Biblio

Smartphone has become the tool which is used daily in modern human life. Some activities in human life, according to the usage of the smartphone can be related to the information which has a high privilege and needs a privacy. It causes the owners of the smartphone needs a system which can protect their privacy. Unfortunately, the secure the system, the unease of the usage. Hence, the system which has an invulnerable environment but also gives the ease of use is very needful. The aspect which is related to the ease of use is an authentication mechanism. Sometimes, this aspect correspondence to the effectiveness and the efficiency. This study is going to analyze the application related to this aspect which is a lock screen application. This lock screen application uses the context data based on the environment condition around the user. The context data used are GPS location and Mac Address of Wi-Fi. The system is going to detect the context and is going to determine if the smartphone needs to run the authentication mechanism or to bypass it based on the analysis of the context data. Hopefully, the smartphone application which is developed still can provide mobility and usability features, and also can protect the user privacy even though it is located in the environment which its context data is unknown.

Personal privacy is an important issue when publishing social network data. An attacker may have information to reidentify private data. So, many researchers developed anonymization techniques, such as k-anonymity, k-isomorphism, l-diversity, etc. In this paper, we focus on graph k-degree anonymity by editing edges. Our method is divided into two steps. First, we propose an efficient algorithm to find a new degree sequence with theoretically minimal edit cost. Second, we insert and delete edges based on the new degree sequence to achieve k-degree anonymity.

Traditional image compressed sensing (CS) coding frameworks solve an inverse problem that is based on the measurement coding tools (prediction, quantization, entropy coding, etc.) and the optimization based image reconstruction method. These CS coding frameworks face the challenges of improving the coding efficiency at the encoder, while also suffering from high computational complexity at the decoder. In this paper, we move forward a step and propose a novel deep network based CS coding framework of natural images, which consists of three sub-networks: sampling sub-network, offset sub-network and reconstruction sub-network that responsible for sampling, quantization and reconstruction, respectively. By cooperatively utilizing these sub-networks, it can be trained in the form of an end-to-end metric with a proposed rate-distortion optimization loss function. The proposed framework not only improves the coding performance, but also reduces the computational cost of the image reconstruction dramatically. Experimental results on benchmark datasets demonstrate that the proposed method is capable of achieving superior rate-distortion performance against state-of-the-art methods.

Due to the rapid development of internet in our daily life, protecting privacy has become a focus of attention. To create privacy-preserving database and prevent illegal user access the database, oblivious transfer with access control (OTAC) was proposed, which is a cryptographic primitive that extends from oblivious transfer (OT). It allows a user to anonymously query a database where each message is protected by an access control policy and only if the user' s attribute satisfy that access control policy can obtain it. In this paper, we propose a new protocol for OTAC by using elliptic curve cryptography, which is more efficient compared to the existing similar protocols. In our scheme, we also preserves user's anonymity and ensures that the user's attribute is not disclosed to the sender. Additionally, our construction guarantees the user to verify the correctness of messages recovered at the end of each transfer phase.

This paper proposes a lightweight and privacy-preserving data aggregation scheme for dynamic electricity pricing based billing in smart grids using the concept of single-pass authenticated encryption (AE). Unlike existing literature that only considers static pricing, to the best of our knowledge, this is the first paper to address privacy under dynamic pricing.

In an augmented reality system, labelling technique is a very useful assistant technique for browsing and understanding unfamiliar objects or environments, through which the superimposed virtual labels of words or pictures on the real scene provide convenient information to the viewers, expand the recognition to area of interests and promote the interaction with real scene. How to design the layout of labels in user's field of view, keep the clarity of virtual information and balance the ratio between virtual information and real scene information is a key problem in the field of view management. This paper presents the empirical results extracted from experiment aiming at the user's visual perception to labelling layout, which reflects the subjective preferences to different factors influencing the labelling result. Statistical analysis of the experiment results shows the intuitive visual judgement accomplished by subjects. The quantitative measurement of clutter indicates the change induced by labels on real scene, therefore contributing the label design on view management in future.

