International Business Machines Corporation

Intel Corporation

Salman Abdul Baset

Chunqiang Tang

Long Wang

Petr Novotny

Qi Zhang

Hai Huang

Byung Chul Tak

Sambit Sahu

Philippe Suter

Manas Singh

Omer Tripp

Nitin Gaur

Anca Sailer

Harigovind Venkatraj Ramasamy

Alexei A Karve

Alla Segal

Palani Sakthi

Michael John Fork

Daniel W Bingham

Ashish Kundu

Ruchi Mahindru

Yu Deng

Mahesh Viswanathan

Hui Lei

Andrzej P Kochut

Donna N Dillenberger

Julian Timothy Dolby

Soumitra Sarkar

Minkyong Kim

Norbert George Vogl

Rafah A Hosn

Canturk Isci

Kirk Alan Beaty

Charles Otto Schulz

Mathew S Thoennes

John Erik Wittern

Shu Tao

Kevin Roland Fall

Donna N Eng Dillenberger

Ali Anwar

Tao Tao

Allan Douglas Knies

Sylvia Ratnasamy

Sahil Suneja

Gianluca Iannaccone

Ashu Gupta

Shih-Wei Li

Cuong Manh Pham

Maziar H Manesh

Kyung Hwa Kim

Rajesh Garg

Marianna Rapoport

Imani N Palmer

Bao Hua Dai

Nilton T Bila

Mohamed Fazal Mohamed Feroze

Sumithra Ravichandran

MadhuSudhan Kagalavadi Ningaiah

Katerina Argyraki

Byung-Gon Chun

Norbert Egi

Mihai Dobrescu

An example operation may include one or more of receiving, by a risk score module, a blockchain transaction proposal, obtaining transaction proposal data, obtaining external data, computing a risk score from the transaction proposal data and the external data, comparing the risk score to a risk score threshold, providing a commitment decision, based on the comparison, and one of committing or rejecting an endorsed transaction corresponding to the transaction proposal.

Anomalous transaction commitment prevention for database

An example operation may include one or more of receiving, by a blockchain node or peer comprising hot asset storage and cold asset storage, an asset request to provide an asset, the asset comprising a key-value pair, determining if the asset request may be satisfied without accessing a blockchain of a shared ledger, providing the asset to a requesting node or peer from hot asset storage in response to the blockchain node or peer determining the asset request may be satisfied without accessing the blockchain, and in response to the blockchain node or peer determining the asset request cannot be satisfied without accessing the blockchain, the method further comprising utilizing a pointer in cold asset storage to obtain the requested asset from the blockchain.

Database world state performance improvement

An example operation may include one or more of receiving, by a risk score module, a blockchain transaction proposal, obtaining transaction proposal data, obtaining external data, computing a risk score from the transaction proposal data and the external data, comparing the risk score to a risk score threshold, providing an endorsement decision, based on the comparison, and one of endorsing or rejecting the transaction proposal.

Anomalous transaction detection for database

An example operation may include one or more of receiving, by a blockchain node or peer, a deploy chaincode blockchain transaction, and in response establishing a cold asset storage repository comprising pointers to each of the assets of a blockchain stored in a shared ledger, each of the assets comprising a key-value pair, establishing a hot asset storage repository of predetermined size and configured to store a subset of the assets of the blockchain, and configuring asset classification rules to organize hot asset storage and cold asset storage.

Database asset fulfillment chaincode deployment

An example operation may include one or more of receiving, by a lead peer, a new block from an orderer node over a blockchain network that includes a plurality of peers, acquiring, by the lead peer, a shared memory handler for a channel of the blockchain network from an anchor peer, appending, by the lead peer, the new block to a shared memory buffer based on the handler, and notifying the plurality of the peers that the new block is available on the shared memory buffer.

Secure data dissemination

A method for updating an application executing on a virtual machine includes replicating the virtual machine to produce a replica, applying an update to the replica that is scheduled to be made to the virtual machine, in parallel with the applying, updating one of the virtual machine and the replica so that a state of the virtual machine matches a state of the replica, and continuing execution of the application on one of the virtual machine and the replica.

