Biblio

We propose a new Bloom filter structure for searchable encryption schemes in which a large Bloom filter is treated as (replaced with) two smaller ones for the search index. False positive is one inherent drawback of Bloom filter. We formulate the false positive rates for one regular large Bloom filter, and then derive the false positive rate for the two smaller ones. With examples, we show how the new scheme cuts down the false positive rate and the size of Bloom filter to a balanced point that fulfills the user requirements and increases the efficiency of the structure.

The increase of sensitive data in the current Internet of Things (IoT) raises demands of computation, communication and storage capabilities. Indeed, thanks to RFID tags and wireless sensor networks, anything can be part of IoT. As a result, a large amount of data is generated, which is hard for many IoT devices to handle, as many IoT devices are resource-constrained and cannot use the existing standard security protocols. Cloud computing might seem like a convenient solution, since it offers on-demand access to a shared pool of resources such as processors, storage, applications and services. However this comes as a cost, as unnecessary communications not only burden the core network, but also the data center in the cloud. Therefore, considering suitable approaches such as fog computing and security middleware solutions is crucial. In this paper, we propose a novel middleware architecture to solve the above issues, and discuss the generic concept of using fog computing along with cloud in order to achieve a higher security level. Our security middleware acts as a smart gateway as it is meant to pre-process data at the edge of the network. Depending on the received information, data might either be processed and stored locally on fog or sent to the cloud for further processing. Moreover, in our scheme, IoT constrained devices communicate through the proposed middleware, which provide access to more computing power and enhanced capability to perform secure communications. We discuss these concepts in detail, and explain how our proposal is effective to cope with some of the most relevant IoT security challenges.

Vulnerabilities in hypervisors are crucial in multi-tenant clouds since they can undermine the security of all virtual machines (VMs) consolidated on a vulnerable hypervisor. Unfortunately, 107 vulnerabilitiesin KVM+QEMU and 38 vulnerabilities in Xen have been reported in 2016. The device-emulation layer in hypervisors is a hotbed of vulnerabilities because the code for virtualizing devices is complicated and requires knowledge on the device internals. We propose a "device request filter", called Nioh, that raises the bar for attackers to exploit the vulnerabilities in hypervisors. The key insight behind Nioh is that malicious I/O requests attempt to exploit vulnerabilities and violate device specifications in many cases. Nioh inspects I/O requests from VMs and rejects those that do not conform to a device specification. A device specification is modeled as a device automaton in Nioh, an extended automaton to facilitate the description of device specifications. The software framework is also provided to encapsulate the interactions between the device request filter and the underlying hypervisors. The results of our attack evaluation suggests that Nioh can defend against attacks that exploit vulnerabilities in device emulation, i.e., CVE-2015-5158, CVE-2016-1568, CVE-2016-4439, and CVE-2016-7909. This paper shows that the notorious VENOM attack can be detected and rejected by using Nioh.

It is currently impossible for an application to verify that the data it passes to the kernel for storage is actually submitted to an underlying device or that the data returned to an application by the kernel has actually originated from an underlying device. A compromised or malicious OS can silently discard data written by the application or return fabricated data during a read operation. This is a serious data integrity issue for use-cases where verifiable storage and retrieval of data is a necessary precondition for ensuring correct operation, for example with secure logging, APT monitoring and compliance. We outline a solution for verifiable data storage and retrieval by providing a trustworthy mechanism, based on Intel SGX, to authenticate and verify request data at both the application and storage device endpoints. Even in the presence of a malicious OS our design ensures the authenticity and integrity of data while performing disk I/O and detects any data loss attributable to the untrusted OS fabricating or discarding read and write requests respectively. We provide a nascent prototype implementation for the core system together with an evaluation highlighting the temporal overheads imposed by this mechanism.

Object flow integrity (OFI) augments control-flow integrity (CFI) and software fault isolation (SFI) protections with secure, first-class support for binary object exchange across inter-module trust boundaries. This extends both source-aware and source-free CFI and SFI technologies to a large class of previously unsupported software: those containing immutable system modules with large, object-oriented APIs—which are particularly common in component-based, event-driven consumer software. It also helps to protect these inter-module object exchanges against confused deputy-assisted vtable corruption and counterfeit object-oriented programming attacks. A prototype implementation for Microsoft Component Object Model demonstrates that OFI is scalable to large interfaces on the order of tens of thousands of methods, and exhibits low overheads of under 1% for some common-case applications. Significant elements of the implementation are synthesized automatically through a principled design inspired by type-based contracts.

Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms are being used in diverse domains where security is a concern, such as, automotive systems, finance, health-care, computer vision, speech recognition, natural-language processing, and malware detection. Of particular concern is use of ML in cyberphysical systems, such as driver-less cars and aviation, where the presence of an adversary can cause serious consequences. In this paper we focus on attacks caused by adversarial samples, which are inputs crafted by adding small, often imperceptible, perturbations to force a ML model to misclassify. We present a simple gradient-descent based algorithm for finding adversarial samples, which performs well in comparison to existing algorithms. The second issue that this paper tackles is that of metrics. We present a novel metric based on few computer-vision algorithms for measuring the quality of adversarial samples.

A one-message unilateral entity authentication scheme allows one party, called the prover, to authenticate himself, i.e., to prove his identity, to another party, called the verifier, by sending a single authentication message. In this paper we consider schemes where the prover and the verifier do not share any secret information, such as a password, in advance. We propose the first theoretical characterization for one-message unilateral entity authentication schemes, by formalizing the security requirements for such schemes with respect to different kinds of adversaries. Afterwards, we propose three provably-secure constructions for one-message unilateral entity authentication schemes.

Context-sensitive graph grammars have been rigorous formalisms for specifying visual programming languages, as they possess sufficient expressive powers and intuitive forms. Efficient parsing mechanisms are essential to these formalisms. However, the existent parsing algorithms are either inefficient or confined to a minority of graph grammars. This paper introduces the notion of partial precedence, defines the partial precedence graph of a graph grammar and theoretically unveils the existence of a valid parsing path conforming to the topological orderings of the partial precedence graph. Then, it provides algorithms for computing the partial precedence graph and presents an approach to improving general parsing algorithms with the graph based on the drawn conclusion. It is shown that the approach can considerably improve the efficiency of general parsing algorithms.

User's personal portable devices such as smartphone, tablet and laptop require continuous authentication of the user to prevent against illegitimate access to the device and personal data. Current authentication techniques require users to enter password or scan fingerprint, making frequent access to the devices inconvenient. In this work, we propose to exploit user's on-body wearable devices to detect their proximity from her portable devices, and use the proximity for continuous authentication of the portable devices. We present PCASA which utilizes acoustic communication for secure proximity estimation with sub-meter level accuracy. PCASA uses Differential Pulse Position Modulation scheme that modulates data through varying the silence period between acoustic pulses to ensure energy efficiency even when authentication operation is being performed once every second. It yields an secure and accurate distance estimation even when user is mobile by utilizing Doppler effect for mobility speed estimation. We evaluate PCASA using smartphone and smartwatches, and show that it supports up to 34 hours of continuous authentication with a fully charged battery.

There are many risks in moving data into public storage environments, along with an increasing threat around large-scale data leakage. Secret sharing scheme has been proposed as a keyless and resilient mechanism to mitigate this, but scaling through large scale data infrastructure has remained the bane of using secret sharing scheme in big data storage and retrievals. This work applies secret sharing methods as used in cryptography to create robust and secure data storage and retrievals in conjunction with data fragmentation. It outlines two different methods of distributing data equally to storage locations as well as recovering them in such a manner that ensures consistent data availability irrespective of file size and type. Our experiments consist of two different methods - data and key shares. Using our experimental results, we were able to validate previous works on the effects of threshold on file recovery. Results obtained also revealed the varying effects of share writing to and retrieval from storage locations other than computer memory. The implication is that increase in fragment size at varying file and threshold sizes rather than add overheads to file recovery, do so on creation instead, underscoring the importance of choosing a varying fragment size as file size increases.

Mobile cloud computing paradigm enables cloud servers to extend the limited hardware resources of mobile devices improving availability and reliability of the services provided. Consequently, private, financial, business and critical data pass through wireless access media exposed to malicious attacks. Mobile cloud infrastructure requires new security mechanisms, at the same time as offloading operations need to maintain the advantages of saving processing and energy of the device. Thus, this paper implements a middleware-based computational offloading with cryptographic algorithms and evaluates two mechanisms (symmetric and asymmetric), to provide the integrity and authenticity of data that a smartphone offloads to mobile cloud servers. Also, the paper discusses the factors that impact on power consumption and performance on smartphones that's run resource-intensive applications.