Millimeter Wave (mmWave) networks can deliver multi-Gbps wireless links that use extremely narrow directional beams. This provides us with a new way to exploit spatial reuse in order to scale network throughput. In this work, we present MilliNet, the first millimeter wave network that can exploit dense spatial reuse to allow many links to operate in parallel in a confined space and scale the wireless throughput with the number of clients. Results from a 60 GHz testbed show that MilliNet can deliver a total wireless network data rate of more than 38 Gbps for 10 clients which is 5.8× higher than current 802.11 mmWave standards.

Conventional HVAC control systems are usually incognizant of the physical structures and materials of buildings. These systems merely follow pre-set HVAC control logic based on abstract building thermal response models, which are rough approximations to true physical models, ignoring dynamic spatial variations in built environments. To enable more accurate and responsive HVAC control, this paper introduces the notion of self-aware smart buildings, such that buildings are able to explicitly construct physical models of themselves (e.g., incorporating building structures and materials, and thermal flow dynamics). The question is how to enable self-aware buildings that automatically acquire dynamic knowledge of themselves. This paper presents a novel approach using augmented reality. The extensive user-environment interactions in augmented reality not only can provide intuitive user interfaces for building systems, but also can capture the physical structures and possibly materials of buildings accurately to enable real-time building simulation and control. This paper presents a building system prototype incorporating augmented reality, and discusses its applications.

Security research has made extensive use of exhaustive Internet-wide scans over the recent years, as they can provide significant insights into the overall state of security of the Internet, and ZMap made scanning the entire IPv4 address space practical. However, the IPv4 address space is exhausted, and a switch to IPv6, the only accepted long-term solution, is inevitable. In turn, to better understand the security of devices connected to the Internet, including in particular Internet of Things devices, it is imperative to include IPv6 addresses in security evaluations and scans. Unfortunately, it is practically infeasible to iterate through the entire IPv6 address space, as it is 2ˆ96 times larger than the IPv4 address space. Therefore, enumeration of active hosts prior to scanning is necessary. Without it, we will be unable to investigate the overall security of Internet-connected devices in the future. In this paper, we introduce a novel technique to enumerate an active part of the IPv6 address space by walking DNSSEC-signed IPv6 reverse zones. Subsequently, by scanning the enumerated addresses, we uncover significant security problems: the exposure of sensitive data, and incorrectly controlled access to hosts, such as access to routing infrastructure via administrative interfaces, all of which were accessible via IPv6. Furthermore, from our analysis of the differences between accessing dual-stack hosts via IPv6 and IPv4, we hypothesize that the root cause is that machines automatically and by default take on globally routable IPv6 addresses. This is a practice that the affected system administrators appear unaware of, as the respective services are almost always properly protected from unauthorized access via IPv4. Our findings indicate (i) that enumerating active IPv6 hosts is practical without a preferential network position contrary to common belief, (ii) that the security of active IPv6 hosts is currently still lagging behind the security state of IPv4 hosts, and (iii) that unintended IPv6 connectivity is a major security issue for unaware system administrators.

We investigate mobile phone pointing in Spatial Augmented Reality (SAR). Three pointing methods are compared, raycasting, viewport, and tangible (i.e. direct contact), using a five-projector "full" SAR environment with targets distributed on varying surfaces. Participants were permitted free movement in the environment to create realistic variations in target occlusion and target incident angle. Our results show raycast is fastest for high and distant targets, tangible is fastest for targets in close proximity to the user, and viewport performance is in between.

The growing use of smart phones has also given opportunity to the intruders to create malicious apps thereby the security and privacy concerns of a novice user has also grown. This research focuses on the privacy concerns of a user who unknowingly installs a malicious apps created by the programmer. In this paper we created an attack scenario and created an app capable of compromising the privacy of the users. After accepting all the permissions by the user while installing the app, the app allows us to track the live location of the Android device and continuously sends the GPS coordinates to the server. This spying app is also capable of sending the call log details of the user. This paper evaluates two leading smart phone operating systems- Android and IOS to find out the flexibility provided by the two operating systems to their programmers to create the malicious apps.

Oblivious RAM is a cryptographic primitive that embodies one of the cornerstones of privacy-preserving technologies for database protection. While any Oblivious RAM (ORAM) construction offers access pattern hiding, there does not seem to be a construction that is safe against the potential leakage due to knowledge about the number of accesses performed by a client. Such leakage constitutes a privacy violation, as client data may be stored in a domain specific fashion. In this work, we examine this leakage by considering an adversary that can probe the server that stores an ORAM database, and who takes regular snapshots of it. We show that even against such a weak adversary, no major ORAM architecture is resilient, except for the trivial case, where the client scans the whole database in order to access a single element. In fact, we argue that constructing a non-trivial ORAM that is formally resilient seems impossible. Moreover, we quantify the leakage of different constructions to show which architecture offers the best privacy in practice.

