The Experts below are selected from a list of 10803 Experts worldwide ranked by ideXlab platform

Unamay Oreilly - One of the best experts on this subject based on the ideXlab platform.

  • adversarial co evolution of Attack and defense in a segmented computer network environment
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Erik Hemberg, Neal Wagner, Joseph R Zipkin, Richard Skowyra, Unamay Oreilly
    Abstract:

    In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that are effective against a range of Attackers. We consider network segmentation, a widely recommended defensive strategy, deployed against the threat of serial network security Attacks that delay the mission of the network's operator. We employ a simulation model to investigate the effectiveness over time of different defensive strategies against different Attack strategies. For a set of four network topologies, we generate strong Availability Attack patterns that were not identified a priori. Then, by combining the simulation with a co-evolutionary algorithm to explore the adversaries' action spaces, we identify effective configurations that minimize mission delay when facing the Attacks. The novel application of co-evolutionary computation to enterprise network security represents a step toward course-of-action determination that is robust to responses by intelligent adversaries.1

Sheng Peiyao - One of the best experts on this subject based on the ideXlab platform.

  • ACeD: Scalable Data Availability Oracle
    2021
    Co-Authors: Sheng Peiyao, Kannan Sreeram, Xue Bowen, Viswanath Pramod
    Abstract:

    A popular method in practice offloads computation and storage in blockchains by relying on committing only hashes of off-chain data into the blockchain. This mechanism is acknowledged to be vulnerable to a stalling Attack: the blocks corresponding to the committed hashes may be unavailable at any honest node. The straightforward solution of broadcasting all blocks to the entire network sidesteps this data Availability Attack, but it is not scalable. In this paper, we propose ACeD, a scalable solution to this data Availability problem with $O(1)$ communication efficiency, the first to the best of our knowledge. The key innovation is a new protocol that requires each of the $N$ nodes to receive only $O(1/N)$ of the block, such that the data is guaranteed to be available in a distributed manner in the network. Our solution creatively integrates coding-theoretic designs inside of Merkle tree commitments to guarantee efficient and tamper-proof reconstruction; this solution is distinct from Asynchronous Verifiable Information Dispersal (in guaranteeing efficient proofs of malformed coding) and Coded Merkle Tree (which only provides guarantees for random corruption as opposed to our guarantees for worst-case corruption). We implement ACeD with full functionality in 6000 lines of Rust code, integrate the functionality as a smart contract into Ethereum via a high-performance implementation demonstrating up to 10,000 transactions per second in throughput and 6000x reduction in gas cost on the Ethereum testnet Kovan

  • ACeD: scalable data Availability oracle
    2021
    Co-Authors: Sheng Peiyao
    Abstract:

    A popular method in practice offloads computation and storage in blockchains by relying on committing only hashes of off-chain data into the blockchain. This mechanism is acknowledged to be vulnerable to a stalling Attack: the blocks corresponding to the committed hashes may be unavailable at any honest node. The straightforward solution of broadcasting all blocks to the entire network sidesteps this data Availability Attack, but it is not scalable. In this work, we propose ACeD, a scalable solution to this data Availability problem with O(1) communication efficiency, the first to the best of our knowledge. The key innovation is a new protocol that requires each of the N nodes to receive only O(1/N) of the block, such that the data is guaranteed to be available in a distributed manner in the network. Our solution creatively integrates coding-theoretic designs inside of Merkle tree commitments to guarantee efficient and tamper-proof reconstruction; this solution is distinct from Asynchronous Verifiable Information Dispersal (in guaranteeing efficient proofs of malformed coding) and Coded Merkle Tree (which only provides guarantees for random corruption as opposed to our guarantees for worst-case corruption). We implement ACeD with full functionality in 6000 lines of Rust code, integrate the functionality as a smart contract into Ethereum via a high-performance implementation demonstrating up to 10,000 transactions per second in throughput and 6000x reduction in gas cost on the Ethereum testnet Kovan

Viswanath Pramod - One of the best experts on this subject based on the ideXlab platform.

