Biblio
We propose a new class of post-quantum digital signature schemes that: (a) derive their security entirely from the security of symmetric-key primitives, believed to be quantum-secure, and (b) have extremely small keypairs, and, (c) are highly parameterizable. In our signature constructions, the public key is an image y=f(x) of a one-way function f and secret key x. A signature is a non-interactive zero-knowledge proof of x, that incorporates a message to be signed. For this proof, we leverage recent progress of Giacomelli et al. (USENIX'16) in constructing an efficient Σ-protocol for statements over general circuits. We improve this Σ-protocol to reduce proof sizes by a factor of two, at no additional computational cost. While this is of independent interest as it yields more compact proofs for any circuit, it also decreases our signature sizes. We consider two possibilities to make the proof non-interactive: the Fiat-Shamir transform and Unruh's transform (EUROCRYPT'12, '15,'16). The former has smaller signatures, while the latter has a security analysis in the quantum-accessible random oracle model. By customizing Unruh's transform to our application, the overhead is reduced to 1.6x when compared to the Fiat-Shamir transform, which does not have a rigorous post-quantum security analysis. We implement and benchmark both approaches and explore the possible choice of f, taking advantage of the recent trend to strive for practical symmetric ciphers with a particularly low number of multiplications and end up using Low MC (EUROCRYPT'15).
Autonomous vehicles must communicate with each other effectively and securely to make robust decisions. However, today's Internet falls short in supporting efficient data delivery and strong data security, especially in a mobile ad-hoc environment. Named Data Networking (NDN), a new data-centric Internet architecture, provides a better foundation for secure data sharing among autonomous vehicles. We examine two potential threats, false data dissemination and vehicle tracking, in an NDN-based autonomous vehicular network. To detect false data, we propose a four-level hierarchical trust model and the associated naming scheme for vehicular data authentication. Moreover, we address vehicle tracking concerns using a pseudonym scheme to anonymize vehicle names and certificate issuing proxies to further protect vehicle identity. Finally, we implemented and evaluated our AutoNDN application on Raspberry Pi-based mini cars in a wireless environment.
Currently, the networking of everyday objects, socalled Internet of Things (IoT), such as vehicles and home automation environments is progressing rapidly. Formerly deployed as domain-specific solutions, the development is continuing to link different domains together to form a large heterogeneous IoT ecosystem. This development raises challenges in different fields such as scalability of billions of devices, interoperability across different IoT domains and the need of mobility support. The Information-Centric Networking (ICN) paradigm is a promising candidate to form a unified platform to connect different IoT domains together including infrastructure, wireless, and ad-hoc environments. This paper describes a vision of a harmonized architectural design providing dynamic access of data and services based on an ICN. Within the context of connected vehicles, the paper introduces requirements and challenges of the vision and contributes in open research directions in Information-Centric Networking.
Because of the nature of vehicular communications, security is a crucial aspect, involving the continuous development and analysis of the existing security architectures and punctual theoretical and practical aspects that have been proposed and are in need of continuous updates and integrations with newer technologies. But before an update, a good knowledge of the current aspects is mandatory. Identifying weaknesses and anticipating possible risks of vehicular communication networks through a failure modes and effects analysis (FMEA) represent an important aspect of the security analysis process and a valuable step in finding efficient security solutions for all kind of problems that might occur in these systems.
Fast-changing topologies and uncoordinated transmissions are two critical challenges of implementing data security in vehicular ad-hoc networks (VANETs). We propose a new protocol, where transmitters adaptively switch between backing off retransmissions and changing keys to improve success rate. A new 3-dimensional (3-D) Markov model, which can analyze the proposed protocol with symmetric or asymmetric keys in terms of data security and connectivity, is developed. Analytical results, validated by simulations, show that the proposed protocol achieves substantially improved resistance against collusion attacks.