Leading steganography systems make use of the Syndrome-Trellis Code (STC) algorithm to minimize a distortion function while encoding the desired payload, but this constrains the distortion function to be additive. The Gibbs Embedding algorithm works for a certain class of non-additive distortion functions, but has its own limitations and is highly complex. In this short paper we show that it is possible to modify the STC algorithm in a simple way, to minimize a non-additive distortion function suboptimally. We use it for two examples. First, applying it to the S-UNIWARD distortion function, we show that it does indeed reduce distortion, compared with minimizing the additive approximation currently used in image steganography, but that it makes the payload more – not less – detectable. This parallels research attempting to use Gibbs Embedding for the same task. Second, we apply it to distortion defined by the output of a specific detector, as a counter-move in the steganography game. However, unless the Warden is forced to move first (by fixing the detector) this is highly detectable.

Given graphs with millions or billions of vertices and edges, how can we efficiently make inferences based on partial knowledge? Loopy Belief Propagation(LBP) is a graph inference algorithm widely used in various applications including social network analysis, malware detection, recommendation, and image restoration. The algorithm calculates approximate marginal probabilities of vertices in a graph within a linear running time proportional to the number of edges. However, when it comes to real-world graphs with millions or billions of vertices and edges, this cost overwhelms the computing power of a single machine. Moreover, this kind of large-scale graphs does not fit into the memory of a single machine. Although several distributed LBP methods have been proposed, previous works do not consider the properties of real-world graphs, especially the effect of power-law degree distribution on LBP. Therefore, our work focuses on developing a fast and scalable LBP for such large real-world graphs on distributed environment. In this paper, we propose DLBP, a Distributed Loopy Belief Propagation algorithm which efficiently computes LBP in a distributed manner across multiple machines. By setting the correct convergence criterion and carefully scheduling the computations, DLBP provides up to 10.7x speed up compared to standard distributed LBP. We show that DLBP demonstrates near-linear scalability with respect to the number of machines as well as the number of edges.

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatial-temporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatial-temporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonym-varying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.

The paradigm of fog computing has set new trends and heights in the modern world networking and have overcome the major technical complexities of cloud computing. It is not a replacement of cloud computing technology but it just adds feasible advanced characteristics to existing cloud computing paradigm.fog computing not only provide storage, networking and computing services but also provide a platform for IoT (internet of things). However, the fog computing technology also arise the threat to privacy and security of the data and services. The existing security and privacy mechanisms of the cloud computing cannot be applied to the fog computing directly due to its basic characteristics of large-scale geo-distribution, mobility and heterogeneity. This article provides an overview of the present existing issues and challenges in fog computing.

Mobile communication networks connect much of the world's population. The security of users' calls, SMSs, and mobile data depends on the guarantees provided by the Authenticated Key Exchange protocols used. For the next-generation network (5G), the 3GPP group has standardized the 5G AKA protocol for this purpose. We provide the first comprehensive formal model of a protocol from the AKA family: 5G AKA. We also extract precise requirements from the 3GPP standards defining 5G and we identify missing security goals. Using the security protocol verification tool Tamarin, we conduct a full, systematic, security evaluation of the model with respect to the 5G security goals. Our automated analysis identifies the minimal security assumptions required for each security goal and we find that some critical security goals are not met, except under additional assumptions missing from the standard. Finally, we make explicit recommendations with provably secure fixes for the attacks and weaknesses we found. 

As a decentralized and distributed secure storage technology, the notion of blockchain is now widely used for electronic trading in finance, for issuing digital certificates, for copyrights management, and for many other security-critical applications. With applications in so many domains with high-assurance requirements, the formalization and verification of safety and security properties of blockchain becomes essential, and the aim of the present paper. We present the model-based formalization, simulation and verification of a blockchain protocol by using the SDL formalism of Telelogic Tau. We consider the hierarchical and modular SDL model of the blockchain protocol and exercise a methodology to formally simulate and verify it. This way, we show how to effectively increase the security and safety of blockchain in order to meet high assurance requirements demanded by its application domains. Our work also provides effective support for assessing different network consensus algorithms, which are key components in blockchain protocols, as well as on the topology of blockchain networks. In conclusion, our approach contributes to setting up a verification methodology for future blockchain standards in digital trading.

