Biblio

Code obfuscation is the de facto standard to protect intellectual property when delivering code in an unmanaged environment. It relies on additive layers of code tangling techniques, white-box encryption calls and platform-specific or tool-specific countermeasures to make it harder for a reverse engineer to access critical pieces of data or to understand core algorithms. The literature provides plenty of different obfuscation techniques that can be used at compile time to transform data or control flow in order to provide some kind of protection against different reverse engineering scenarii. Scheduling code transformations to optimize a given metric is known as the pass scheduling problem, a problem known to be NP-hard, but solved in a practical way using hard-coded sequences that are generally satisfactory. Adding code obfuscation to the problem introduces two new dimensions. First, as a code obfuscator needs to find a balance between obfuscation and performance, pass scheduling becomes a multi-criteria optimization problem. Second, obfuscation passes transform their inputs in unconventional ways, which means some pass combinations may not be desirable or even valid. This paper highlights several issues met when blindly chaining different kind of obfuscation and optimization passes, emphasizing the need of a formal model to combine them. It proposes a non-intrusive formalism to leverage on sequential pass management techniques. The model is validated on real-world scenarii gathered during the development of an industrial-strength obfuscator on top of the LLVM compiler infrastructure.

Embedded systems that communicate with each other over the internet and build up a larger, loosely coupled (hardware) system with an unknown configuration at runtime is often referred to as a cyberphysical system. Many of these systems can become, due to its associated risks during their operation, safety critical. With increased complexity of such systems, the number of configurations can either be infinite or even unknown at design time. Hence, a certification at design time for such systems that documents a safe interaction for all possible configurations of all participants at runtime can become unfeasible. If such systems come together in a new configuration, a mechanism is required that can decide whether or not it is safe for them to interact. Such a mechanism can generally not be part of such systems for the sake of trust. Therefore, we present in the following sections the SEnSE device, short for Secure and Safe Embedded, that tackles these challenges and provides a secure and safe integration of safety-critical embedded systems.

The paper outlines the concept of the Digital economy, defines the role and types of intellectual resources in the context of digitalization of the economy, reviews existing approaches and methods to intellectual property valuation and analyzes drawbacks of quantitative evaluation of intellectual resources (based intellectual property valuation) related to: uncertainty, noisy data, heterogeneity of resources, nonformalizability, lack of reliable tools for measuring the parameters of intellectual resources and non-stationary development of intellectual resources. The results of the study offer the ways of further development of methods for quantitative evaluation of intellectual resources (inter alia aimed at their capitalization).

An ideal audio retrieval method should be not only highly efficient in identifying an audio track from a massive audio dataset, but also robust to any distortion. Unfortunately, none of the audio retrieval methods is robust to all types of distortions. An audio retrieval method has to do with both the audio fingerprint and the strategy, especially how they are combined. We argue that the Sampling and Counting Method (SC), a state-of-the-art audio retrieval method, would be promising towards an ideal audio retrieval method, if we could make it robust to time-stretch and pitch-stretch. Towards this objective, this paper proposes a turning point alignment method to enhance SC with resistance to time-stretch, which makes Philips and Philips-like fingerprints resist to time-stretch. Experimental results show that our approach can resist to time-stretch from 70% to 130%, which is on a par to the state-of-the-art methods. It also marginally improves the retrieval performance with various noise distortions.

Indoor localization of unknown acoustic events with MEMS microphone arrays have a huge potential in applications like home assisted living and surveillance. This article presents an Angle of Arrival (AoA) fingerprinting method for use in Wireless Acoustic Sensor Networks (WASNs) with low-profile microphone arrays. In a first research phase, acoustic measurements are performed in an anechoic room to evaluate two computationally efficient time domain delay-based AoA algorithms: one based on dot product calculations and another based on dot products with a PHAse Transform (PHAT). The evaluation of the algorithms is conducted with two sound events: white noise and a female voice. The algorithms are able to calculate the AoA with Root Mean Square Errors (RMSEs) of 3.5° for white noise and 9.8° to 16° for female vocal sounds. In the second research phase, an AoA fingerprinting algorithm is developed for acoustic event localization. The proposed solution is experimentally verified in a room of 4.25 m by 9.20 m with 4 acoustic sensor nodes. Acoustic fingerprints of white noise, recorded along a predefined grid in the room, are used to localize white noise and vocal sounds. The localization errors are evaluated using one node at a time, resulting in mean localization errors between 0.65 m and 0.98 m for white noise and between 1.18 m and 1.52 m for vocal sounds.

