Module Introduction

SiCAP-Data Analysis, based on data, scenario-oriented and algorithm-supported, conducts comprehensive intelligent analysis of IT asset configuration, monitoring, operation, logs, processes and other data based on big data, machine learning, deep learning and other technologies, including landing scenarios in multiple technological directions such as anomalous risk analysis, multi-dimensional primary cause, root cause analysis, intelligent auditing, intelligent operation, intelligent prediction, and intelligent robotic assistants, which improves the overall risk perception and security dynamic protection.

Feature Introduction

• Unusual Risk Analysis

  Key Inclusions:Log anomaly detection: based on log clustering, pattern recognition and other machine learning techniques and algorithms to discover abnormal logs and potential risks.
  Abnormal Behavior Analysis: Establish a behavioral baseline, correlate user and asset behaviors, and use machine learning and other algorithms to identify suspicious abnormal behaviors that seriously deviate from the baseline.
  Threat Attack Monitoring: Based on network traffic, apply artificial intelligence technologies and algorithms such as machine learning, deep learning, etc. to backtrack and analyze abnormal network behaviors, and discover potential security threats and unknown network attacks by matching them with threat intelligence and behavioral models.
• Multidimensional Principal Cause Analysis

  It is mainly a multi-dimensional principal cause analysis of traffic events that can identify the main dimensions that generate traffic anomalies.
• Root Cause Analysis

  For alarms or anomalous events, combined with network topology, asset and service correlation relationships, and fault transmission relationships, intelligent algorithms such as decision trees and correlation analysis are applied to locate the root cause of the problem and effectively shorten the fault resolution time.
• Intelligent Audit

  Apply big data and machine learning algorithms to operation sessions and historical audit records, form a rule base by analyzing character commands and user behaviors, audit operation commands and behaviors to see if they are normal by matching the rule base, carry out intelligent auditing of operation sessions, provide intelligent scoring and health level, assist audit administrators in their daily audit work, and dynamically update the rule base based on the final audit results.
• Intelligent Operation

  Key Inclusions：①　Operation Authorization Recommendation: Apply intelligent algorithms to recommend authorization for new users based on historical authorization, user roles, attributes, and asset information for quick authorization.。
  Rapid Dispatch Recommendation: When an asset malfunction or abnormality occurs, based on intelligent algorithms and automated scripts to collect malfunction characteristics, analyze the cause of the malfunction, form malfunction characteristics, and generate work order recommendation information based on these malfunction characteristics and historical operation behaviors, which facilitates the rapid formation of special operation work orders for operation processing.
  运③　Operation Failure Solution Recommendation: By analyzing historical operation information and combining the knowledge of operation solutions provided by users to form an operation knowledge base, we recommend solutions based on failure characteristics and related historical operation information to assist users in quickly resolving failures.。
• Intelligent Prediction

  Relying on big data analysis and machine learning capabilities, it establishes a failure prediction model for business scenarios, and conducts correlation analysis and deep learning based on the correlation of historical alarms. Meanwhile, it is combined with the data acquired by the monitoring system in real time to perform trend analysis and prediction of IT failures, capacity, etc., to provide early warning of possible future failures and realize intelligent prediction of failures.
• Intelligent Assistants

  It builds a bridge between information producers and information consumers, adopts a high-performance computing framework, organizes data such as the collection of users' historical behaviors and functional correlations, establishes a user portrait system based on machine learning, data mining and other technologies, integrates a variety of algorithmic models, and makes all-round, multi-dimensional and accurate predictions and recommendations of the functions or contents required by users, so as to reduce users' operating costs.