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Light-OCB: Parallel Lightweight Authenticated Cipher with Full Security
Get PDFThis paper proposes a lightweight authenticated encryption (AE) scheme, called Light-OCB, which can be viewed as a lighter variant of the CAESAR winner OCB as well as a faster variant of the high profile NIST LWC competition submission LOCUS-AEAD. Light-OCB is structurally similar to LOCUS-AEAD and uses a nonce-based derived key that provides optimal security, and short-tweak tweakable blockcipher (tBC) for efficient domain separation. Light-OCB improves over LOCUS-AEAD by reducing the number of primitive calls, and thereby significantly optimizing the throughput. To establish our claim, we provide FPGA hardware implementation details and benchmark for Light-OCB against LOCUS-AEAD and several other well-known AEs. The implementation results depict that, when instantiated with the tBC TweGIFT64, Light-OCB achieves an extremely low hardware footprint - consuming only around 1128 LUTs and 307 slices (significantly lower than that for LOCUS-AEAD) while maintaining a throughput of 880 Mbps, which is almost twice that of LOCUS-AEAD. To the best of our knowledge, this figure is significantly better than all the known implementation results of other lightweight ciphers with parallel structures
Relative Flatness and Generalization
Get PDFFlatness of the loss curve is conjectured to be connected to the generalization
ability of machine learning models, in particular neural networks. While it has
been empirically observed that flatness measures consistently correlate strongly
with generalization, it is still an open theoretical problem why and under which
circumstances flatness is connected to generalization, in particular in light of
reparameterizations that change certain flatness measures but leave generalization
unchanged. We investigate the connection between flatness and generalization
by relating it to the interpolation from representative data, deriving notions of
representativeness, and feature robustness. The notions allow us to rigorously
connect flatness and generalization and to identify conditions under which the
connection holds. Moreover, they give rise to a novel, but natural relative flatness
measure that correlates strongly with generalization, simplifies to ridge regression
for ordinary least squares, and solves the reparameterization issue
Assessing the Use of Insecure ICS Protocols via IXP Network Traffic Analysis
Get PDFModern Industrial Control Systems (ICSs) allow
remote communication through the Internet using industrial
protocols that were not designed to work with external networks.
To understand security issues related to this practice, prior work
usually relies on active scans by researchers or services such as
Shodan. While such scans can identify publicly open ports, they
cannot identify legitimate use of insecure industrial traffic. In
particular, source-based filtering in Network Address Translation
or Firewalls prevent detection by active scanning, but do not
ensure that insecure communication is not manipulated in transit.
In this work, we compare Shodan-only analysis with large-
scale traffic analysis at a local Internet Exchange Point (IXP),
based on sFlow sampling. This setup allows us to identify ICS
endpoints actually exchanging industrial traffic over the Internet.
Besides, we are able to detect scanning activities and what other
type of traffic is exchanged by the systems (i.e., IT traffic).
We find that Shodan only listed less than 2% of hosts that
we identified as exchanging industrial traffic, and only 7% of
hosts identified by Shodan actually exchange industrial traffic.
Therefore, Shodan does not allow to understand the actual use
of insecure industrial protocols on the Internet and the current
security practices in ICS communications. We show that 75.6%
of ICS hosts still rely on unencrypted communications without
integrity protection, leaving those critical systems vulnerable to
malicious attacks
Rate-1 Quantum Fully Homomorphic Encryption
No full textSecure function evaluation (SFE) allows Alice to publish an encrypted version of her input m such that Bob (holding a circuit C) can send a single message that reveals C(m) to Alice, and nothing more. Security is required to hold against malicious parties, that may behave arbitrarily. In this work we study the notion of SFE in the quantum setting, where Alice outputs an encrypted quantum state ∣∣????⟩
|
ψ
⟩
and learns ????(∣∣????⟩)
C
(
|
ψ
⟩
)
after receiving Bob’s message.