One challenge for engineered cyber physical systems (CPSs) is the possibility for a malicious intruder to change the data transmitted across the cyber channel as a means to degrade the performance of the physical system. In this paper, we consider a data injection attack on a cyber physical system. We propose a hybrid framework for detecting the presence of an attack and operating the plant in spite of the attack. Our method uses an observer-based detection mechanism and a passivity balance defense framework in the hybrid architecture. By switching the controller, passivity and exponential stability are established under the proposed framework.
The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.
Traditionally, the focus of security and ensuring confidentiality, integrity, and availability of data in spacecraft systems has been on the ground segment and the uplink/downlink components. Although these are the most obvious attack vectors, potential security risks against the satellite's platform is also a serious concern. This paper discusses a notional satellite architecture and explores security vulnerabilities using a systems-level approach. Viewing attacks through this paradigm highlights several potential attack vectors that conventional satellite security approaches fail to consider. If left undetected, these could yield physical effects limiting the satellite's mission or performance. The approach presented aids in risk analysis and gives insight into architectural design considerations which improve the system's overall resiliency.
Utility networks are part of every nation's critical infrastructure, and their protection is now seen as a high priority objective. In this paper, we propose a threat awareness architecture for critical infrastructures, which we believe will raise security awareness and increase resilience in utility networks. We first describe an investigation of trends and threats that may impose security risks in utility networks. This was performed on the basis of a viewpoint approach that is capable of identifying technical and non-technical issues (e.g., behaviour of humans). The result of our analysis indicated that utility networks are affected strongly by technological trends, but that humans comprise an important threat to them. This provided evidence and confirmed that the protection of utility networks is a multi-variable problem, and thus, requires the examination of information stemming from various viewpoints of a network. In order to accomplish our objective, we propose a systematic threat awareness architecture in the context of a resilience strategy, which ultimately aims at providing and maintaining an acceptable level of security and safety in critical infrastructures. As a proof of concept, we demonstrate partially via a case study the application of the proposed threat awareness architecture, where we examine the potential impact of attacks in the context of social engineering in a European utility company.
Life-cycle management of stateful VNF services is a complicated task, especially when automated resiliency and scaling should be handled in a secure manner, without service degradation. We present FlowSNAC, a resilient and scalable VNF service for user authentication and service deployment. FlowSNAC consists of both stateful and stateless components, some of that are SDN-based and others that are NFVs. We describe how it adapts to changing conditions by automatically updating resource allocations through a series of intermediate steps of traffic steering, resource allocation, and secure state transfer. We conclude by highlighting some of the lessons learned during implementation, and their wider consequences for the architecture of SDN/NFV management and orchestration systems.
The modern electric power grid is a complex cyber-physical system whose reliable operation is enabled by a wide-area monitoring and control infrastructure. Recent events have shown that vulnerabilities in this infrastructure may be exploited to manipulate the data being exchanged. Such a scenario could cause the associated control applications to mis-operate, potentially causing system-wide instabilities. There is a growing emphasis on looking beyond traditional cybersecurity solutions to mitigate such threats. In this paper we perform a testbed-based validation of one such solution - Attack Resilient Control (ARC) - on Iowa State University's PowerCyber testbed. ARC is a cyber-physical security solution that combines domain-specific anomaly detection and model-based mitigation to detect stealthy attacks on Automatic Generation Control (AGC). In this paper, we first describe the implementation architecture of the experiment on the testbed. Next, we demonstrate the capability of stealthy attack templates to cause forced under-frequency load shedding in a 3-area test system. We then validate the performance of ARC by measuring its ability to detect and mitigate these attacks. Our results reveal that ARC is efficient in detecting stealthy attacks and enables AGC to maintain system operating frequency close to its nominal value during an attack. Our studies also highlight the importance of testbed-based experimentation for evaluating the performance of cyber-physical security and control applications.