Advancements in computing, communication, and sensing technologies are making it possible to embed, control, and gather vital information from tiny devices that are being deployed and utilized in practically every aspect of our modernized society. From smart home appliances to municipal water and electric industrial facilities to our everyday work environments, the next Internet frontier, dubbed IoT, is promising to revolutionize our lives and tackle some of our nations' most pressing challenges. While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements in many aspects and diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. Further, such compromised devices will undeniably be leveraged as the next generation of botnets, given their increased processing capabilities and abundant bandwidth. While several demonstrations exist in the literature describing the exploitation procedures of a number of IoT devices, the up-to-date inference, characterization, and analysis of unsolicited IoT devices that are currently deployed "in the wild" is still in its infancy. In this article, we address this imperative task by leveraging active and passive measurements to report on unsolicited Internet-scale IoT devices. This work describes a first step toward exploring the utilization of passive measurements in combination with the results of active measurements to shed light on the Internet-scale insecurities of the IoT paradigm. By correlating results of Internet-wide scanning with Internet background radiation traffic, we disclose close to 14,000 compromised IoT devices in diverse sectors, including critical infrastructure and smart home appliances. To this end, we also analyze their generated traffic to create effective mitigation signatures that could be deployed in local IoT realms. To support largescale empirical data analytics in the context of IoT, we make available the inferred and extracted IoT malicious raw data through an authenticated front-end service. The outcomes of this work confirm the existence of such compromised devices on an Internet scale, while the generated inferences and insights are postulated to be employed for inferring other similarly compromised IoT devices, in addition to contributing to IoT cyber security situational awareness.

With the development of Internet of Things, numerous IoT devices have been brought into our daily lives. Bluetooth Low Energy (BLE), due to the low energy consumption and generic service stack, has become one of the most popular wireless communication technologies for IoT. However, because of the short communication range and exclusive connection pattern, a BLE-equipped device can only be used by a single user near the device. To fully explore the benefits of BLE and make BLE-equipped devices truly accessible over the Internet as IoT devices, in this paper, we propose a cloud-based software framework that can enable multiple users to interact with various BLE IoT devices over the Internet. This framework includes an agent program, a suite of services hosting in cloud, and a set of RESTful APIs exposed to Internet users. Given the availability of this framework, the access to BLE devices can be extended from local to the Internet scale without any software or hardware changes to BLE devices, and more importantly, shared usage of remote BLE devices over the Internet is also made available.

Let Addk,N denote the Boolean function which takes as input k strings of N bits each, representing k numbers a(1),…,a(k) in \0,1,…,2N−1\, and outputs 1 if and only if a(1) + ⋯ + a(k) ≥ 2N. Let MAJt,n denote a monotone unweighted threshold gate, i.e., the Boolean function which takes as input a single string x ∈ \0,1\n and outputs 1 if and only if x1 + ⋯ + xn ≥ t. The function Addk,N may be viewed as a monotone function that performs addition, and MAJt,n may be viewed as a monotone gate that performs counting. We refer to circuits that are composed of MAJ gates as monotone majority circuits. The main result of this paper is an exponential lower bound on the size of bounded-depth monotone majority circuits that compute Addk,N. More precisely, we show that for any constant d ≥ 2, any depth-d monotone majority circuit that computes Addd,N must have size 2Ω(N1/d). As Addk,N can be computed by a single monotone weighted threshold gate (that uses exponentially large weights), our lower bound implies that constant-depth monotone majority circuits require exponential size to simulate monotone weighted threshold gates. This answers a question posed by Goldmann and Karpinski (STOC’93) and recently restated by Håstad (2010, 2014). We also show that our lower bound is essentially best possible, by constructing a depth-d, size 2O(N1/d) monotone majority circuit for Addd,N. As a corollary of our lower bound, we significantly strengthen a classical theorem in circuit complexity due to Ajtai and Gurevich (JACM’87). They exhibited a monotone function that is in AC0 but requires super-polynomial size for any constant-depth monotone circuit composed of unbounded fan-in AND and OR gates. We describe a monotone function that is in depth-3 AC0 but requires exponential size monotone circuits of any constant depth, even if the circuits are composed of MAJ gates.

