Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

Smart contract has greatly improved the services and capabilities of blockchain,but it has become the weakest link of blockchain security because of its code nature.Therefore,efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system.Oriented to Ethereum smart contract,the study solves the problems of redundant input and low coverage in the smart contract fuzz.In this paper,a taint analysis method based on EVM is proposed to reduce the invalid input,a dangerous operation database is designed to identify the dangerous input,and genetic algorithm is used to optimize the code coverage of the input,which construct the fuzzing framework for smart contract together.Finally,by comparing Oyente and ContractFuzzer,the performance and efficiency of the framework are proved.

A Novel Intrusion Detection Algorithm Based on Long Short Term Memory Network

In recent years,machine learning technology has been widely used for timely network attack detection and classification.However,due to the large number of network traffic and the complex and variable nature of malicious attacks,many challenges have arisen in the field of network intrusion detection.Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection,this paper proposes a Bi-LSTM method based on attention mechanism,which learns by transmitting IDS data to multiple hidden layers.Abstract information and high-dimensional feature representation in network data messages are used to improve the accuracy of intrusion detection.In the experiment,we use the public data set KDD-Cup 99 for verification.The experimental results show that the model can effectively detect unpredictable malicious behaviors under the current network environment,improve detection accuracy and reduce false positive rate compared with traditional intrusion detection methods.

Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

Smart contract has greatly improved the services and capabilities of blockchain,but it has become the weakest link of blockchain security because of its code nature.Therefore,efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system.Oriented to Ethereum smart contract,the study solves the problems of redundant input and low coverage in the smart contract fuzz.In this paper,a taint analysis method based on EVM is proposed to reduce the invalid input,a dangerous operation database is designed to identify the dangerous input,and genetic algorithm is used to optimize the code coverage of the input,which construct the fuzzing framework for smart contract together.Finally,by comparing Oyente and ContractFuzzer,the performance and efficiency of the framework are proved.

A karyopherin constrains nuclear activity of the NLR protein SNC1 and is essential to prevent autoimmunity in Arabidopsis

The nucleotide-binding and leucine-rich repeat(NLR)proteins comprise a major class of intracellular immune receptors that are capable of detecting pathogen-derived molecules and activating immunity and cell death in plants.The activity of some NLRs,particularly the Toll-like/interleukin-1 receptor(TIR)type,is highly correlated with their nucleocytoplasmic distribution.However,whether and how the nucleocytoplasmic homeostasis of NLRs is coordinated through a bidirectional nuclear shuttling mechanism remains unclear.Here,we identified a nuclear transport receptor,KA120,which is capable of affecting the nucleocytoplasmic distribution of an NLR protein and is essential in preventing its autoactivation.We showed that the ka120 mutant displays an autoimmune phenotype and NLR-induced transcriptome features.Through a targeted genetic screen using an artificial NLR microRNA library,we identified the TIR-NLR gene SNC1 as a genetic interactor of KA120.Loss-of-function snc1 mutations as well as compromising SNC1 protein activities all substantially suppressed ka120-induced autoimmune activation,and the enhanced SNC1 activity upon loss of KA120 functionappeared to occur at the protein level.Overexpression of KA120 efficiently repressed SNC1 activity and led to a nearly complete suppression of the autoimmune phenotype caused by the gain-of-function snc1-1 mutation or SNC1 overexpression in transgenic plants.Further florescence imaging analysis indicated that SNC1 undergoes altered nucleocytoplasmic distribution with significantly reduced nuclear signal when KA120 is constitutively expressed,supporting a role of KA120 in coordinating SNC1 nuclear abundance and activity.Consistently,compromising the SNC1 nuclear level by disrupting the nuclear pore complex could also partially rescue ka120-induced autoimmunity.Collectively,our study demonstrates that KA120 is essential to avoid autoimmune activation in the absence of pathogens and is required to constrain the nuclear activity of SNC1,possibly through coordinating SNC1 nucleocytoplasmic homeostasis as a potential mechanism.