Customer Edge Switching (CES) is an experimental Internet architecture that provides reliable and resilient multi-domain communications. It provides resilience against security threats because domains negotiate inbound and outbound policies before admitting new traffic. As CES and its signalling protocols are being prototyped, there is a need for independent testing of the CES architecture. Hence, our research goal is to develop an automated test framework that CES protocol designers and early adopters can use to improve the architecture. The test framework includes security, functional, and performance tests. Using the Robot Framework and STRIDE analysis, in this paper we present this automated security test framework. By evaluating sample test scenarios, we show that the Robot Framework and our CES test suite have provided productive discussions about this new architecture, in addition to serving as clear, easy-to-read documentation. Our research also confirms that test automation can be useful to improve new protocol architectures and validate their implementation.
In a world where traditional notions of privacy are increasingly challenged by the myriad companies that collect and analyze our data, it is important that decision-making entities are held accountable for unfair treatments arising from irresponsible data usage. Unfortunately, a lack of appropriate methodologies and tools means that even identifying unfair or discriminatory effects can be a challenge in practice. We introduce the unwarranted associations (UA) framework, a principled methodology for the discovery of unfair, discriminatory, or offensive user treatment in data-driven applications. The UA framework unifies and rationalizes a number of prior attempts at formalizing algorithmic fairness. It uniquely combines multiple investigative primitives and fairness metrics with broad applicability, granular exploration of unfair treatment in user subgroups, and incorporation of natural notions of utility that may account for observed disparities. We instantiate the UA framework in FairTest, the first comprehensive tool that helps developers check data-driven applications for unfair user treatment. It enables scalable and statistically rigorous investigation of associations between application outcomes (such as prices or premiums) and sensitive user attributes (such as race or gender). Furthermore, FairTest provides debugging capabilities that let programmers rule out potential confounders for observed unfair effects. We report on use of FairTest to investigate and in some cases address disparate impact, offensive labeling, and uneven rates of algorithmic error in four data-driven applications. As examples, our results reveal subtle biases against older populations in the distribution of error in a predictive health application and offensive racial labeling in an image tagger.
Efficient management and control of modern and next-gen networks is of paramount importance as networks have to maintain highly reliable service quality whilst supporting rapid growth in traffic demand and new application services. Rapid mitigation of network service degradations is a key factor in delivering high service quality. Automation is vital to achieving rapid mitigation of issues, particularly at the network edge where the scale and diversity is the greatest. This automation involves the rapid detection, localization and (where possible) repair of service-impacting faults and performance impairments. However, the most significant challenge here is knowing what events to detect, how to correlate events to localize an issue and what mitigation actions should be performed in response to the identified issues. These are defined as policies to systems such as ECOMP. In this paper, we present AESOP, a data-driven intelligent system to facilitate automatic learning of policies and rules for triggering remedial actions in networks. AESOP combines best operational practices (domain knowledge) with a variety of measurement data to learn and validate operational policies to mitigate service issues in networks. AESOP's design addresses the following key challenges: (i) learning from high-dimensional noisy data, (ii) capturing multiple fault models, (iii) modeling the high service-cost of false positives, and (iv) accounting for the evolving network infrastructure. We present the design of our system and show results from our ongoing experiments to show the effectiveness of our policy leaning framework.
In a continually evolving cyber-threat landscape, the detection and prevention of cyber attacks has become a complex task. Technological developments have led organisations to digitise the majority of their operations. This practice, however, has its perils, since cybespace offers a new attack-surface. Institutions which are tasked to protect organisations from these threats utilise mainly network data and their incident response strategy remains oblivious to the needs of the organisation when it comes to protecting operational aspects. This paper presents a system able to combine threat intelligence data, attack-trend data and organisational data (along with other data sources available) in order to achieve automated network-defence actions. Our approach combines machine learning, visual analytics and information from business processes to guide through a decision-making process for a Security Operation Centre environment. We test our system on two synthetic scenarios and show that correlating network data with non-network data for automated network defences is possible and worth investigating further.