Algorithm parallelization diversifies a complicated computing task into small parts, and thus it receives wide attention when it is implemented to evolutionary algorithms (EA). This works considers a recently developed EA called the Swarm Intelligence Based (SIB) method as a benchmark to compare the performance of two types of parallel computing approaches: a CPU-based approach via OpenMP and a GPU-based approach via CUDA. The experiments are conducted to solve an optimization problem in the search of supersaturated designs via the SIB method. Unlike conventional suggestions, we show that the CPU-based OpenMP outperforms CUDA at the execution time. At the end of this paper, we provide several potential problems in GPU parallel computing towards EA and suggest to use CPU-based OpenMP for parallel computing of EA.

Memory-based vulnerabilities are a major source of attack vectors. They allow attackers to gain unauthorized access to computers and their data. Previous research has made significant progress in detecting attacks. However, developers still need to locate and fix these vulnerabilities, a mostly manual and time-consuming process. They face a number of challenges. Particularly, the manifestation of an attack does not always coincide with the exploited vulnerabilities, and many attacks are hard to reproduce in the lab environment, leaving developers with limited information to locate them. In this paper, we propose Ravel, an architectural approach to pinpoint vulnerabilities from attacks. Ravel consists of an online attack detector and an offline vulnerability locator linked by a record & replay mechanism. Specifically, Ravel records the execution of a production system and simultaneously monitors it for attacks. If an attack is detected, the execution is replayed to reveal the targeted vulnerabilities by analyzing the program's memory access patterns under attack. We have built a prototype of Ravel based on the open-source FreeBSD operating system. The evaluation results in security and performance demonstrate that Ravel can effectively pinpoint various types of memory vulnerabilities and has low performance overhead.

The Polytope Escape Problem for continuous linear dynamical systems consists of deciding, given an affine function f:Rd -\textbackslashtextgreater Rd and a convex polytope P⊆ Rd, both with rational descriptions, whether there exists an initial point x0 in P such that the trajectory of the unique solution to the differential equation: ·x(t)=f(x(t)) x 0= x0 is entirely contained in P. We show that this problem is reducible in polynomial time to the decision version of linear programming with real algebraic coefficients. The latter is a special case of the decision problem for the existential theory of real closed fields, which is known to lie between NP and PSPACE. Our algorithm makes use of spectral techniques and relies, among others, on tools from Diophantine approximation.

Certification of keys and attributes is in practice typically realized by a hierarchy of issuers. Revealing the full chain of issuers for certificate verification, however, can be a privacy issue since it can leak sensitive information about the issuer's organizational structure or about the certificate owner. Delegatable anonymous credentials solve this problem and allow one to hide the full delegation (issuance) chain, providing privacy during both delegation and presentation of certificates. However, the existing delegatable credentials schemes are not efficient enough for practical use. In this paper, we present the first hierarchical (or delegatable) anonymous credential system that is practical. To this end, we provide a surprisingly simple ideal functionality for delegatable credentials and present a generic construction that we prove secure in the UC model. We then give a concrete instantiation using a recent pairing-based signature scheme by Groth and describe a number of optimizations and efficiency improvements that can be made when implementing our concrete scheme. The latter might be of independent interest for other pairing-based schemes as well. Finally, we report on an implementation of our scheme in the context of transaction authentication for blockchain, and provide concrete performance figures.

This paper presents Themis, an end-to-end static analysis tool for finding resource-usage side-channel vulnerabilities in Java applications. We introduce the notion of epsilon-bounded non-interference, a variant and relaxation of Goguen and Meseguer's well-known non-interference principle. We then present Quantitative Cartesian Hoare Logic (QCHL), a program logic for verifying epsilon-bounded non-interference. Our tool, Themis, combines automated reasoning in CHL with lightweight static taint analysis to improve scalability. We evaluate Themis on well known Java applications and demonstrate that Themis can find unknown side-channel vulnerabilities in widely-used programs. We also show that Themis can verify the absence of vulnerabilities in repaired versions of vulnerable programs and that Themis compares favorably against Blazer, a state-of-the-art static analysis tool for finding timing side channels in Java applications.

