Micro Focus Lcc

Novell, Inc.

Phonex Broadband Corporation

Douglas M Grover

Michael F Angelo

Martin Arlitt

Lyman D Horne

W Paul Willes

Michael J Miller

Alexander Hoole

Michael A Aday

Lloyd Leon Burch

Samuel Charles Kingston

Vamsi Krishna

Ronald S Leahy

Blaine S Dennis

Qian-Ru Zhai

Stephan F Jou

Russel F Weiser

Zhenbin Lin

Ye Jiang

Meng-Jie Chen

William Earl Rieske

Duane F Buss

Tony L Worwood

Source code for a type of malware is received. For example, the source code may be source code from a type of computer virus. An Artificial Intelligence (AI) algorithm is identified. For example, the AI algorithm may be ChatGPT. The source code of the type of malware is run through the AI algorithm to produce mutated source code for the type of malware. A prediction algorithm is used to predict a signature of the mutated source code for the type of malware. For example, the prediction algorithm is trained using existing source code of different types of malware to generate a prediction model. The signature of the mutated source code for the type of malware is then compared to a signature of a potentially new type of malware to determine if the signatures are similar.

Identification of variants of artificial intelligence generated malware

A defect in the code of a software program is identified. An initial ranking for the defect in the code of the software program is determined. One or more network websites are crawled to identify information associated with the defect in the code of the software program. The information associated with the defect in the defect in the code of the software program is analyzed. In response to analyzing the information associated with the defect in the code of the software program, a second ranking is created for the defect in the code of the software program. The defects in the code of the software program and the second ranking are generated for display in a graphical user interface. By prioritizing which defects are more critical, the quality of the released software improved. In addition, the released software is more secure because critical defects have been removed.

Using threat intelligence to manage software fixes

A watermark is generated. The generated watermark is specific to an individual owner of a media. The media may be any type of electronic media, such as, an image, a document, a movie, an audio file, a software application, and/or the like. The watermark is inserted into the media. The watermark in the media is changed when ownership of the media is changed. For example, as the media is sold to a new owner, the new owner's watermark is added to the media so that a chain of title can be verified directly from the media. In addition, the chain of title may also be verified via a blockchain.

Using watermarks to identify a chain of title in media

A determination is made to see if a user has authenticated to a computer system using a plurality of authentication levels. For example, the user may have had a first session where the user is authenticated at authentication level one and a second session where the user is authenticated at authentication level two. Behavior of the user is separately tracked at each of the plurality of authentication levels to identify separate usage patterns of the user at each of the plurality of authentication levels. Anomalous behavior of the user is identified based on one or more variations from the separate usage patterns of the user at, at least one of the plurality of authentication levels. An action is taken based on identifying the anomalous behavior of the user. For example, the user's account may be locked, or an administrator may be notified.

Anomaly detection based on multi-level authentication

A transaction block in a blockchain is generated. The transaction block in the blockchain comprises a token that points to blockchain data in a tokenization table. An event is detected (e.g., an administrative event). The event is to forget the blockchain data in the tokenization table. In response to detecting the event, the blockchain data in the tokenization table is forgotten. For example, forgetting the blockchain data may comprise permanently deleting the blockchain data in the tokenization table and/or permanently deleting an encryption key for the blockchain data in the tokenization table.

Forget me tokenization tables for blockchains

A current thread pattern is identified. For example, a thread pattern of a running software application is identified. Current resource information associated with the current thread pattern is identified. For example, the current resource information may include disk usage, packets sent, ports used, accounts created, etc. The current thread pattern and the current resource information associated with the current thread pattern are compared to an existing malicious thread pattern associated with a type of malware and existing malicious resource information associated with the existing thread pattern. A determination is made if the comparison meets a threshold. For example, if the current thread pattern is 90% similar to the existing malicious thread pattern and the current resource information is within 75% of the existing malicious resource information, the threshold is met. In response to the comparison meeting the threshold, an action is taken to mitigate the type of malware.