Control-hijacking attacks include code injection attacks and code reuse attacks. In recent years, with the emergence of the defense mechanism data-execution prevention(DEP), code reuse attacks have become mainstream, such as return-oriented programming(ROP), Jump-Oriented Programming(JOP), and Counterfeit Object-oriented Programming(COOP). And a series of defensive measures have been proposed, such as DEP, address space layout randomization (ASLR), coarse-grained Control-Flow Integrity(CFI) and fine-grained CFI. In this paper, we propose a new attack called function-oriented programming(FOP) to construct malicious program behavior. FOP takes advantage of the existing function of the C program to induce attack. We propose concrete algorithms for FOP gadgets and build a tool to identify FOP gadgets. FOP can successfully bypass coarse-grained CFI, and FOP also can bypass some existing fine-grained CFI technologies, such as shadow stack technology. We show a real-world attack for proftpd1.3.0 server in the Linux x64 environment. We believe that the FOP attack will encourage people to come up with more effective defense measures.

Blockchain-based cryptocurrencies secure a decentralized consensus protocol by incentives. The protocol participants, called miners, generate (mine) a series of blocks, each containing monetary transactions created by system users. As incentive for participation, miners receive newly minted currency and transaction fees paid by transaction creators. Blockchain bandwidth limits lead users to pay increasing fees in order to prioritize their transactions. However, most prior work focused on models where fees are negligible. In a notable exception, Carlsten et al. [17] postulated that if incentives come only from fees then a mining gap would form\textasciitilde— miners would avoid mining when the available fees are insufficient. In this work, we analyze cryptocurrency security in realistic settings, taking into account all elements of expenses and rewards. To study when gaps form, we analyze the system as a game we call the gap game. We analyze the game with a combination of symbolic and numeric analysis tools in a wide range of scenarios. Our analysis confirms Carlsten et al.'s postulate; indeed, we show that gaps form well before fees are the only incentive, and analyze the implications on security. Perhaps surprisingly, we show that different miners choose different gap sizes to optimize their utility, even when their operating costs are identical. Alarmingly, we see that the system incentivizes large miner coalitions, reducing system decentralization. We describe the required conditions to avoid the incentive misalignment, providing guidelines for future cryptocurrency design.

Motivation: The security of any system is a direct consequence of stakeholders' decisions regarding security requirements. Such decisions are taken with varying degrees of expertise, and little is currently understood about how various demographics - security experts, general computer scientists, managers - approach security decisions and the strategies that underpin those decisions. What are the typical decision patterns, the consequences of such patterns and their impact on the security of the system in question? Nor is there any substantial understanding of how the strategies and decision patterns of these different groups contrast. Is security expertise necessarily an advantage when making security decisions in a given context? Answers to these questions are key to understanding the "how" and "why" behind security decision processes. The Game: In this talk1, we present a tabletop game: Decisions and Disruptions (D-D)2 that tasks a group of players with managing the security of a small utility company while facing a variety of threats. The game is kept short - 2 hours - and simple enough to be played without prior training. A cyber-physical infrastructure, depicted through a Lego\textregistered board, makes the game easy to understand and accessible to players from varying backgrounds and security expertise, without being too trivial a setting for security experts. Key insights: We played D-D with 43 players divided into homogeneous groups: 4 groups of security experts, 4 groups of nontechnical managers and 4 groups of general computer scientists. • Strategies: Security experts had a strong interest in advanced technological solutions and tended to neglect intelligence gathering, to their own detriment. Managers, too, were technology-driven and focused on data protection while neglecting human factors more than other groups. Computer scientists tended to balance human factors and intelligence gathering with technical solutions, and achieved the best results of the three demographics. • Decision Processes: Technical experience significantly changes the way players think. Teams with little technical experience had shallow, intuition-driven discussions with few concrete arguments. Technical teams, and the most experienced in particular, had much richer debates, driven by concrete scenarios, anecdotes from experience, and procedural thinking. Security experts showed a high confidence in their decisions - despite some of them having bad consequences - while the other groups tended to doubt their own skills - even when they were playing good games. • Patterns: A number of characteristic plays were identified, some good (balance between priorities, open-mindedness, and adapting strategies based on inputs that challenge one's pre-conceptions), some bad (excessive focus on particular issues, confidence in charismatic leaders), some ugly ("tunnel vision" syndrome by over-confident players). These patterns are documented in the full paper - showing the virtue of the positive ones, discouraging the negative ones, and inviting the readers to do their own introspection. Conclusion: Beyond the analysis of the security decisions of the three demographics, there is a definite educational and awareness-raising aspect to D-D (as noted consistently by players in all our subject groups). Game boxes will be brought to the conference for demonstration purposes, and the audience will be invited to experiment with D-D themselves, make their own decisions, and reflect on their own perception of security.