  • ACeD: Scalable Data Availability Oracle
    2021
    Co-Authors: Sheng Peiyao, Kannan Sreeram, Xue Bowen, Viswanath Pramod
    Abstract:

    A popular method in practice offloads computation and storage in blockchains by relying on committing only hashes of off-chain data into the blockchain. This mechanism is acknowledged to be vulnerable to a stalling Attack: the blocks corresponding to the committed hashes may be unavailable at any honest node. The straightforward solution of broadcasting all blocks to the entire network sidesteps this data Availability Attack, but it is not scalable. In this paper, we propose ACeD, a scalable solution to this data Availability problem with $O(1)$ communication efficiency, the first to the best of our knowledge. The key innovation is a new protocol that requires each of the $N$ nodes to receive only $O(1/N)$ of the block, such that the data is guaranteed to be available in a distributed manner in the network. Our solution creatively integrates coding-theoretic designs inside of Merkle tree commitments to guarantee efficient and tamper-proof reconstruction; this solution is distinct from Asynchronous Verifiable Information Dispersal (in guaranteeing efficient proofs of malformed coding) and Coded Merkle Tree (which only provides guarantees for random corruption as opposed to our guarantees for worst-case corruption). We implement ACeD with full functionality in 6000 lines of Rust code, integrate the functionality as a smart contract into Ethereum via a high-performance implementation demonstrating up to 10,000 transactions per second in throughput and 6000x reduction in gas cost on the Ethereum testnet Kovan

  • Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
    2019
    Co-Authors: Yu Mingchao, Sahraei Saeid, Li Songze, Avestimehr Salman, Kannan Sreeram, Viswanath Pramod
    Abstract:

    In this paper, we propose coded Merkle tree (CMT), a novel hash accumulator that offers a constant-cost protection against data Availability Attacks in blockchains, even if the majority of the network nodes are malicious. A CMT is constructed using a family of sparse erasure codes on each layer, and is recovered by iteratively applying a peeling-decoding technique that enables a compact proof for data Availability Attack on any layer. Our algorithm enables any node to verify the full Availability of any data block generated by the system by just downloading a $\Theta(1)$ byte block hash commitment and randomly sampling $\Theta(\log b)$ bytes, where $b$ is the size of the data block. With the help of only one connected honest node in the system, our method also allows any node to verify any tampering of the coded Merkle tree by just downloading $\Theta(\log b)$ bytes. We provide a modular library for CMT in Rust and Python and demonstrate its efficacy inside the Parity Bitcoin client.Comment: To appear in Financial Cryptography and Data Security (FC) 202

Erik Hemberg - One of the best experts on this subject based on the ideXlab platform.

  • adversarial co evolution of Attack and defense in a segmented computer network environment
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Erik Hemberg, Neal Wagner, Joseph R Zipkin, Richard Skowyra, Unamay Oreilly
    Abstract:

    In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that are effective against a range of Attackers. We consider network segmentation, a widely recommended defensive strategy, deployed against the threat of serial network security Attacks that delay the mission of the network's operator. We employ a simulation model to investigate the effectiveness over time of different defensive strategies against different Attack strategies. For a set of four network topologies, we generate strong Availability Attack patterns that were not identified a priori. Then, by combining the simulation with a co-evolutionary algorithm to explore the adversaries' action spaces, we identify effective configurations that minimize mission delay when facing the Attacks. The novel application of co-evolutionary computation to enterprise network security represents a step toward course-of-action determination that is robust to responses by intelligent adversaries.1

Neal Wagner - One of the best experts on this subject based on the ideXlab platform.

  • adversarial co evolution of Attack and defense in a segmented computer network environment
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Erik Hemberg, Neal Wagner, Joseph R Zipkin, Richard Skowyra, Unamay Oreilly
    Abstract:

    In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that are effective against a range of Attackers. We consider network segmentation, a widely recommended defensive strategy, deployed against the threat of serial network security Attacks that delay the mission of the network's operator. We employ a simulation model to investigate the effectiveness over time of different defensive strategies against different Attack strategies. For a set of four network topologies, we generate strong Availability Attack patterns that were not identified a priori. Then, by combining the simulation with a co-evolutionary algorithm to explore the adversaries' action spaces, we identify effective configurations that minimize mission delay when facing the Attacks. The novel application of co-evolutionary computation to enterprise network security represents a step toward course-of-action determination that is robust to responses by intelligent adversaries.1