For a secure communication over an insecure channel the Diffie-Hellman key exchange protocol (DHKEP) is treated as the de facto standard. However, it suffers form server-side compromisation, identity compromisation, man-in-the-middle, replay attacks, etc. Also, there are single point of vulnerability (SOV), single point of failure (SOF) and user privacy preservation issues. This work proposes an identity-based two-server DHKEP to address the aforesaid issues and alleviating the attacks. To preserve user identity from outside intruders, a k-anonymity based identity hiding principle has been adopted. Further, to ensure efficient utilization of channel bandwidth, the proposed scheme employs elliptic curve cryptography. The security analysis substantiate that our scheme is provably secure and successfully addressed the above-mentioned issues. The performance study contemplates that the overhead of the protocol is reasonable and comparable with other schemes.

Network accountability, forensic analysis, and failure diagnosis are becoming increasingly important for network management and security. Network provenance significantly aids network administrators in these tasks by explaining system behavior and revealing the dependencies between system states. Although resourceful, network provenance can sometimes be too rich, revealing potentially sensitive information that was involved in system execution. In this paper, we propose a cryptographic approach to preserve the confidentiality of provenance (sub)graphs while allowing users to query and access the parts of the graph for which they are authorized. Our proposed solution is a novel application of searchable symmetric encryption (SSE) and more generally structured encryption (SE). Our SE-enabled provenance system allows a node to enforce access control policies over its provenance data even after the data has been shipped to remote nodes (e.g., for optimization purposes). We present a prototype of our design and demonstrate its practicality, scalability, and efficiency for both provenance maintenance and querying.

With technology at our fingertips waiting to be exploited, the past decade saw the revolutionizing Human Computer Interactions. The ease with which a user could interact was the Unique Selling Proposition (USP) of a sales team. Human Computer Interactions have many underlying parameters like Data Visualization and Presentation as some to deal with. With the race, on for better and faster presentations, evolved many frameworks to be widely used by all software developers. As the need grew for user friendly applications, more and more software professionals were lured into the front-end sophistication domain. Application frameworks have evolved to such an extent that with just a few clicks and feeding values as per requirements we are able to produce a commercially usable application in a few minutes. These frameworks generate quantum lines of codes in minutes which leaves a contrail of bugs to be discovered in the future. We have also succumbed to the benchmarking in Software Quality Metrics and have made ourselves comfortable with buggy software's to be rectified in future. The exponential evolution in the cyber domain has also attracted attackers equally. Average human awareness and knowledge has also improved in the cyber domain due to the prolonged exposure to technology for over three decades. As the attack sophistication grows and zero day attacks become more popular than ever, the suffering end users only receive remedial measures in spite of the latest Antivirus, Intrusion Detection and Protection Systems installed. We designed a software to display the complete services and applications running in users Operating System in the easiest perceivable manner aided by Computer Graphics and Data Visualization techniques. We further designed a study by empowering the fence sitter users with tools to actively participate in protecting themselves from threats. The designed threats had impressions from the complete threat canvas in some form or other restricted to systems functioning. Network threats and any sort of packet transfer to and from the system in form of threat was kept out of the scope of this experiment. We discovered that end users had a good idea of their working environment which can be used exponentially enhances machine learning for zero day threats and segment the unmarked the vast threat landscape faster for a more reliable output.

With technology at our fingertips waiting to be exploited, the past decade saw the revolutionizing Human Computer Interactions. The ease with which a user could interact was the Unique Selling Proposition (USP) of a sales team. Human Computer Interactions have many underlying parameters like Data Visualization and Presentation as some to deal with. With the race, on for better and faster presentations, evolved many frameworks to be widely used by all software developers. As the need grew for user friendly applications, more and more software professionals were lured into the front-end sophistication domain. Application frameworks have evolved to such an extent that with just a few clicks and feeding values as per requirements we are able to produce a commercially usable application in a few minutes. These frameworks generate quantum lines of codes in minutes which leaves a contrail of bugs to be discovered in the future. We have also succumbed to the benchmarking in Software Quality Metrics and have made ourselves comfortable with buggy software's to be rectified in future. The exponential evolution in the cyber domain has also attracted attackers equally. Average human awareness and knowledge has also improved in the cyber domain due to the prolonged exposure to technology for over three decades. As the attack sophistication grows and zero day attacks become more popular than ever, the suffering end users only receive remedial measures in spite of the latest Antivirus, Intrusion Detection and Protection Systems installed. We designed a software to display the complete services and applications running in users Operating System in the easiest perceivable manner aided by Computer Graphics and Data Visualization techniques. We further designed a study by empowering the fence sitter users with tools to actively participate in protecting themselves from threats. The designed threats had impressions from the complete threat canvas in some form or other restricted to systems functioning. Network threats and any sort of packet transfer to and from the system in form of threat was kept out of the scope of this experiment. We discovered that end users had a good idea of their working environment which can be used exponentially enhances machine learning for zero day threats and segment the unmarked the vast threat landscape faster for a more reliable output.