In this paper we propose a new algorithm to detect Advanced Persistent Threats (APT's) that relies on a graph model of HTTP traffic. We also implement a complete detection system with a web interface that allows to interactively analyze the data. We perform a complete parameter study and experimental evaluation using data collected on a real network. The results show that the performance of our system is comparable to currently available antiviruses, although antiviruses use signatures to detect known malwares while our algorithm solely uses behavior analysis to detect new undocumented attacks.

In this paper, we propose a graph-based algorithmic technique for malware detection, utilizing the System-call Dependency Graphs (ScDG) obtained through taint analysis traces. We leverage the grouping of system-calls into system-call groups with respect to their functionality to merge disjoint vertices of ScDG graphs, transforming them to Group Relation Graphs (GrG); note that, the GrG graphs represent malware's behavior being hence more resilient to probable mutations of its structure. More precisely, we extend the use of GrG graphs by mapping their vertices on the plane utilizing the degrees and the vertex-weights of a specific underlying graph of the GrG graph as to compute domination relations. Furthermore, we investigate how the activity of each system-call group could be utilized in order to distinguish graph-representations of malware and benign software. The domination relations among the vertices of GrG graphs result to a new graph representation that we call Coverage Graph of the GrG graph. Finally, we evaluate the potentials of our detection model using graph similarity between Coverage Graphs of known malicious and benign software samples of various types.

Vulnerabilities in hypervisors are crucial in multi-tenant clouds and attractive for attackers because a vulnerability in the hypervisor can undermine all the virtual machine (VM) security. This paper focuses on vulnerabilities in instruction emulators inside hypervisors. Vulnerabilities in instruction emulators are not rare; CVE-2017-2583, CVE-2016-9756, CVE-2015-0239, CVE-2014-3647, to name a few. For backward compatibility with legacy x86 CPUs, conventional hypervisors emulate arbitrary instructions at any time if requested. This design leads to a large attack surface, making it hard to get rid of vulnerabilities in the emulator. This paper proposes FWinst that narrows the attack surface against vulnerabilities in the emulator. The key insight behind FWinst is that the emulator should emulate only a small subset of instructions, depending on the underlying CPU micro-architecture and the hypervisor configuration. FWinst recognizes emulation contexts in which the instruction emulator is invoked, and identifies a legitimate subset of instructions that are allowed to be emulated in the current context. By filtering out illegitimate instructions, FWinst narrows the attack surface. In particular, FWinst is effective on recent x86 micro-architectures because the legitimate subset becomes very small. Our experimental results demonstrate FWinst prevents existing vulnerabilities in the emulator from being exploited on Westmere micro-architecture, and the runtime overhead is negligible.

The NEREIDA wave generation power plant installed in Mutriku, Spain is a multiple Oscillating Water Column (OWC) plant. The power takeoff consists of a Wells turbine coupled to a Doubly Fed Induction Generator (DFIG). The stalling behavior present in the Wells turbine limits the generated power. This paper presents the modeling and a Harmony Search Algorithm-based airflow control of the OWC. The Harmony Search Algorithm (HSA) is proposed to help overcome the limitations of a traditionally tuned PID. An investigation between HSA-tuned controller and the traditionally tuned controller has been performed. Results of the controlled and uncontrolled plant prove the effectiveness of the airflow control and the superiority of the HSA-tuned controller.