, the stego noise is divided into six kinds of pixel regions, and the regional noise variances are utilized to compare the security between PVD and least significant bit matching (LSBM) steganography. A mathematical conclusion is presented that, with the embedding capacity less than 2.75 bits per pixel, PVD is always not safer than LSBM under the same embedding rate, regardless of region selection. Finally, 10000 image samples are used to observe the validity of mathematical conclusion. For most images and regions, the data are also shown to be consistent with the prior judgment. Meanwhile, the cases of exception are analyzed seriously, and are found to be caused by randomness of pixel selection and abandoned blocks in PVD scheme. In summary, the unity of theory and practice completely indicates the effectiveness of our new method.

We consider the automatic verification of information flow security policies of web-based workflows, such as conference submission systems like EasyChair. Our workflow description language allows for loops, non-deterministic choice, and an unbounded number of participating agents. The information flow policies are specified in a temporal logic for hyperproperties. We show that the verification problem can be reduced to the satisfiability of a formula of first-order linear-time temporal logic, and provide decidability results for relevant classes of workflows and specifications. We report on experimental results obtained with an implementation of our approach on a series of benchmarks.

NEtwork MObility (NEMO) has gained recently a lot of attention from a number of standardization and researches committees. Although NEMO-Basic Support Protocol (NEMO-BSP) seems to be suitable in the context of the Intelligent Transport Systems (ITS), it has several shortcomings, such as packets loss and lack of security, since it is a host-based mobility scheme. Therefore, in order to improve handoff performance and solve these limitations, schemes adapting Proxy MIPv6 for NEMO have been appeared. But the majorities did not deal with the case of the handover of the Visiting Mobile Nodes (VMN) located below the Mobile Router (MR). Thus, this paper proposes a Visiting Mobile Node Authentication Protocol for Proxy MIPv6-Based NEtwork MObility which ensures strong authentication between entities. To evaluate the security performance of our proposition, we have used the AVISPA/SPAN software which guarantees that our proposed protocol is a safe scheme.

Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.

Trustworthy operation of industrial control systems depends on secure and real-time code execution on the embedded programmable logic controllers (PLCs). The controllers monitor and control the critical infrastructures, such as electric power grids and healthcare platforms, and continuously report back the system status to human operators. We present Zeus, a contactless embedded controller security monitor to ensure its execution control flow integrity. Zeus leverages the electromagnetic emission by the PLC circuitry during the execution of the controller programs. Zeus's contactless execution tracking enables non-intrusive monitoring of security-critical controllers with tight real-time constraints. Those devices often cannot tolerate the cost and performance overhead that comes with additional traditional hardware or software monitoring modules. Furthermore, Zeus provides an air-gap between the monitor (trusted computing base) and the target (potentially compromised) PLC. This eliminates the possibility of the monitor infection by the same attack vectors. Zeus monitors for control flow integrity of the PLC program execution. Zeus monitors the communications between the human machine interface and the PLC, and captures the control logic binary uploads to the PLC. Zeus exercises its feasible execution paths, and fingerprints their emissions using an external electromagnetic sensor. Zeus trains a neural network for legitimate PLC executions, and uses it at runtime to identify the control flow based on PLC's electromagnetic emissions. We implemented Zeus on a commercial Allen Bradley PLC, which is widely used in industry, and evaluated it on real-world control program executions. Zeus was able to distinguish between different legitimate and malicious executions with 98.9% accuracy and with zero overhead on PLC execution by design.

Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. Recent studies on decoy routing consider all decoy routing systems to be susceptible to a fundamental attack – regardless of their specific designs–in which the censors re-route traffic around decoy ASes, thereby preventing censored users from using such systems. In this paper, we propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to all previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it downstream-only decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Designing a downstream-only decoy routing system is a challenging engineering problem since decoy routers do not intercept the upstream traffic of censored users. We design the first downstream-only decoy routing system, called Waterfall, by devising unique covert communication mechanisms. We also use various techniques to make our Waterfall implementation resistant to traffic analysis attacks. We believe that downstream-only decoy routing is a significant step towards making decoy routing systems practical. This is because a downstream-only decoy routing system can be deployed using a significantly smaller number of volunteer ASes, given a target resistance to rerouting attacks. For instance, we show that a Waterfall implementation with only a single decoy AS is as resistant to routing attacks (against China) as a traditional decoy system (e.g., Telex) with 53 decoy ASes.

Smart mobile devices are becoming the main vessel of personal privacy information. While they carry valuable information, data erasure is somehow much more vulnerable than was predicted. The security mechanisms provided by the Android system are not flexible enough to thoroughly delete sensitive data. In addition to the weakness among several provided data-erasing and file-deleting mechanisms, we also target the Android OS design flaws in data erasure, and unveil that the design of the Android OS contradicts some secure data-erasure demands. We present the data-erasure flaws in three typical scenarios on mainstream Android devices, such as the data clearing flaw, application uninstallation flaw, and factory reset flaw. Some of these flaws are inherited data-deleting security issues from the Linux kernel, and some are new vulnerabilities in the Android system. Those scenarios reveal the data leak points in Android systems. Moreover, we reveal that the data remanence on the disk is rarely affected by the user’s daily operation, such as file deletion and app installation and uninstallation, by a real-world data deletion latency experiment. After one volunteer used the Android phone for 2 months, the data remanence amount was still considerable. Then, we proposed DataRaider for file recovering from disk fragments. It adopts a file-carving technique and is implemented as an automated sensitive information recovering framework. DataRaider is able to extract private data in a raw disk image without any file system information, and the recovery rate is considerably high in the four test Android phones. We propose some mitigation for data remanence issues, and give the users some suggestions on data protection in Android systems.