We show that, assuming the quantum hardness of the learning with errors problem (LWE), there exists an SFE protocol for quantum computation with communication complexity
(|∣∣????⟩|+|????(∣∣????⟩)|)⋅(1+????(1))
which is nearly optimal. This result is obtained by two main technical steps, which might be of independent interest. Specifically, we show (i) a construction of a rate-1 quantum fully-homomorphic encryption and (ii) a generic transformation to achieve malicious circuit privacy in the quantum setting
Universal Proxy Re-Encryption
No full textWe put forward the notion of universal proxy re-encryption (UPRE). A UPRE scheme enables a proxy to convert a ciphertext under a (delegator) public key of any existing public-key encryption (PKE) scheme into another ciphertext under a (delegatee) public key of any existing PKE scheme (possibly different from the delegator one). The proxy has a re-encryption key generated from the delegator’s secret key and the delegatee public key. Thus UPRE generalizes proxy re-encryption by supporting arbitrary PKE schemes and allowing to convert ciphertexts into ones of possibly different PKE schemes. In this work, we
provide syntax and definitions for both UPRE and a variant we call relaxed UPRE. The relaxed variant means that decryption algorithms for re-encrypted ciphertexts are slightly modified but still only use the original delegatee secret keys for decryption.
construct a UPRE based on probabilistic indistinguishability obfuscation (PIO). It allows us to re-encrypt ciphertexts polynomially many times.
construct relaxed UPRE from garbled circuits (GCs). We provide two variants of this construction, one which allows us to re-encrypt ciphertexts polynomially many times, and a second one which satisfies a stronger security requirement but only allows us to re-encrypt ciphertexts a constant number of times
Neural Circuit Synthesis from Specification Patterns
Get PDFWe train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study
A Temporal Logic for Strategic Hyperproperties
Get PDFHyperproperties are commonly used in computer security to define information-flow policies and other requirements that reason about the relationship between multiple computations. In this paper, we study a novel class of hyperproperties where the individual computation paths are chosen by the strategic choices of a coalition of agents in a multi-agent system. We introduce HyperATL^*, an extension of computation tree logic with path variables and strategy quantifiers. HyperATL^* can express strategic hyperproperties, such as that the scheduler in a concurrent system has a strategy to avoid information leakage. HyperATL^* is particularly useful to specify asynchronous hyperproperties, i.e., hyperproperties where the speed of the execution on the different computation paths depends on the choices of the scheduler. Unlike other recent logics for the specification of asynchronous hyperproperties, our logic is the first to admit decidable model checking for the full logic. We present a model checking algorithm for HyperATL^* based on alternating word automata, and show that our algorithm is asymptotically optimal by providing a matching lower bound. We have implemented a prototype model checker for a fragment of HyperATL^*, able to check various security properties on small programs
Future Moment Assessment for Action Query
No full textIn this paper, we aim to tackle the task of Assessing Future Moment of an Action of Interest (AFM-AI). The goal of this task is to assess if an action of interest will happen or not as well as the starting moment of the action. We aim to assess starting moments at any time-horizon of the future. To this end, we tackle the regression task of the starting moments as a generation task using a Deterministic Residual Guided Variational Regression Module (DR-VRM), which is built on a Variational Regression Module (VRM) and a deterministic residual network. The VRM takes the uncertainty into account and is capable of generating diverse predictions for the starting moment. The deterministic network encourages the VRM to learn from deterministic residual information in order to generate more precise predictions for moment assessment. Experimental results on three datasets clearly show that the proposed method is capable of generating both diverse and precise predictions of starting moments for query actions
BGPeek-a-Boo: Active BGP-based Traceback for Amplification DDoS Attacks
Get PDFAmplification DDoS attacks inherently rely on IP spoofing to steer attack traffic to the victim. At the same time, IP spoofing undermines prosecution, as the originating attack infrastructure remains hidden. Researchers have therefore proposed various mechanisms to trace back amplification attacks (or IP-spoofed attacks in general). However, existing traceback techniques require either the cooperation of external parties or a priori knowledge about the attacker.
We propose BGPeek-a-Boo, a BGP-based approach to trace back amplification attacks to their origin network. BGPeek-a-Boo monitors amplification attacks with honeypots and uses BGP poisoning to temporarily shut down ingress traffic from selected Autonomous Systems. By systematically probing the entire AS space, we detect systems forwarding and originating spoofed traffic. We then show how a graph-based model of BGP route propagation can reduce the search space, resulting in a 5x median speed-up and over 20x for 1/4 of all cases. BGPeek-a-Boo achieves a unique traceback result 60% of the time in a simulation-based evaluation supported by real-world experiments