We present AVAMAT: AntiVirus and Malware Analysis Tool - a tool for analysing the malware detection capabilities of AntiVirus (AV) products running on different operating system (OS) platforms. Even though similar tools are available, such as VirusTotal and MetaDefender, they have several limitations, which motivated the creation of our own tool. With AVAMAT we are able to analyse not only whether an AV detects a malware, but also at what stage of inspection does it detect it and on what OS. AVAMAT enables experimental campaigns to answer various research questions, ranging from the detection capabilities of AVs on OSs, to optimal ways in which AVs could be combined to improve malware detection capabilities.
The increasing growth of cybercrimes targeting mobile devices urges an efficient malware analysis platform. With the emergence of evasive malware, which is capable of detecting that it is being analyzed in virtualized environments, bare-metal analysis has become the definitive resort. Existing works mainly focus on extracting the malicious behaviors exposed during bare-metal analysis. However, after malware analysis, it is equally important to quickly restore the system to a clean state to examine the next sample. Unfortunately, state-of-the-art solutions on mobile platforms can only restore the disk, and require a time-consuming system reboot. In addition, all of the existing works require some in-guest components to assist the restoration. Therefore, a kernel-level malware is still able to detect the presence of the in-guest components. We propose Bolt, a transparent restoration mechanism for bare-metal analysis on mobile platform without rebooting. Bolt achieves a reboot-less restoration by simultaneously making a snapshot for both the physical memory and the disk. Memory snapshot is enabled by an isolated operating system (BoltOS) in the ARM TrustZone secure world, and disk snapshot is accomplished by a piece of customized firmware (BoltFTL) for flash-based block devices. Because both the BoltOS and the BoltFTL are isolated from the guest system, even kernel-level malware cannot interfere with the restoration. More importantly, Bolt does not require any modifications into the guest system. As such, Bolt is the first that simultaneously achieves efficiency, isolation, and stealthiness to recover from infection due to malware execution. We have implemented a Bolt prototype working with the Android OS. Experimental results show that Bolt can restore the guest system to a clean state in only 2.80 seconds.
In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a single image or sequence of images. This real time image/video processing requires high computational power and specialized hardware architecture and can't be achieved using general purpose CPUs. In this paper, a FPGA based generic canny edge detector is introduced. Edge detection is one of the basic steps in image processing, image analysis, image pattern recognition, and computer vision. We have implemented a re-sizable canny edge detector IP on programmable logic (PL) of PYNQ-Platform. The IP is integrated with HDMI input/output blocks and can process 1080p input video stream at 60 frames per second. As mentioned the canny edge detection IP is scalable with respect to frame size i.e. depending on the input frame size, the hardware architecture can be scaled up or down by changing the template parameters. The offloading of canny edge detection from PS to PL causes the CPU usage to drop from about 100% to 0%. Moreover, hardware based edge detector runs about 14 times faster than the software based edge detector running on Cortex-A9 ARM processor.
A growing need for scalable solutions for both machine learning and interactive analytics exists in the area of cyber-security. Machine learning aims at segmentation and classification of log events, which leads towards optimization of the threat monitoring processes. The tools for interactive analytics are required to resolve the uncertain cases, whereby machine learning algorithms are not able to provide a convincing outcome and human expertise is necessary. In this paper we focus on a case study of a security operations platform, whereby typical layers of information processing are integrated with a new database engine dedicated to approximate analytics. The engine makes it possible for the security experts to query massive log event data sets in a standard relational style. The query outputs are received orders of magnitude faster than any of the existing database solutions running with comparable resources and, in addition, they are sufficiently accurate to make the right decisions about suspicious corner cases. The engine internals are driven by the principles of information granulation and summary-based processing. They also refer to the ideas of data quantization, approximate computing, rough sets and probability propagation. In the paper we study how the engine's parameters can influence its performance within the considered environment. In addition to the results of experiments conducted on large data sets, we also discuss some of our high level design decisions including the choice of an approximate query result accuracy measure that should reflect the specifics of the considered threat monitoring operations.
