Biblio
In this paper, we investigate detectability and identifiability of attacks on linear dynamical systems that are subjected to external disturbances. We generalize a concept for a security index, which was previously introduced for static systems. The index exactly quantifies the resources necessary for targeted attacks to be undetectable and unidentifiable in the presence of disturbances. This information is useful for both risk assessment and for the design of anomaly detectors. Finally, we show how techniques from the fault detection literature can be used to decouple disturbances and to identify attacks, under certain sparsity constraints.
We will focused the concept of serializability in order to ensure the correct processing of transactions. However, both serializability and relevant properties within transaction-based applications might be affected. Ensure transaction serialization in corrupt systems is one of the demands that can handle properly interrelated transactions, which prevents blocking situations that involve the inability to commit either transaction or related sub-transactions. In addition some transactions has been marked as malicious and they compromise the serialization of running system. In such context, this paper proposes an approach for the processing of transactions in a cloud of databases environment able to secure serializability in running transactions whether the system is compromised or not. We propose also an intrusion tolerant scheme to ensure the continuity of the running transactions. A case study and a simulation result are shown to illustrate the capabilities of the suggested system.
Sophisticated cyber attacks by state-sponsored and criminal actors continue to plague government and industrial infrastructure. Intuitively, partitioning cyber systems into survivable, intrusion tolerant compartments is a good idea. This prevents witting and unwitting insiders from moving laterally and reaching back to their command and control (C2) servers. However, there is a lack of artifacts that can predict the effectiveness of this approach in a realistic setting. We extend earlier work by relaxing simplifying assumptions and providing a new attacker-facing metric. In this article, we propose new closed-form mathematical models and a discrete time simulation to predict three critical statistics: probability of compromise, probability of external host compromise and probability of reachback. The results of our new artifacts agree with one another and with previous work, which suggests they are internally valid and a viable method to evaluate the effectiveness of cyber zone defense.
Advances in virtual reality have generated substantial interest in accurately reproducing and storing spatial audio in the higher order ambisonics (HOA) representation, given its rendering flexibility. Recent standardization for HOA compression adopted a framework wherein HOA data are decomposed into principal components that are then encoded by standard audio coding, i.e., frequency domain quantization and entropy coding to exploit psychoacoustic redundancy. A noted shortcoming of this approach is the occasional mismatch in principal components across blocks, and the resulting suboptimal transitions in the data fed to the audio coder. Instead, we propose a framework where singular value decomposition (SVD) is performed after transformation to the frequency domain via the modified discrete cosine transform (MDCT). This framework not only ensures smooth transition across blocks, but also enables frequency dependent SVD for better energy compaction. Moreover, we introduce a novel noise substitution technique to compensate for suppressed ambient energy in discarded higher order ambisonics channels, which significantly enhances the perceptual quality of the reconstructed HOA signal. Objective and subjective evaluation results provide evidence for the effectiveness of the proposed framework in terms of both higher compression gains and better perceptual quality, compared to existing methods.
To enhance the encryption and anti-translation capability of the information, we constructed a five-dimensional chaotic system. Combined with the Lü system, a time-switched system with multiple chaotic attractors is realized in the form of a digital circuit. Some characteristics of the five-dimensional system are analyzed, such as Poincare mapping, the Lyapunov exponent spectrum, and bifurcation diagram. The analysis shows that the system exhibits chaotic characteristics for a wide range of parameter values. We constructed a time-switched expression between multiple chaotic attractors using the communication between a microcontroller unit (MCU) and field programmable gate array (FPGA). The system can quickly switch between different chaotic attractors within the chaotic system and between chaotic systems at any time, leading to signal sources with more variability, diversity, and complexity for chaotic encryption.
The study of spin waves (SW) excitation in magnetic devices is one of the most important topics in modern magnetism due to the applications of the information carrier and the signal processing. We experimentally realize a spin-wave generator, capable of frequency modulation, in a magnonic waveguide. The emission of spin waves was produced by the reversal or oscillation of nanoscale magnetic vortex cores in a NiFe disk array. The vortex cores in the disk array were excited by an out of plane radio frequency (rf) magnetic field. The dynamic behaviors of the magnetization of NiFe were studied using a micro-focused Brillouin light scattering spectroscopy (BLS) setup.
Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (\textbackslashtextless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- \textbackslashtextbar\textbackslashtextbar
We set up a framework for the formal proofs of RFID protocols in the computational model. We rely on the so-called computationally complete symbolic attacker model. Our contributions are: 1) to design (and prove sound) axioms reflecting the properties of hash functions (Collision-Resistance, PRF). 2) to formalize computational unlinkability in the model. 3) to illustrate the method, providing the first formal proofs of unlinkability of RFID protocols, in the omputational model.
