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As their effectiveness remains self-claimed, the community is divided on whether a secure PoW protocol is possible. In order to resolve this ambiguity and to lay down the foundation of a common body of knowledge, this paper introduces a multi-metric evaluation framework to quantitatively analyze PoW protocols' chain quality and attack resistance. Subsequently we use this framework to evaluate the security of these improved designs through Markov decision processes. We conclude that to date, no PoW protocol achieves ideal chain quality or is resistant against all three attacks.
We attribute existing PoW protocols' imperfect chain quality to their unrealistic security assumptions, and their unsatisfactory attack resistance to a dilemma between "rewarding the bad" and "punishing the good". Moreover, our analysis reveals various new protocol-specific attack strategies. Based on our analysis, we propose future directions toward more secure PoW protocols and indicate several common pitfalls in PoW security analyses.
Paterson Royal Holloway, University of London. We show that the problem of reconstructing encrypted databases from access pattern leakage is closely related to statistical learning theory. This new viewpoint enables us to develop broader attacks that are supported by streamlined performance analyses. This already goes significantly beyond the state-of-the-art for such attacks, as represented by Kellaris et al. Our analysis relies on an application of learning theory to PQ-trees, special data structures tuned to compactly record certain ordering constraints.
Finally, we generalize from ranges to consider what learning theory tells us about the impact of access pattern leakage for other classes of queries, focusing on prefix and suffix queries. We illustrate this with both concrete attacks for prefix queries and with a general lower bound for all query classes. We also show a very general reduction from reconstruction with known or chosen queries to PAC learning. Misspelled keywords have become an appealing target in search poisoning, since they are less competitive to promote than the correct queries and account for a considerable amount of search traffic.
Search engines have adopted several countermeasure strategies, e. However, a sophisticated class of attack, which we term as linguistic-collision misspelling, can evade auto-correction and poison search results. Cybercriminals target special queries where the misspelled terms are existent words, even in other languages e. In this paper, we perform the first large-scale analysis on linguistic-collision search poisoning attacks.
In particular, we check 1. We leverage edit distance operations and linguistic properties to generate misspelling candidates. To more efficiently identify linguistic-collision search terms, we design a deep learning model that can improve collection rate by 2.
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Our results show that the abuse is prevalent: around 1. We also find that cybercriminals mainly target categories of gambling, drugs, and adult content. Mobile-device users disproportionately search for misspelled keywords, presumably due to small screen for input. Our work highlights this new class of search engine poisoning and provides insights to help mitigate the threat. Fuzzing has become the de facto standard technique for finding software vulnerabilities.
However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs. Most popular fuzzers use evolutionary guidance to generate inputs that can trigger different bugs. Such evolutionary algorithms, while fast and simple to implement, often get stuck in fruitless sequences of random mutations. Gradient-guided optimization presents a promising alternative to evolutionary guidance. Gradient-guided techniques have been shown to significantly outperform evolutionary algorithms at solving high-dimensional structured optimization problems in domains like machine learning by efficiently utilizing gradients or higher-order derivatives of the underlying function.
However, gradient-guided approaches are not directly applicable to fuzzing as real-world program behaviors contain many discontinuities, plateaus, and ridges where the gradient-based methods often get stuck. In this paper, we propose a novel program smoothing technique using surrogate neural network models that can incrementally learn smooth approximations of a complex, real-world program's branching behaviors. We further demonstrate that such neural network models can be used together with gradient-guided input generation schemes to significantly increase the efficiency of the fuzzing process.
Our extensive evaluations demonstrate that NEUZZ significantly outperforms 10 state-of-the-art graybox fuzzers on 10 popular real-world programs both at finding new bugs and achieving higher edge coverage. NEUZZ found 31 previously unknown bugs including two CVEs that other fuzzers failed to find in 10 real-world programs and achieved 3X more edge coverage than all of the tested graybox fuzzers over 24 hour runs.
Zhao University of Chicago. Lack of transparency in deep neural networks DNNs make them susceptible to backdoor attacks, where hidden associations or triggers override normal classification to produce unexpected results. For example, a model with a backdoor always identifies a face as Bill Gates if a specific symbol is present in the input. Backdoors can stay hidden indefinitely until activated by an input, and present a serious security risk to many security or safety related applications, e.
We present the first robust and generalizable detection and mitigation system for DNN backdoor attacks.
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Our techniques identify backdoors and reconstruct possible triggers. We identify multiple mitigation techniques via input filters, neuron pruning and unlearning. We demonstrate their efficacy via extensive experiments on a variety of DNNs, against two types of backdoor injection methods identified by prior work.
Our techniques also prove robust against a number of variants of the backdoor attack. Their protocol uses slightly more communication than competitive schemes working over fields. However, implementation-wise, their approach allows for arithmetic to be carried out using native 32 or bit CPU operations rather than modulo a large prime. The authors thus conjectured that the increased communication would be more than made up for by the increased efficiency of implementations. In this work we answer their conjecture in the affirmative. We do so by implementing their scheme, and designing and implementing new efficient protocols for equality test, comparison, and truncation over rings.