Large-scale parallel applications with complex global data dependencies beyond those of reductions pose significant scalability challenges in an asynchronous runtime system. Internodal challenges include identifying the all-to-all communication of data dependencies among the nodes. Intranodal challenges include gathering together these data dependencies into usable data objects while avoiding data duplication. This paper addresses these challenges within the context of a large-scale, industrial coal boiler simulation using the Uintah asynchronous many-task runtime system on GPU architectures. We show significant reduction in time spent analyzing data dependencies through refinements in our dependency search algorithm. Multiple task graphs are used to eliminate subsequent analysis when task graphs change in predictable and repeatable ways. Using a combined data store and task scheduler redesign reduces data dependency duplication ensuring that problems fit within host and GPU memory. These modifications did not require any changes to application code or sweeping changes to the Uintah runtime system. We report results running on the DOE Titan system on 119K CPU cores and 7.5K GPUs simultaneously. Our solutions can be generalized to other task dependency problems with global dependencies among thousands of nodes which must be processed efficiently at large scale.

In this article, a detailed survey of the quantum approach to image processing is presented. Recently, it has been established that existing quantum algorithms are applicable to image processing tasks allowing quantum informational models of classical image processing. However, efforts continue in identifying the diversity of its applicability in various image processing domains. Here, in addition to reviewing some of the critical image processing applications that quantum mechanics have targeted, such as denoising, edge detection, image storage, retrieval, and compression, this study will also highlight the complexities in transitioning from the classical to the quantum domain. This article shall establish theoretical fundamentals, analyze performance and evaluation, draw key statistical evidence to support claims, and provide recommendations based on published literature mostly during the period from 2010 to 2015.

Single Root I/O Virtualization (SRIOV) allows one physical device to be used by multiple virtual machines simultaneously without the mediation from the hypervisor. Such technique significantly decreases the overhead of I/O virtualization. But according to our latest findings, in the meantime, it introduces a high-risk security issue that enables an adversary-controlled VM to cut off the connectivity of the host machine, given the limited filtering capabilities provided by the SRIOV devices. As showcase, we demonstrate two attacks against SRIOV NIC by exploiting a vulnerability in the standard network management protocol, OAM. The vulnerability surfaces because SRIOV NICs treat the packets passing through OAM as data-plane packets and allow untrusted VMs to send and receive these packets on behalf of the host. By examining several off-the-shelf SRIOV NICs and switches, we show such attack can easily turn off the network connection within a short period of time. In the end, we propose a defense mechanism which runs on the existing hardware and can be readily deployed.

Deployment of billions of Commercial Off-The-Shelf (COTS) RFID tags has drawn much of the attention of the research community because of the performance gaps of current systems. In particular, hash-enabled protocol (HEP) is one of the most thoroughly studied topics in the past decade. HEPs are designed for a wide spectrum of notable applications (e.g., missing detection) without need to collect all tags. HEPs assume that each tag contains a hash function, such that a tag can select a random but predicable time slot to reply with a one-bit presence signal that shows its existence. However, the hash function has never been implemented in COTS tags in reality, which makes HEPs a 10-year untouchable mirage. This work designs and implements a group of analog on-tag hash primitives (called Tash) for COTS Gen2-compatible RFID systems, which moves prior HEPs forward from theory to practice. In particular, we design three types of hash primitives, namely, tash function, tash table function and tash operator. All of these hash primitives are implemented through selective reading, which is a fundamental and mandatory functionality specified in Gen2 protocol, without any hardware modification and fabrication. We further apply our hash primitives in two typical HEP applications (i.e., cardinality estimation and missing detection) to show the feasibility and effectiveness of Tash. Results from our prototype, which is composed of one ImpinJ reader and 3,000 Alien tags, demonstrate that the new design lowers 60% of the communication overhead in the air. The tash operator can additionally introduce an overhead drop of 29.7%.