Identification of malicious programs using thread patterns and related data

A blockchain is created. The blockchain comprises an authentication block that defines one or more authentication credentials that are required to be provided by an endorser to add a first type of transaction block to the blockchain. For example, a user may be required to provide a username/password to add a smart contract transaction block to the blockchain. A request to add the first type of transaction block to the blockchain is received. The first type of transaction block is added to the blockchain. Adding the first type of transaction block to the blockchain is based on the endorser providing the one or more authentication credentials.

Managing smart contracts using a distributed ledger

A communication stream is received. For example, the communication stream may be a part of a communication session, such as, a voicemail, a videomail, a voice conference call, a video conference call, and/or the like. A determination is made if the communication stream is completely generated using a session watermark. The session watermark is associated with the communication session. In response to determining that the communication stream is completely generated using the session watermark, the communication stream is identified as a legitimate communication stream. In response to determining that the communication stream has not been completely generated using the session watermark, the communication stream is identified as potentially a vishing communication steam.

Prevention of vishing attacks

A request to add a new block to a blockchain is received. Data associated with the new block is scanned to identify malware and/or an anomaly. In response to identifying the malware and/or the anomaly in the data associated with the new block, an action is taken. The action includes: rejecting the request to add the new block to the blockchain, or removing the malware/anomaly from the new block and adding the new block to the blockchain. In a second embodiment, a malware event is identified that identifies malware/an anomaly in a block in a blockchain. In response to the malware event, an action is taken. The action includes: consolidating the blockchain, bypassing the block in the blockchain, consolidating the blockchain and bypassing the block in the blockchain, and deleting an encryption key that was used to encrypt the associated data that comprises the malware and/or the anomaly.

Blockchain malware protection

Thread information generated by one or more computing systems is captured. A thread pattern is identified from the captured thread information. The thread pattern is compared to a learned thread pattern. An anomaly is identified in the thread pattern based on a variance from the learned thread pattern. In response to identifying the anomaly in the thread pattern, an action is taken based on the anomalous thread pattern. For example, a user may be notified. The thread patterns may be extended to compare between operating systems, hypervisors, containers, and/or virtual machines.

Using thread patterns to identify anomalous behavior

A multi-factor authentication request of a user is received. For example, the multi-factor authentication request may include a valid username/password and a valid fingerprint scan. A first authentication factor of the multi-factor authentication request is an access authentication factor (e.g., the valid username/password) and a second authentication factor (e.g., the valid fingerprint scan) of the multi-factor authentication request is one of: a time-only authentication factor; a multi-session authentication factor; and a location-only authentication factor. The user is authenticated based on the first authentication factor and the second authentication factor. Access is granted to one or more resources according to one or more rules associated with the first authentication factor and second authentication factor.

Time-only based authentication factors

A first node in a distributed blockchain ledger validates all remaining nodes in the distributed blockchain ledger to secure the distributed blockchain ledger. The remaining nodes in the distributed blockchain ledger can be validated in various ways. For example, an encrypted package can be exchanged to obtain a unique session key for each of the remaining nodes in the distributed blockchain ledger. Another alternative is to receive split-key fragments from all the nodes in the distributed blockchain ledger. The received split-key fragments are used to generate a common key for validating all the remaining nodes in the distributed blockchain ledger.

Private secure blockchain

An anomaly on a computer network is identified by processing data generated by the computer network. The anomaly is identified based on a first anomaly threshold of a plurality of anomaly thresholds associated with the anomaly. In response to determining that the anomaly has met the first anomaly threshold of the plurality of anomaly thresholds associated with the anomaly, a first authentication level associated with the first anomaly threshold is identified. The plurality of anomaly thresholds associated with the anomaly have a plurality of associated authentication levels. A user interface is displayed to an administrator that includes a prompt to authenticate the administrator at the first authentication level. Authenticating the administrator at the first authentication level allows the administrator to take an action associated with the anomaly. For example, the administrator may unload an application that may likely have been compromised.

Tying anomalies to authentication levels

A request to install a first version of a software application is received. The first version of the software application is stored in a first blockchain in a first distributed blockchain ledger. In response to receiving the request to install the first version of the software application, the first version of the software application is validated by running a hash of the first blockchain. In response to validating the first version of the software application, the first version of the software application is installed from the blockchain to a device. The software application may also be validated after being installed to the device.

Growing community of inventors

Elk Ridge, UT, United States of America

Douglas M Grover