General Electric Company

Annarita Giani

Lalit Keshav Mestha

Masoud Abbaszadeh

John William Carbone

Benjamin Edward Beckmann

Joseph James Salvo

Peter Koudal

Walter Charles Yund

Dan Yang

Junrong Yan

Patricia MacKenzie

According to some embodiments, a system may facilitate collaborative transaction processing associated with a supply chain having a first entity and a second entity. In particular, a first entity database may store electronic records including information associated with at least a portion of the supply chain, and a first entity communication port may exchange information via a distributed computer system. A first entity computer processor may retrieve from the first entity database the information associated with the at least a portion of the supply chain. A subset of information about the supply chain may be identified by the first entity computer processor as being of interest to the second entity. The identified subset of information about the supply chain may then be recorded via a secure, distributed transaction ledger.

Blockchain enabled collaborative transaction information processing for a supply chain

According to some embodiments, a plurality of monitoring nodes may each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. A node classification computer may determine, for each monitoring node, a classification result indicating whether each monitoring node is in a normal or abnormal state. A disambiguation engine may receive the classification results from the node classification computer and associate a Hidden Markov Model ('HMM') with each monitoring node. For each node in an abnormal state, the disambiguation engine may execute the HMM associated with that monitoring node to determine a disambiguation result indicating if the abnormal state is a result of an attack or a fault and output a current status of each monitoring node based on the associated classification result and the disambiguation result.

Decision system and method for separating faults from attacks

An original transaction data store may provide information about a series of transactions over time. A transaction data collection platform may group some of those transactions into a subset of transactions and apply a hash function to that subset to create a signature representing verifiable state information associated with an enterprise. The transaction data collection platform may then record information about the signature via a secure, distributed transaction meta-ledger without recording the subset of transactions. According to some embodiments, a transaction audit platform may retrieve the signature from the meta-ledger and retrieve original information associated with the subset of transactions (e.g., from an offline original transaction data store). The transaction audit platform may then apply the hash function to the original information to re-create an original signature, compare the retrieved signature with the original signature, and output an indication of a verification result of the comparison.

Hierarchical meta-ledger transaction recording

Streams of monitoring node signal values over time, representing a current operation of the industrial asset, are used to generate current monitoring node feature vectors. Each feature vector is compared with a corresponding decision boundary separating normal from abnormal states. When a first monitoring node passes a corresponding decision boundary, an attack is detected and classified as an independent attack. When a second monitoring node passes a decision boundary, an attack is detected and a first decision is generated based on a first set of inputs indicating if the attack is independent/dependent. From the beginning of the attack on the second monitoring node until a final time, the first decision is updated as new signal values are received for the second monitoring node. When the final time is reached, a second decision is generated based on a second set of inputs indicating if the attack is independent/dependent.

Learning method and system for separating independent and dependent attacks

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Niskayuna, NY, United States of America

Annarita Giani