Dynamic taint analysis and symbolic execution find many important applications in security-related program analyses. However, current techniques for such analyses do not take proper account of control transfers due to exceptions. As a result, they can fail to account for implicit flows arising from exception-based control transfers, leading to loss of precision and potential false negatives in analysis results. While the idea of using exceptions for obfuscating (unconditional) control transfers is well known, we are not aware of any prior work discussing the use of exceptions to implement conditional control transfers and implicit information flows. This paper demonstrates the problems that can arise in existing dynamic taint analysis and symbolic execution systems due to exception-based implicit information flows and proposes a generic architecture-agnostic solution for reasoning about the behavior of code using user-defined exception handlers. Experimental results from a prototype implementation indicate that the ideas described produce better results than current state-of-the-art systems.

Nowadays, the database security problem is becoming significantly interesting in the data mining field. How can exploit legitimate data and avoid disclosing sensitive information. There have been many approaches in which the outstanding solution among them is privacy preservation in association rule mining to hide sensitive rules. In the recent years, a meta-heuristic algorithm is becoming effective for this goal, the algorithm is applied in the cuckoo optimization algorithm (COA4ARH). In this paper, an improved proposal of the COA4ARH to minimize the side effect of the missing non-sensitive rules will be introduced. The main contribution of this study is a new pre-process stage to determine the minimum number of necessary transactions for the process of initializing an initial habitat, thus restriction of modified operation on the original data. To evaluate the effectiveness of the proposed method, we conducted several experiments on the real datasets. The experimental results show that the improved approach has higher performance in compared to the original algorithm.

Thumbnail preserving encryption (TPE) was suggested by Wright et al. [Information Hiding & Multimedia Security Workshop 2015] as a way to balance privacy and usability for online image sharing. The idea is to encrypt a plaintext image into a ciphertext image that has roughly the same thumbnail as well as retaining the original image format. At the same time, TPE allows users to take advantage of much of the functionality of online photo management tools, while still providing some level of privacy against the service provider. In this work we present two new approximate TPE encryption schemes. In our schemes, ciphertexts and plaintexts have perceptually similar, but not identical, thumbnails. Our constructions are the first TPE schemes designed to work well with JPEG compression. In addition, we show that they also have provable security guarantees that characterize precisely what information about the plaintext is leaked by the ciphertext image. We empirically evaluate our schemes according to the similarity of plaintext & ciphertext thumbnails, increase in file size under JPEG compression, preservation of perceptual image hashes, among other aspects. We also show how approximate TPE can be an effective tool to thwart inference attacks by machine-learning image classifiers, which have shown to be effective against other image obfuscation techniques.

This paper presents the results of a cryptographic analysis of the protocols used by the European Rail Traffic Management System (ERTMS). A stack of three protocols secures the communication between trains and trackside equipment; encrypted radio communication is provided by the GSM-R protocol, on top of this the EuroRadio protocol provides authentication for a train control application-level protocol. We present an attack which exploits weaknesses in all three protocols: GSM-R has the same well known weaknesses as the GSM protocol, and we present a new collision attack against the EuroRadio protocol. Combined with design weaknesses in the application-level protocol, these vulnerabilities allow an attacker, who observes a MAC collision, to forge train control messages. We demonstrate this attack with a proof of concept using train control messages we have generated ourselves. Currently, ERTMS is only used to send small amounts of data for short sessions, therefore this attack does not present an immediate danger. However, if EuroRadio was to be used to transfer larger amounts of data trains would become vulnerable to this attack. Additionally, we calculate that, under reasonable assumptions, an attacker who could monitor all backend control centres in a country the size of the UK for 45 days would have a 1% chance of being able to take control of a train.