Patching systems and applications in a virtualized environment

An example system may include a processor and memory, wherein the processor is configured to perform one or more of receive a first biometric sample of a user from a terminal, execute a smart contract to compare the first biometric sample against a pre-recorded second biometric sample stored on a blockchain, in response to the match, acquire shared data of the user from a communication service provider, execute a smart contract to generate a question based on the shared data, receive an answer to the question from the user and execute a smart contract to record the answer on the blockchain, and execute a smart contract to authenticate the user based on the answer to the question and the first biometric sample.

Consented authentication

An example system may include a processor and memory, wherein the processor is configured to perform one or more of acquire a first biometric sample of a user, compare the first biometric sample of the user against a pre-recorded second biometric sample, in response to the match, access shared data of the user stored on a blockchain, generate a question based on the shared data, receive an answer to the question from the user, and authenticate the user based on the answer to the question and the first biometric sample.

Evaluating the performance of an application when migrated from a first environment in which the application is currently executing to a different second environment includes generating a configuration file using data obtained from the application executing in the first environment, installing the configuration file in a virtual machine residing in the second environment, launching the application in the virtual machine after the installing, and obtaining a metric indicative of the performance from the virtual machine.

Evaluating distributed application performance in a new environment

An example operation may include one or more of identifying, via a ledger validation thread, a corrupted data block that is stored within a chain of blocks on a distributed ledger, selecting a blockchain peer from among a plurality of blockchain peers that have access to the distributed ledger, and retrieving a replacement data block from the selected peer, determining whether the replacement data block is valid based on one or more of a previously stored validation block associated with the chain of blocks, and, in response to determining the replacement data block is valid, replacing the corrupted data block with the replacement data block on the distributed ledger.

Runtime self-correction for blockchain ledgers

Techniques that facilitate automated security testing of one or more mobile applications and/or one or more backend servers for the one or more mobile applications are provided. In one example, a system includes an analysis component and a security component. The analysis component can analyze computer instructions of a mobile application. The analysis component can also determine an identifier pattern comprising an application programming interface for one or more server endpoints associated with the mobile application. The security component can perform a security test for a server device based on the identifier pattern for the one or more server endpoints.

Automated security testing for a mobile application or a backend server

A computer system, method, and computer readable product are provided for identifying and isolating library code that has been obfuscated in software applications. A call graph is created for the execution of at least one module of preexisting library code within a bundle of software modules through either static analysis of the software code or dynamic analysis of the executing code, and then one or more anchor points are devised based upon the call graph that are indicative of the preexisting library code. Then a bundle of software modules can be analyzed or its execution monitored to determine if a discrete module of library code is present in the executing bundle based upon the modules' interaction with the one or more anchor points, and the discrete module of library code in the executing bundle can be identified as a module of preexisting library code.

Identifying and isolating library code in software applications

In an approach for determining optimal performance-related configurations of applications by analyzing peer data, a processor monitors instances of an application, wherein the instances of the application are running on a plurality of devices. A processor receives data related to: configuration parameters of the application, system parameters of the plurality of devices, and performance metrics of the plurality of devices. A processor determines correlation and dependencies between the configuration parameters with associated system parameters, using: an all pair analysis and a Pearson product-moment correlation coefficient (PPMCC). A processor determines dependencies between the configuration parameters with performance metrics. A processor recommends to a user, configuration settings of the application, based on the analysis of the data.

Peer-based optimal performance configuration recommendation

A method for recommending a web application programming interface (API) includes: a cloud computing service retrieving test source code from a remote computer of the developer; the cloud computing service determining whether a call to a first web API among a plurality of web APIs is present in the test source code; and when it is determined that the call is present, the cloud computing service reading at least one log file to extract information about at least one application that has used the first web API; the cloud computing service determining a first quality of the first web API from the log file; and the cloud computing service sending a message to the remote computer recommending a second web API among the web APIs having a second quality higher than the first quality.

Web API recommendations based on usage in cloud-provided runtimes

A method and system of determining a vulnerability of software. Libraries are downloaded and stored in a database. For each library, a set of features are extracted and stored in a library index table of the database. For each library, it is determined whether it poses a security concern and flagged accordingly in the library index table. Applications are downloaded and stored in the database. For each application a set of features are extracted and stored in an application index table of the database. For each application, the set of features of the application of the application are compared to the set of features of each of the libraries in the library index table to identify which libraries in the library index table are associated with the application. For each application, a name of the application and names of the associated libraries are stored in a vulnerability reference table in the database.

Detecting vulnerable applications

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New York, NY, United States of America

Salman Abdul Baset