Graph-based detection systems and techniques are provided to identify potential malicious lateral movement paths. System and security events may be used to generate a network connection graph and detect remote file executions and/or other detections, for use in tracking malicious lateral movement across a computer network, such as a compromised computer network. Lateral movement determination across a computer network may be divided into two subproblems: forensic analysis and general detection. With forensic analysis, given a malicious node, possible lateral movement leading into or out of the node is identified. General detection identifies previously unknown malicious lateral movement on a network using a remote file execution detector, and/or other detectors, and a rare path anomaly detection algorithm.

Detecting malicious lateral movement across a computer network

Provided is a system, method and computer readable medium for detecting at least one non-compliant commercial card transaction for a plurality of transactions received from a merchant, and for generating at least one score for a received transaction, based at least partially on a scoring model, to determine whether a transaction is non-compliant. The scoring model includes at least one score determined by unsupervised learning with feedback from score influencing rules, case disposition data, transactional data, historical data and old scoring models and automatically modifying, at predefined intervals, the scoring model based on current score influencing rules and case disposition data. Machine learning is programmed to score the model based at least partially on a probability-based outlier detection algorithm and a clustering algorithm and to provide a case presentation system for audit and review of scored transactions and to receive input comprising case disposition data and score influencing rules.

System, Method, and Apparatus for Self-Adaptive Scoring to Detect Misuse or Abuse of Commercial Cards

A method, apparatus, system and a computer program product are provided for scoring performance of a chatbot by capturing specified node performance metrics based on user interactions with one or more nodes in a dialog flow for the chatbot; assigning a score to each of the one or more nodes based on the specified node performance metrics; and assessing a performance measure for at least a portion of the chatbot by combining scores assigned to selected nodes in the dialog flow.

System and method for scoring performance of chatbots

Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.

Monitoring and controlling continuous stochastic processes based on events in time series data

Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Customer journey management engine

Embodiments are directed to a method of performing autonomous learning for updating input features used for an artificial intelligence model, the method comprising receiving updated data of an information space that includes a graph of nodes having a defined topology, the updated data including historical data of requests to the artificial intelligence model and output results associated with the requests, wherein different categories of input data corresponds to different input nodes of the graph. The method may further comprise updating edge connections between the nodes of the graph by performing path optimizations that each use a set of agents to explore the information space over cycles to reduce a cost function, each connection including a strength value, wherein during each path optimization, path information is shared between the rest of agents at each cycle for determining a next position value for each of the set of agents in the graph.

Autonomous learning platform for novel feature discovery

A method for authenticating a user for access using artificial intelligence includes communicating a first user stimuli, receiving a first user response associated with a user to the first user stimuli, determining a probability that the user is an authenticated user based on whether the first user response to the first user stimuli corresponds to an expected user response for the first user stimuli, determining a second user stimuli to communicate based on the probability, communicating the second user stimuli, receiving a second user response associated with the user to the second user stimuli, determining the probability that the user is an authenticated user based on whether the second user response to the second user stimuli corresponds to an expected user response for the second user stimuli, and generating an authentication indication associated with the user based on the probability, the authentication indication indicating whether the user is authenticated.

System, method, and computer program product for using artificial intelligence to enhance communication for computer interfaces

To speed up the authentication process, a data security hub can create a predictive model using a clustering algorithm based on interaction data for previous access requests. The predictive model can be used to determine predicted communities for an incoming access request. The predicted communities can be used to determine a reduce set of registered authentication information to compare against authentication information in the incoming access request. The predicted communities can also be used to determine a risk score for the access request based on how similar the predicted communities for the incoming access request are to expected communities determined based on past access requests.

Data security using graph communities

A method is disclosed and includes receiving, by a first communication device, a first local authentication model, the first local authentication model being derived from a master authentication model at a remote server computer, and receiving a request to perform an interaction with a second communication device, the interaction being performed in an offline manner. The method may further include applying, by the first communication device, the first local authentication model to the interaction to determine a first authentication result and determining whether or not to allow the interaction to proceed based upon the first authentication result. The method may also include updating the first local authentication model using the master authentication model when the first communication device is online.

Offline authorization of interactions and controlled tasks

Described herein are a system and techniques for enabling access control utilizing one or more blockchains associated with a user. A blockchain provider can manage one or more blockchains specifically associated with a an entity, where each blockchain may be associated with a differing sensitivity level. The entity may be a person or a machine such as an IOT (Internet of Things) device. The blockchain provider can manage access control policies associated with each blockchain such that access to the data of the blockchain may be allowed or restricted to requesting entities according to those access control policies.

Techniques for data access control utilizing blockchains

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Li Yue

Author Tools

Chat about Author