In the multi-cloud tenancy environments, Web Service offers an standard approach for discovering and using capabilities in an environment that transcends ownership domains. This brings into concern the ownership and security related to Web Service governance. Our approach for this issue involves an ESB-integrated middleware for security criteria regulation on Clouds. It uses an attribute-based security policy model for the exhibition of assets consumers' security profiles and deducing service accessing decision. Assets represent computing power/functionality and information/data provided by entities. Experiments show the middleware to bring minor governance burdens on the hardware aspect, as well as better performance with colosum scaling property, dealing well with cumbersome policy files, which is probably the situation of complex composite service scenarios.
Learning analytics open up a complex landscape of privacy and policy issues, which, in turn, influence how learning analytics systems and practices are designed. Research and development is governed by regulations for data storage and management, and by research ethics. Consequently, when moving solutions out the research labs implementers meet constraints defined in national laws and justified in privacy frameworks. This paper explores how the OECD, APEC and EU privacy frameworks seek to regulate data privacy, with significant implications for the discourse of learning, and ultimately, an impact on the design of tools, architectures and practices that now are on the drawing board. A detailed list of requirements for learning analytics systems is developed, based on the new legal requirements defined in the European General Data Protection Regulation, which from 2018 will be enforced as European law. The paper also gives an initial account of how the privacy discourse in Europe, Japan, South-Korea and China is developing and reflects upon the possible impact of the different privacy frameworks on the design of LA privacy solutions in these countries. This research contributes to knowledge of how concerns about privacy and data protection related to educational data can drive a discourse on new approaches to privacy engineering based on the principles of Privacy by Design. For the LAK community, this study represents the first attempt to conceptualise the issues of privacy and learning analytics in a cross-cultural context. The paper concludes with a plan to follow up this research on privacy policies and learning analytics systems development with a new international study.
The size of counterfeiting activities is increasing day by day. These activities are encountered especially in electronics market. In this paper, a countermeasure against counterfeiting on intellectual properties (IP) on Field-Programmable Gate Arrays (FPGA) is proposed. FPGA vendors provide bitstream ciphering as an IP security solution such as battery-backed or non-volatile FPGAs. However, these solutions are secure as long as they can keep decryption key away from third parties. Key storage and key transfer over unsecure channels expose risks for these solutions. In this work, physical unclonable functions (PUFs) have been used for key generation. Generating a key from a circuit in the device solves key transfer problem. Proposed system goes through different phases when it operates. Therefore, partial reconfiguration feature of FPGAs is essential for feasibility of proposed system.
In 2007, Shacham published a seminal paper on Return-Oriented Programming (ROP), the first systematic formulation of code reuse. The paper has been highly influential, profoundly shaping the way we still think about code reuse today: an attacker analyzes the "geometry" of victim binary code to locate gadgets and chains these to craft an exploit. This model has spurred much research, with a rapid progression of increasingly sophisticated code reuse attacks and defenses over time. After ten years, the common perception is that state-of-the-art code reuse defenses are effective in significantly raising the bar and making attacks exceedingly hard. In this paper, we challenge this perception and show that an attacker going beyond "geometry" (static analysis) and considering the "dynamics" (dynamic analysis) of a victim program can easily find function call gadgets even in the presence of state-of-the-art code-reuse defenses. To support our claims, we present Newton, a run-time gadget-discovery framework based on constraint-driven dynamic taint analysis. Newton can model a broad range of defenses by mapping their properties into simple, stackable, reusable constraints, and automatically generate gadgets that comply with these constraints. Using Newton, we systematically map and compare state-of-the-art defenses, demonstrating that even simple interactions with popular server programs are adequate for finding gadgets for all state-of-the-art code-reuse defenses. We conclude with an nginx case study, which shows that a Newton-enabled attacker can craft attacks which comply with the restrictions of advanced defenses, such as CPI and context-sensitive CFI.