Smart Grids (SGs) are Critical Infrastructures (CI), which are responsible for controlling and maintaining the distribution of electricity. To manage this task, modern SGs integrate an Information and Communication Infrastructure (ICT) beside the electrical power grid. Aside from the benefits derived from the increasing control and management capabilities offered by the ICT, unfortunately the introduction of this cyber layer provides an attractive attack surface for hackers. As a consequence, security becomes a fundamental prerequisite to be fulfilled. In this context, the adoption of Systems Engineering (SE) tools combined with Modeling and Simulation (M&S) techniques represent a promising solution to support the evaluation process of a SG during early design stages. In particular, the paper investigates on the identification, modeling and assessment of attacks in SG environments, by proposing a model for representing attack scenarios as a combination of attack types, attack schema and their temporal occurrence. Simulation techniques are exploited to enable the execution of such attack combinations in the SG domain. Specifically, a simulator, which allows to assess the SG behaviour to identify possible flaws and provide preventive actions before its realization, is developed on the basis of the proposed model and exemplified through a case study.

Audio-CAPTCHA prevents malicious bots from attacking Web services and provides Web accessibility for visually-impaired persons. Most of the conventional methods employ statistical noise to distort sounds and let users remember and spell the words, which are difficult and laborious work for humans. In this paper, we utilize the difficulty on speaker-independent recognition for ASR machines instead of distortion with statistical noise. Our scheme synthesizes various voices by changing voice speed, pitch and native language of speakers. Moreover, we employ semantic identification problems between random phoneme sequences and meaningful words to release users from remembering and spelling words, so it improves the accuracy of humans and usability. We also evaluated our scheme in several experiments.

Recent increases in the field of infrastructure has led to the emerging of cloud computing a virtualized computing platform. This technology provides a lot of pros like rapid elasticity, ubiquitous network access and on-demand access etc. Compare to other technologies cloud computing provides many essential services. As the elasticity and scalability increases the chance for vulnerability of the system is also high. There are many known and unknown security risks and challenges present in this environment. In this research an environment is proposed which can handle security issues and deploys various security levels. The system handles the security of various infrastructure like VM and also handles the Dynamic infrastructure request control. One of the key feature of proposed approach is Dual authorization in which all account related data will be authorized by two privileged administrators of the cloud. The auto scalability feature of the cloud is be made secure for on-demand service request handling by providing an on-demand scheduler who will process the on-demand request and assign the required infrastructure. Combining these two approaches provides a secure environment for cloud users as well as handle On-demand Infrastructure request.

Software-defined networks provide new facilities for deploying security mechanisms dynamically. In particular, it is possible to build and adjust security chains to protect the infrastructures, by combining different security functions, such as firewalls, intrusion detection systems and services for preventing data leakage. It is important to ensure that these security chains, in view of their complexity and dynamics, are consistent and do not include security violations. We propose in this paper an automated strategy for supporting the verification of security chains in software-defined networks. It relies on an architecture integrating formal verification methods for checking both the control and data planes of these chains, before their deployment. We describe algorithms for translating specifications of security chains into formal models that can then be verified by SMT1 solving or model checking. Our solution is prototyped as a package, named Synaptic, built as an extension of the Frenetic family of SDN programming languages. The performances of our approach are evaluated through extensive experimentations based on the CVC4, veriT, and nuXmv checkers.

Quantum Key Distribution (QKD) allows two parties to establish a shared secret key secure against an all-powerful adversary. Typically, one designs new QKD protocols and then analyzes their maximal tolerated noise mathematically. If the noise in the quantum channel connecting the two parties is higher than this threshold value, they must abort. In this paper we design and evaluate a new real-coded Genetic Algorithm which takes as input statistics on a particular quantum channel (found using standard channel estimation procedures) and outputs a QKD protocol optimized for the specific given channel. We show how this method can be used to find QKD protocols for channels where standard protocols would fail.

