Helmholtz Center for Information Security
CISPA – Helmholtz-Zentrum für InformationssicherheitNot a member yet
3406 research outputs found
Sort by
On a dual/hybrid approach to small secret LWE
Get PDFIn this paper, we investigate the security of the Learning With Error (LWE) problem with small secrets by refining and improv- ing the so-called dual lattice attack. More precisely, we use the dual attack on a projected sublattice, which allows generating instances of the LWE problem with a slightly bigger noise that correspond to a frac- tion of the secret key. Then, we search for the fraction of the secret key by computing the corresponding noise for each candidate using the newly constructed LWE samples. As secrets are small, we can perform the search step very efficiently by exploiting the recursive structure of the search space. This approach offers a trade-off between the cost of lattice reduction and the complexity of the search part which allows to speed up the attack. Besides, we aim at providing a sound and non-asymptotic analysis of the techniques to enable its use for practical selection of se- curity parameters. As an application, we revisit the security estimates of some fully homomorphic encryption schemes, including the Fast Fully Homomorphic Encryption scheme over the Torus (TFHE) which is one of the fastest homomorphic encryption schemes based on the (Ring-)LWE problem. We provide an estimate of the complexity of our method for various parameters under three different cost models for lattice reduc- tion and show that the security level of the TFHE scheme should be re-evaluated according to the proposed improvement (for at least 7 bits for the most recent update of the parameters that are used in the imple- mentation)
Securing Asynchronous Exceptions
Get PDFLanguage-based information-flow control (IFC) techniques often rely on special purpose, ad-hoc primitives to address different covert channels that originate in the runtime system, beyond the scope of language constructs. Since these piecemeal solutions may not compose securely, there is a need for a unified mechanism to control covert channels. As a first step towards this goal, we argue for the design of a general interface that allows programs to safely interact with the runtime system and the available computing resources. To coordinate the communication between programs and the runtime system, we propose the use of asynchronous exceptions (interrupts), which, to the best of our knowledge, have not been considered before in the context of IFC languages. Since asynchronous exceptions can be raised at any point during execution—often due to the occurrence of an external event—threads must temporarily mask them out when manipulating locks and shared data structures to avoid deadlocks and, therefore, breaking program invariants. Crucially, the naive combination of asynchronous exceptions with existing features of IFC languages (e.g., concurrency and synchronization variables) may open up new possibilities of information leakage. In this paper, we present MACasync, a concurrent, statically enforced IFC language that, as a novelty, features asynchronous exceptions. We show how asynchronous exceptions easily enable (out of the box) useful programming patterns like speculative ex- ecution and some degree of resource management. We prove that programs in MACasync satisfy progress-sensitive non-interference and mechanize our formal claims in the Agda proof assistant
Lossiness and Entropic Hardness for Ring-LWE
Get PDFThe hardness of the Ring Learning with Errors problem (RLWE) is a central building block for efficiency-oriented lattice-based cryptography. Many applications use an “entropic” variant of the problem where the so-called “secret” is not distributed uniformly as prescribed but instead comes from some distribution with sufficient min-entropy. However, the hardness of the entropic variant has not been substantiated thus far.
For standard LWE (not over rings) entropic results are known, using a “lossiness approach” but it was not known how to adapt this approach to the ring setting. In this work we present the first such results, where entropic security is established either under RLWE or under the Decisional Small Polynomial Ratio (DSPR) assumption which is a mild variant of the NTRU assumption.