A cyber-attack detection system issues alerts when an attacker attempts to coerce a trusted software application to perform unsafe actions on the attacker's behalf. One way of issuing such alerts is to create an application-agnostic cyber- attack detection system that responds to prevalent software vulnerabilities. The creation of such an autonomic alert system, however, is impeded by the disparity between implementation language, function, quality-of-service (QoS) requirements, and architectural patterns present in applications, all of which contribute to the rapidly changing threat landscape presented by modern heterogeneous software systems. This paper evaluates the feasibility of creating an autonomic cyber-attack detection system and applying it to several exemplar web-based applications using program transformation and machine learning techniques. Specifically, we examine whether it is possible to detect cyber-attacks (1) online, i.e., as they occur using lightweight structures derived from a call graph and (2) offline, i.e., using machine learning techniques trained with features extracted from a trace of application execution. In both cases, we first characterize normal application behavior using supervised training with the test suites created for an application as part of the software development process. We then intentionally perturb our test applications so they are vulnerable to common attack vectors and then evaluate the effectiveness of various feature extraction and learning strategies on the perturbed applications. Our results show that both lightweight on-line models based on control flow of execution path and application specific off-line models can successfully and efficiently detect in-process cyber-attacks against web applications.
An approximate sparse recovery system in ℓ1 norm consists of parameters k, ε, N; an m-by-N measurement Φ; and a recovery algorithm R. Given a vector, x, the system approximates x by &xwidehat; = R(Φ x), which must satisfy ‖ &xwidehat;-x‖1 ≤ (1+ε)‖ x - xk‖1. We consider the “for all” model, in which a single matrix Φ, possibly “constructed” non-explicitly using the probabilistic method, is used for all signals x. The best existing sublinear algorithm by Porat and Strauss [2012] uses O(ε−3klog (N/k)) measurements and runs in time O(k1 − αNα) for any constant α textgreater 0. In this article, we improve the number of measurements to O(ε − 2klog (N/k)), matching the best existing upper bound (attained by super-linear algorithms), and the runtime to O(k1+βpoly(log N,1/ε)), with a modest restriction that k ⩽ N1 − α and ε ⩽ (log k/log N)γ for any constants α, β, γ textgreater 0. When k ⩽ log cN for some c textgreater 0, the runtime is reduced to O(kpoly(N,1/ε)). With no restrictions on ε, we have an approximation recovery system with m = O(k/εlog (N/k)((log N/log k)γ + 1/ε)) measurements. The overall architecture of this algorithm is similar to that of Porat and Strauss [2012] in that we repeatedly use a weak recovery system (with varying parameters) to obtain a top-level recovery algorithm. The weak recovery system consists of a two-layer hashing procedure (or with two unbalanced expanders for a deterministic algorithm). The algorithmic innovation is a novel encoding procedure that is reminiscent of network coding and that reflects the structure of the hashing stages. The idea is to encode the signal position index i by associating it with a unique message mi, which will be encoded to a longer message m′i (in contrast to Porat and Strauss [2012] in which the encoding is simply the identity). Portions of the message m′i correspond to repetitions of the hashing, and we use a regular expander graph to encode the linkages among these portions. The decoding or recovery algorithm consists of recovering the portions of the longer messages m′i and then decoding to the original messages mi, all the while ensuring that corruptions can be detected and/or corrected. The recovery algorithm is similar to list recovery introduced in Indyk et al. [2010] and used in Gilbert et al. [2013]. In our algorithm, the messages \mi\ are independent of the hashing, which enables us to obtain a better result.
Main-memory database management systems (DBMS) can achieve excellent performance when processing massive volume of on-line transactions on modern multi-core machines. But existing durability schemes, namely, tuple-level and transaction-level logging-and-recovery mechanisms, either degrade the performance of transaction processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it is possible to achieve speedy failure recovery without introducing any costly logging overhead to the execution of concurrent transactions. We propose PACMAN, a parallel database recovery mechanism that is specifically designed for lightweight, coarse-grained transaction-level logging. PACMAN leverages a combination of static and dynamic analyses to parallelize the log recovery: at compile time, PACMAN decomposes stored procedures by carefully analyzing dependencies within and across programs; at recovery time, PACMAN exploits the availability of the runtime parameter values to attain an execution schedule with a high degree of parallelism. As such, recovery performance is remarkably increased. We evaluated PACMAN in a fully-fledged main-memory DBMS running on a 40-core machine. Compared to several state-of-the-art database recovery mechanisms, can significantly reduce recovery time without compromising the efficiency of transaction processing.
