6.4 AI and Machine Learning in Network Operations Flashcards
Summarize AI (generative and predictive) and machine learning in network operations. (22 cards)
What is artificial intelligence (AI) in network operations?
It simulates human intelligence in machines.
In network operations, AI automates tasks, optimizes performance, and analyzes network data to make decisions.
List the benefits of AI in network operations.
- Proactive fault detection
- Optimized traffic management
- Enhanced security
- Automated configurations
- Data-driven insights
Proactive fault detection: AI identifies potential issues before they impact the network.
Optimized traffic management: AI improves traffic flow by adjusting to real-time network conditions.
Enhanced security: AI detects and responds to security threats more quickly.
Automated configurations: AI simplifies the setup and management of network devices.
Data-driven insights: AI analyzes data to provide actionable insights for improving network performance.
What is predictive AI in network operations?
It forecasts future network events.
It uses historical data to predict traffic spikes, equipment failures, and potential bottlenecks before they happen.
What is generative AI in network operations?
It creates new solutions from existing data.
It optimizes configurations, designs, and resolves issues by generating solutions based on past network data and patterns.
Fill in the blank:
______ AI uses historical data to predict future network events.
Predictive
Predictive AI analyzes historical traffic data to help optimize bandwidth and prevent network congestion.
Why is predictive AI crucial for network fault management?
It anticipates potential network issues.
By analyzing patterns, predictive AI allows network operators to address problems before they occur, minimizing downtime.
How does AI-based anomaly detection work in network security?
It detects unusual network activity.
AI compares real-time data with learned patterns to identify and flag potential security threats, such as DDoS attacks or unauthorized access.
What is the role of machine learning in network optimization?
It analyzes data to improve network performance.
ML continuously learns from network behavior to optimize traffic flow, identify issues, and improve efficiency.
List the applications of machine learning in network security.
- Threat detection
- Anomaly detection
- Intrusion detection
- Vulnerability assessment
Threat detection: ML identifies potential threats based on patterns and behavior.
Anomaly detection: ML detects deviations from normal network behavior, flagging suspicious activity.
Intrusion detection: ML analyzes network traffic to identify unauthorized access attempts.
Vulnerability assessment: ML evaluates systems for potential weaknesses that could be exploited.
True or False:
Machine learning needs explicit programming to identify data patterns.
False
Machine learning enables systems to learn from data automatically, adapting to new patterns without human intervention.
Fill in the blank:
______ learning is used in SDN to optimize routing decisions based on ongoing performance feedback.
Reinforcement
Reinforcement learning enables systems to make decisions through trial and error, adjusting based on feedback to improve outcomes over time.
What is a supervised machine learning model in network operations?
It uses labeled data to make predictions.
Supervised learning is useful for classifying network traffic and detecting failures based on known data patterns.
Fill in the blank:
In machine learning, the process of adjusting model parameters based on data input is known as ______.
training
Training involves feeding data into machine learning algorithms to adjust their parameters, improving their ability to make accurate predictions or decisions.
What is an unsupervised machine learning model in network operations?
It identifies patterns in unlabeled data.
Unsupervised learning helps detect network anomalies and hidden patterns, often used for intrusion detection and anomaly detection.
What is a neural network in machine learning?
A model inspired by the human brain.
Neural networks can recognize complex patterns and are useful in network operations for tasks such as traffic prediction and fault detection.
Define:
deep learning
A subset of ML that uses multi-layer neural networks to analyze patterns.
As a subset of machine learning, deep learning models can identify complex patterns in large datasets, which helps in tasks like image recognition and detecting anomalies in network traffic.
True or False:
Generative AI can create new network configurations based on historical data.
True
Generative AI analyzes past configurations and generates optimized network settings that improve performance and scalability.
Describe how AI-based optimization works in network traffic routing.
It optimizes traffic paths based on real-time data.
AI analyzes current network conditions, such as bandwidth usage and latency, to dynamically select the most efficient routing paths.
Define:
AI-driven traffic prioritization
AI allocates network resources based on priority.
AI identifies critical data flows and ensures they are prioritized over less important traffic, maintaining performance during peak times.
True or False:
Machine learning can only be applied to small networks.
False
Machine learning can be scaled to large networks, improving network management, security, and performance for both small and large-scale environments.
What is AI-based load balancing?
AI dynamically distributes traffic across resources.
AI load balancing ensures optimal resource utilization by automatically distributing network traffic based on real-time conditions and workloads.
Describe how AI-powered automation improves network troubleshooting.
It automates fault detection and resolution.
AI identifies and addresses network issues by automatically applying predefined troubleshooting steps, reducing downtime.