In the context of general entropic distributions, our results in the ring setting essentially match the known lower bounds (Bolboceanu et al., Asiacrypt 2019; Brakerski and Döttling, Eurocrypt 2020)
On the Origin of Scanning: The Impact of Location on Internet-Wide Scans
Get PDFFast IPv4 scanning has enabled researchers to answer a wealth of security and networking questions. Yet, despite widespread use, there has been little validation of the methodology’s accuracy, including whether a single scan provides sufficient coverage. In this paper, we analyze how scan origin affects the results of Internet-wide scans by completing three HTTP, HTTPS, and SSH scans from seven geographically and topologically diverse networks. We find that individual origins miss an average 1.6–8.4% of HTTP, 1.5–4.6% of HTTPS, and 8.3–18.2% of SSH hosts. We analyze why origins see different hosts, and show how permanent and temporary blocking, packet loss, geographic biases, and transient outages affect scan results. We discuss the implications for scanning and provide recommendations for future studies
Long-Tailed Recognition Using Class-Balanced Experts
Get PDFDeep learning enables impressive performance in image recognition using large-scale artificially-balanced datasets. However, real-world datasets exhibit highly class-imbalanced distributions, yielding two main challenges: relative imbalance amongst the classes and data scarcity for mediumshot or fewshot classes. In this work, we address the problem of long-tailed recognition wherein the training set is highly imbalanced and the test set is kept balanced. Differently from existing paradigms relying on data-resampling, cost-sensitive learning, online hard example mining, loss objective reshaping, and/or memory-based modeling, we propose an ensemble of class-balanced experts that combines the strength of diverse classifiers. Our ensemble of class-balanced experts reaches results close to state-of-the-art and an extended ensemble establishes a new state-of-the-art on two benchmarks for long-tailed recognition. We conduct extensive experiments to analyse the performance of the ensembles, and discover that in modern large-scale datasets, relative imbalance is a harder problem than data scarcity. The training and evaluation code is available at this https URL
Explainable Reactive Synthesis
Get PDFReactive synthesis transforms a specification of a reactive system, given in a temporal logic, into an implementation. The main advantage of synthesis is that it is automatic. The main disadvantage is that the implementation is usually very difficult to understand. In this paper, we present a new synthesis process that explains the synthesized implementation to the user. The process starts with a simple version of the specification and a corresponding simple implementation. Then, desired properties are added one by one, and the corresponding transformations, repairing the implementation, are explained in terms of counterexample traces. We present SAT-based algorithms for the synthesis of repairs and explanations. The algorithms are evaluated on a range of examples including benchmarks taken from the SYNTCOMP competition
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
No full textHigh-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applications e.g., cloud prediction APIs. Recent advances in model functionality stealing attacks via black-box access (i.e., inputs in, predictions out) threaten the business model of such applications, which require a lot of time, money, and effort to develop. Existing defenses take a passive role against stealing attacks, such as by truncating predicted information. We find such passive defenses ineffective against DNN stealing attacks. In this paper, we propose the first defense which actively perturbs predictions targeted at poisoning the training objective of the attacker. We find our defense effective across a wide range of challenging datasets and DNN model stealing attacks, and additionally outperforms existing defenses. Our defense is the first that can withstand highly accurate model stealing attacks for tens of thousands of queries, amplifying the attacker's error rate up to a factor of 85× with minimal impact on the utility for benign users
A Statistical Analysis Framework for ICS Process Datasets
Get PDFIn recent years, several schemes have been proposed to detect anomalies and attacks on Cyber-Physical Systems (CPSs) such as Industrial Control Systems (ICSs). Based on the analysis of sensor data, unexpected or malicious behavior is detected. Those schemes often rely on (implicit) assumptions on temporally stable sensor data distributions and invariants between process values. Unfortunately, the proposed schemes often do not perform optimally, with Recall scores lower than 70% (e.g., missing 3 alarms every 10 anomalies) for some ICS datasets, with unclear root issues.
In this work, we propose a general framework to analyze whether a given ICS dataset has specific properties (stable sensor distributions in normal operations, potentially state-dependent), which then allows to determine whether certain Anomaly Detection approaches can be expected to perform well. We apply our framework to three datasets showing that the behavior of actuators and sensors are very different between Training set and Test set. In addition, we present high-level guides to consider when designing an Anomaly Detection System