The process of digitalisation has an advanced impact on social lives, state affairs, and the industrial automation domain. Ubiquitous networks and the increased requirements in terms of Quality of Service (QoS) create the demand for future-proof network management. Therefore, new technological approaches, such as Software-Defined Networks (SDN) or the 5G Network Slicing concept, are considered. However, the important topic of cyber security has mainly been ignored in the past. Recently, this topic has gained a lot of attention due to frequently reported security related incidents, such as industrial espionage, or production system manipulations. Hence, this work proposes a concept for adding cyber security requirements to future network management paradigms. For this purpose, various security related standards and guidelines are available. However, these approaches are mainly static, require a high amount of manual efforts by experts, and need to be performed in a steady manner. Therefore, the proposed solution contains a dynamic, machine-readable, automatic, continuous, and future-proof approach to model and describe cyber security QoS requirements for the next generation network management.

The process of digitalisation has an advanced impact on social lives, state affairs, and the industrial automation domain. Ubiquitous networks and the increased requirements in terms of Quality of Service (QoS) create the demand for future-proof network management. Therefore, new technological approaches, such as Software-Defined Networks (SDN) or the 5G Network Slicing concept, are considered. However, the important topic of cyber security has mainly been ignored in the past. Recently, this topic has gained a lot of attention due to frequently reported security related incidents, such as industrial espionage, or production system manipulations. Hence, this work proposes a concept for adding cyber security requirements to future network management paradigms. For this purpose, various security related standards and guidelines are available. However, these approaches are mainly static, require a high amount of manual efforts by experts, and need to be performed in a steady manner. Therefore, the proposed solution contains a dynamic, machine-readable, automatic, continuous, and future-proof approach to model and describe cyber security QoS requirements for the next generation network management.

Neural networks (NNs) are currently a very popular topic in machine learning for both research and practice. GPUs are the dominant computing platform for research efforts and are also gaining popularity as a deployment platform for applications such as autonomous vehicles. As a result, GPU vendors such as NVIDIA have spent enormous effort to write special-purpose NN libraries. On other hardware targets, especially mobile GPUs, such vendor libraries are not generally available. Thus, the development of portable, open, high-performance, energy-efficient GPU code for NN operations would enable broader deployment of NN-based algorithms. A root problem is that high efficiency GPU programming suffers from high complexity, low productivity, and low portability. To address this, this work presents a framework to enable productive, high-efficiency GPU programming for NN computations across hardware platforms and programming models. In particular, the framework provides specific support for metaprogramming and autotuning of operations over ND-Arrays. To show the correctness and value of our framework and approach, we implement a selection of NN operations, covering the core operations needed for deploying three common image-processing neural networks. We target three different hardware platforms: NVIDIA, AMD, and Qualcomm GPUs. On NVIDIA GPUs, we show both portability between OpenCL and CUDA as well competitive performance compared to the vendor library. On Qualcomm GPUs, we show that our framework enables productive development of target-specific optimizations, and achieves reasonable absolute performance. Finally, On AMD GPUs, we show initial results that indicate our framework can yield reasonable performance on a new platform with minimal effort.

Just-in-time return-oriented programming (JIT-ROP) is a powerful memory corruption attack that bypasses various forms of code randomization. Execute-only memory (XOM) can potentially prevent these attacks, but requires source code. In contrast, destructive code reads (DCR) provide a trade-off between security and legacy compatibility. The common belief is that DCR provides strong protection if combined with a high-entropy code randomization. The contribution of this paper is twofold: first, we demonstrate that DCR can be bypassed regardless of the underlying code randomization scheme. To this end, we show novel, generic attacks that infer the code layout for highly randomized program code. Second, we present the design and implementation of BGDX (Byte-Granular DCR and XOM), a novel mitigation technique that protects legacy binaries against code inference attacks. BGDX enforces memory permissions on a byte-granular level allowing us to combine DCR and XOM for legacy, off-the-shelf binaries. Our evaluation shows that BGDX is not only effective, but highly efficient, imposing only a geometric mean performance overhead of 3.95 % on SPEC.