Encryption ransomware is a malicious software that stealthily encrypts user files and demands a ransom to provide access to these files. Several prior studies have developed systems to detect ransomware by monitoring the activities that typically occur during a ransomware attack. Unfortunately, by the time the ransomware is detected, some files already undergo encryption and the user is still required to pay a ransom to access those files. Furthermore, ransomware variants can obtain kernel privilege, which allows them to terminate software-based defense systems, such as anti-virus. While periodic backups have been explored as a means to mitigate ransomware, such backups incur storage overheads and are still vulnerable as ransomware can obtain kernel privilege to stop or destroy backups. Ideally, we would like to defend against ransomware without relying on software-based solutions and without incurring the storage overheads of backups. To that end, this paper proposes FlashGuard, a ransomware tolerant Solid State Drive (SSD) which has a firmware-level recovery system that allows quick and effective recovery from encryption ransomware without relying on explicit backups. FlashGuard leverages the observation that the existing SSD already performs out-of-place writes in order to mitigate the long erase latency of flash memories. Therefore, when a page is updated or deleted, the older copy of that page is anyway present in the SSD. FlashGuard slightly modifies the garbage collection mechanism of the SSD to retain the copies of the data encrypted by ransomware and ensure effective data recovery. Our experiments with 1,447 manually labeled ransomware samples show that FlashGuard can efficiently restore files encrypted by ransomware. In addition, we demonstrate that FlashGuard has a negligible impact on the performance and lifetime of the SSD.
In this paper, a novel method to do feature selection to detect botnets at their phase of Command and Control (C&C) is presented. A major problem is that researchers have proposed features based on their expertise, but there is no a method to evaluate these features since some of these features could get a lower detection rate than other. To this aim, we find the feature set based on connections of botnets at their phase of C&C, that maximizes the detection rate of these botnets. A Genetic Algorithm (GA) was used to select the set of features that gives the highest detection rate. We used the machine learning algorithm C4.5, this algorithm did the classification between connections belonging or not to a botnet. The datasets used in this paper were extracted from the repositories ISOT and ISCX. Some tests were done to get the best parameters in a GA and the algorithm C4.5. We also performed experiments in order to obtain the best set of features for each botnet analyzed (specific), and for each type of botnet (general) too. The results are shown at the end of the paper, in which a considerable reduction of features and a higher detection rate than the related work presented were obtained.
Elliptic Curve Cryptosystems are very much delicate to attacks or physical attacks. This paper aims to correctly implementing the fault injection attack against Elliptic Curve Digital Signature Algorithm. More specifically, the proposed algorithm concerns to fault attack which is implemented to sufficiently alter signature against vigilant periodic sequence algorithm that supports the efficient speed up and security perspectives with most prominent and well known scalar multiplication algorithm for ECDSA. The purpose is to properly injecting attack whether any probable countermeasure threatening the pseudo code is determined by the attack model according to the predefined methodologies. We show the results of our experiment with bits acquire from the targeted implementation to determine the reliability of our attack.
Public-key cryptography schemes are widely used due to their high level of security. As a very efficient one among public-key cryptosystems, elliptic curve cryptography (ECC) has been studied for years. Researchers used to improve the efficiency of ECC through point multiplication, which is the most important and complex operation of ECC. In our research, we use special families of curves and prime fields which have special properties. After that, we introduce the instruction set architecture (ISA) extension method to accelerate this algorithm (192-bit private key) and build an ECC\_ASIP model with six new ECC custom instructions. Finally, the ECC\_ASIP model is implemented in a field-programmable gate array (FPGA) platform. The persuasive experiments have been conducted to evaluate the performance of our new model in the aspects of the performance, the code storage space and hardware resources. Experimental results show that our processor improves 69.6% in the execution efficiency and requires only 6.2% more hardware resources.
In modern enterprises, incorrect or inconsistent security policies can lead to massive damage, e.g., through unintended data leakage. As policy authors have different skills and background knowledge, usable policy editors have to be tailored to the author's individual needs and to the corresponding application domain. However, the development of individual policy editors and the customization of existing ones is an effort consuming task. In this paper, we present a framework for generating tailored policy editors. In order to empower user-friendly and less error-prone specification of security policies, the framework supports multiple platforms, policy languages, and specification paradigms.
At the core of its nature, security is a highly contextual and dynamic challenge. However, current security policy approaches are usually static, and slow to adapt to ever-changing requirements, let alone catching up with reality. In a 2012 Sophos survey, it was stated that a unique malware is created every half a second. This gives a glimpse of the unsustainable nature of a global problem, any improvement in terms of closing the "time window to adapt" would be a significant step forward. To exacerbate the situation, a simple change in threat and attack vector or even an implementation of the so-called "bring-your-own-device" paradigm will greatly change the frequency of changed security requirements and necessary solutions required for each new context. Current security policies also typically overlook the direct and indirect costs of implementation of policies. As a result, technical teams often fail to have the ability to justify the budget to the management, from a business risk viewpoint. This paper considers both the adaptive and cost-benefit aspects of security, and introduces a novel context-aware technique for designing and implementing adaptive, optimized security policies. Our approach leverages the capabilities of stochastic programming models to optimize security policy planning, and our preliminary results demonstrate a promising step towards proactive, context-aware security policies.
SDSM is a novel approach for providing secure directory-based distributed shared memory to CPUs that are connected via an untrusted medium. SDSM scales efficiently to thousands of CPUs with less than 0.8% performance reduction, compared to a system with no security.