We further show that these operations find application in the machine learning domain, and indeed significantly outperform their field-based competitors. In particular, we implement and benchmark oblivious algorithms for decision tree and support vector machine SVM evaluation. Large botnet-based flooding attacks have recently demonstrated unprecedented damage. However, the best-known end-to-end availability guarantees against flooding attacks require costly global-scale coordination among autonomous systems ASes.
A recent proposal called routing around congestion or RAC attempts to offer strong end-to-end availability to a selected critical flow by dynamically rerouting it to an uncongested detour path without requiring any inter-AS coordination. This paper presents an in-depth analysis of the in feasibility of the RAC defense and points out that its rerouting approach, though intriguing, cannot possibly solve the challenging flooding problem. An effective RAC solution should find an inter-domain detour path for its critical flow with the two following desired properties: 1 it guarantees the establishment of an arbitrary detour path of its choice, and 2 it isolates the established detour path from non-critical flows so that the path is used exclusively for its critical flow.
However, we show a fundamental trade-off between the two desired properties, and as a result, only one of them can be achieved but not both. Worse yet, we show that failing to achieve either of the two properties makes the RAC defense not just ineffective but nearly unusable. When the newly established detour path is not isolated, a new adaptive adversary can detect it in real time and immediately congest the path, defeating the goals of the RAC defense.
The first lesson of this study is that BGP-based rerouting solutions in the current inter-domain infrastructure seem to be impractical due to implicit assumptions e. Second, we learn that the analysis of protocol specifications alone is insufficient for the feasibility study of any new defense proposal and, thus, additional rigorous security analysis and various network evaluations, including real-world testing, are required.
Finally, our findings in this paper agree well with the conclusion of the major literature about end-to-end guarantees; that is, strong end-to-end availability should be a security feature of the Internet routing by design, not an ad hoc feature obtained via exploiting current routing protocols. A multi-signature scheme allows a group of signers to collaboratively sign a message, creating a single signature that convinces a verifier that every individual signer approved the message. The increased interest in technologies to decentralize trust has triggered the proposal of highly efficient two-round Schnorr-based multi-signature schemes designed to scale up to thousands of signers, namely BCJ by Bagherzandi et al.
In this work, we point out serious security issues in all currently known two-round multi-signature schemes without pairings. First, we prove that none of the schemes can be proved secure without radically departing from currently known techniques. Namely, we show that if the one-more discrete-logarithm problem is hard, then no algebraic reduction exists that proves any of these schemes secure under the discrete-logarithm or one-more discrete-logarithm problem.
We point out subtle flaws in the published security proofs of the above schemes except CoSi, which was not proved secure to clarify the contradiction between our result and the existing proofs.
Next, we describe practical sub-exponential attacks on all schemes, providing further evidence to their insecurity. Being left without two-round multi-signature schemes, we present mBCJ, a variant of the BCJ scheme that we prove secure under the discrete-logarithm assumption in the random-oracle model. Our experiments show that mBCJ barely affects scalability compared to CoSi, allowing signers to collaboratively sign a message in about 2 seconds, making it a highly practical and provably secure alternative for large-scale deployments.
We present Ouroboros Crypsinous, the first formally analyzed privacy-preserving proof-of-stake blockchain protocol. To model its security we give a thorough treatment of private ledgers in the G UC setting that might be of independent interest.
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To prove our protocol secure against adaptive attacks, we introduce a new coin evolution technique relying on SNARKs and key-private forward secure encryption. The latter primitive—and the associated construction—can be of independent interest. We stress that existing approaches to private blockchain, such as the proof-of-work-based Zerocash are analyzed only against static corruptions.
Payment channels emerged recently as an efficient method for performing cheap micropayments in cryptocurrencies. In contrast to traditional on-chain transactions, payment channels have the advantage that they allow for nearly unlimited number of transactions between parties without involving the blockchain. In this work, we introduce Perun, an off-chain channel system that offers a new method for connecting channels that is more efficient than the existing technique of routing transactions'' over multiple channels.
To this end, Perun introduces a technique called virtual payment channels'' that avoids involvement of the intermediary for each individual payment. In this paper we formally model and prove security of this technique in the case of one intermediary, who can be viewed as a payment hub'' that has direct channels with several parties. Our scheme works over any cryptocurrency that provides Turing-complete smart contracts. As a proof of concept, we implemented Perun's smart contracts in Ethereum.
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Phishing attacks have reached record volumes in recent years. Simultaneously, modern phishing websites are growing in sophistication by employing diverse cloaking techniques to avoid detection by security infrastructure. In this paper, we present PhishFarm: a scalable framework for methodically testing the resilience of anti-phishing entities and browser blacklists to attackers' evasion efforts.
We use PhishFarm to deploy 2, live phishing sites on new, unique, and previously-unseen. We reported subsets of these sites to 10 distinct anti-phishing entities and measured both the occurrence and timeliness of native blacklisting in major web browsers to gauge the effectiveness of protection ultimately extended to victim users and organizations.