The fast and energy-efficient simulation of dynamical systems defined by coupled ordinary/partial differential equations has emerged as an important problem. The accelerated simulation of coupled ODE/PDE is critical for analysis of physical systems as well as computing with dynamical systems. This paper presents a fast and programmable accelerator for simulating dynamical systems. The computing model of the proposed platform is based on multilayer cellular nonlinear network (CeNN) augmented with nonlinear function evaluation engines. The platform can be programmed to accelerate wide classes of ODEs/PDEs by modulating the connectivity within the multilayer CeNN engine. An innovative hardware architecture including data reuse, memory hierarchy, and near-memory processing is designed to accelerate the augmented multilayer CeNN. A dataflow model is presented which is supported by optimized memory hierarchy for efficient function evaluation. The proposed solver is designed and synthesized in 15nm technology for the hardware analysis. The performance is evaluated and compared to GPU nodes when solving wide classes of differential equations and the power consumption is analyzed to show orders of magnitude improvement in energy efficiency.

Apps written in JavaScript are an easy target for reverse engineering attacks, e.g. to steal the intellectual property or to create a clone of an app. Unprotected JavaScript apps even contain high level information such as developer comments, if those were not explicitly stripped. This fact becomes more and more important with the increasing popularity of JavaScript as language of choice for both web development and hybrid mobile apps. In this paper, we present a novel JavaScript obfuscator based on the Google Closure Compiler, which transforms readable JavaScript source code into a representation much harder to analyze for adversaries. We evaluate this obfuscator regarding its performance impact and its semantics-preserving property.

This paper presents PT-CFI, a new backward-edge control flow violation detection system based on a novel use of a recently introduced hardware feature called Intel Processor Trace (PT). Designed primarily for offline software debugging and performance analysis, PT offers the capability of tracing the entire control flow of a running program. In this paper, we explore the practicality of using PT for security applications, and propose to build a new control flow integrity (CFI) model that enforces a backward-edge CFI policy for native COTS binaries based on the traces from Intel PT. By exploring the intrinsic properties of PT with a system call based synchronization primitive and a deep inspection capability, we have addressed a number of technical challenges such as how to make sure the backward edge CFI policy is both sound and complete, how to make PT enforce our CFI policy, and how to balance the performance overhead. We have implemented PT-CFI and evaluated with a number of programs including SPEC2006 and HTTP daemons. Our experimental results show that PT-CFI can enforce a perfect backward-edge CFI with only small overhead for the protected program.

Denial of Service (DoS) attacks are a major threat currently observable in computer networks and especially the Internet. In such an attack a malicious party tries to either break a service, running on a server, or exhaust the capacity or bandwidth of the victim to hinder customers to effectively use the service. Recent reports show that the total number of Distributed Denial of Service (DDoS) attacks is steadily growing with "mega-attacks" peaking at hundreds of gigabit/s (Gbps). In this paper, we will provide a quantification of DDoS attacks in size and duration beyond these outliers reported in the media. We find that these mega attacks do exist, but the bulk of attacks is in practice only a fraction of these frequently reported values. We further show that it is feasible to collect meaningful backscatter traces using surprisingly small telescopes, thereby enabling a broader audience to perform attack intelligence research.

Android smartphones are ubiquitous all over the world, and organizations that turn profits out of data mining user personal information are on the rise. Many users are not aware of the risks of accepting permissions from Android apps, and the continued state of insecurity, manifested in increased level of breaches across all large organizations means that personal information is falling in the hands of malicious actors. This paper aims at shedding the light on privacy leakage in apps that target a specific demography, Arabs. The research takes into consideration apps that cater to specific cultural aspects of this region and identify how they could be abusing the trust given to them by unsuspecting users. Dynamic taint analysis is used in a virtualized environment to analyze top free apps based on popularity in Google Play store. Information presented highlights how different categories of apps leak different categories of private information.