International Business Machines Corporation

Kyndryl, Inc.

HuyAnh Dinh Ngo

Srinivas Babu Tummalapenta

Adam Lee Griffin

Aankur Bhatia

Seng Chai Gan

Gary I Givental

Shikhar Kwatra

Carlos E Aguilera

Nikhilkumar V Shah

Michael John Suzio

Paul A Ragone

Vinod A Valecha

John Kurian

Hariharan N Venkitachalam

Aaron T Wodrich

Adam J Paquin

Wesley A Khademi

Stanley Bryan Hardter

John Blackman

A method controls a modification of an adjustable device. A system establishes a circle of trust for a plurality of devices, where at least one of the plurality of devices is an adjustment control device for adjusting an adjustable device. Operations of the adjustment control device are controlled via a blockchain, where the blockchain must approve the adjustment control device before an adjustment of the adjustable device is performed by the adjustment control device. The system receives an approval from the blockchain for the adjustment control device to adjust the adjustable device, and the adjustment control device adjusts the adjustable device in response to receiving the approval from the blockchain.

Secure environment device management

An example operation may include one or more of receiving storage requests endorsed by blockchain peers of a blockchain, selecting a group of the endorsed storage requests to be stored together and ordering the group of endorsed storage requests with respect to each other based on timestamps, encoding the group of ordered and endorsed storage requests into an image, and storing the encoded image within a data section of a block of the blockchain.

Image encoding for blockchain

A system and method for automatic remediation of non-compliance events are provided. In embodiments, a computer-implemented method includes: accessing a compliance profile and a remediation profile, wherein the compliance profile includes compliance data regarding rules for an enterprise and the remediation profile includes remediation data regarding remediation actions to address non-compliance with one or more of the rules; generating mapped data by mapping compliance data in the compliance profile to remediation data in the remediation profile; receiving non-compliance event data from a workload node in a network; extracting information from the non-compliance event data including the workload node associated with the event and a cause of event; determining a remediation action for the event based on the information and the mapped data; and invoking automatic performance of the remediation action at the workload node based on the determined remediation action.

Automatic remediation of non-compliance events

A method, system, and computer program product for malicious virtual machine detection is provided. The method passes network traffic from a set of virtual machines to a set of network resources. The network traffic passes through a network monitor. The network monitor monitors a set of traffic characteristics for the network traffic. A change in a network traffic characteristic is identified. The change results in a modified traffic characteristic. The method identifies a first virtual machine of the set of virtual machines. The first virtual machine is associated with the modified traffic characteristics. An alert is then generated which identifies the first virtual machine and the modified traffic characteristic.

Malicious virtual machine detection

A method, system and computer-usable medium are disclosed for providing a travel network infrastructure. A dedicated Li-F connection is provided for each physical space such as a seat, expected to accommodate a passenger in a travel network such as an airline. A passenger is authenticated according to a physical space occupied by the passenger. If authentication is successful, a secure Li-Fi channel is established for the passenger. The access point of the Li-Fi channel is only leveraged by the passenger and no other passengers.

System and method for credential generation for wireless infrastructure and security

A method provides an intermediate mitigation of a vulnerability in a particular computer system. One or more processors receive a description of a vulnerability of a computer system to a malicious attack. The processor(s) perform an NLP analysis of the description of the vulnerability in order to extract risk information related to the vulnerability, where the risk information includes an identity of a type of vulnerable computer system resource in the computer system. The processor(s) match the vulnerable computer system resource to a computer system resource in a particular computer system, and perform an intermediate mitigation action that reduces a functionality of the computer system resource in the particular computer system until a solution is implemented that both restores the functionality of the computer system resource in the particular computer system and mitigates the vulnerability of the particular computer system to the malicious attack.

Security protection rule prediction and enforcement

A system for prioritizing network resource dispatch may include receiving a request for a network resource from a mobile device associated with a user, the request comprising mobile device and user characteristics, and location information. A request priority may be determined based on some combination of a mobile device score and user score. The request priority is assigned to the request. A highest ranked mobile device request priority is determined, and the network resource is dispatched to a location corresponding to the highest ranked request priority. A heatmap may be generated to illustrate a geographical representation of each mobile device data usage rate at each mobile device respective location. The request priorities may change based on any combination of the mobile device characteristics, user characteristics, location information and the usage characteristics. Therefore, the priority of dispatching the network resource may be determined dynamically and updated as the inputs change.

Prioritizing requested network resource delivery

A method assigns a particular rule for a previous client to a new client for use in executing a security feature on a computer system used by the new client. One or more processors match a new client profile for the new client to a previous client profile for the previous client. The new client profile is based on types of one or more client assets of the new client and an intrusion detection alert history of the new client. The processor(s) assign the particular rule for the previous client to the new client based on the new client profile matching the previous client profile. The processor(s) receive information indicating that a violation of the particular rule has occurred, and execute a security feature of the computer system used by the new client in order to resolve the violation of the particular rule.

Artificial intelligence assisted rule generation

Provided are techniques for dynamic LIFI extenders routing. One or more geographic locations of a physical region are identified based on historical crowd density, historical network traffic, current crowd density, and current network traffic. For each of the one or more geographic locations and a plurality of mobile bots, a mobile bot of the plurality of mobile bots is sent to that geographic location at a specified time, where the mobile bot provides a dynamic Light Fidelity (LIFI) extender router that acts as a network access point, and users at that geographic location are associated with the mobile bot, where each of the users has a LIFI enabled mobile device that communicates with a network of devices by sending data to the dynamic LIFI extender router via LIFI.

Dynamic light fidelity (LIFI) extenders routing

A method manages software images in a blockchain-based environment of trusted devices. A trusted device creates a software image for sharing among members of a set of trusted devices that includes the trusted device. A blockchain mechanism stores the software image on a virtual machine that is in communication with the set of trusted devices, where the blockchain mechanism uses the set of trusted devices as peer members of the blockchain mechanism. A trusted device from the set of trusted devices receives, via the virtual machine, an access request for the software image from a requesting device from the set of trusted devices. The blockchain mechanism authorizes the access request by providing the software image to the requesting device.

A method improves a security of a computer system by building a new set of rules for the computer system. One or more processors input a plurality of client profiles to an artificial intelligence (AI) system, where the plurality of client profiles are based on an analysis of respective client environments comprising client assets and an intrusion detection alert history of a plurality of clients. The processor(s) match a new client profile to a respective client profile from the plurality of client profiles. The processor(s) build a new set of rules for the new client based on a similarity measure of the new client profile to the respective client profile. The processor(s) subsequently receive information indicating that a violation of the new set of rules has occurred and then execute a security feature of the computer system in order to resolve the violation of the new set of rules.

An event retrieval and analysis system compares counts of event data for a device to stored profile counts to determine if alerts should be triggered. Event data can be retrieved by a sensor. Rules for analyzing the event data can be retrieved based on the device. The event data is analyzed based on the rules to determine recordable events. Recordable events are organized into categories representing a type or severity of attack. Current event counts are calculated by summing the recordable events for each category. A normal profile is retrieved for the device and compared to the current event count. A percentage change trigger can be retrieved from a threshold matrix based on the current event count. The percentage increase of the current event count over the normal profile is calculated and compared to the percentage change trigger to determine if an alert is triggered by the analysis system.

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Sterling Heights, MI, United States of America

HuyAnh Dinh Ngo