Cisco 200-201 Exam Questions

Rule-based detection methods rely on predefined rules and patterns that are known beforehand. These rules are created based on prior knowledge of what constitutes normal and abnormal behavior.

Statistical detection, on the other hand, involves analyzing data to identify anomalies. It is based on assumptions about what normal behavior looks like and uses statistical methods to detect deviations from this norm.

Rule-based systems are typically straightforward but may miss novel attacks that do not match existing rules.

Statistical methods can detect previously unknown threats by recognizing patterns that deviate from established baselines but may produce more false positives.

Intrusion Detection Systems (IDS) Concepts

Comparative Studies on Rule-based and Statistical Anomaly Detection

Understanding Anomaly Detection in Network Security

In the NetFlow flow-record format, a dataflow set is a collection of data records that follow the template FlowSet in an export packet. Each data record corresponds to a flow and contains values for the fields defined in the template FlowSet. This allows for efficient organization and retrieval of flow information by NetFlow collectors.

Understanding Cisco Cybersecurity Operations Fundamentals (CBROPS)

NetFlow Version 9 Flow-Record Format Documentation

Rule-based detection systems operate using predefined patterns and signatures to identify known threats. These patterns are based on prior knowledge of attack methods and vulnerabilities.

Behavioral detection systems, on the other hand, analyze the normal behavior of a network or system to establish a baseline. They then monitor for deviations from this baseline, which may indicate potential threats.

Rule-based systems are effective at detecting known threats but may struggle with novel or zero-day attacks that do not match existing signatures.

Behavioral systems can detect unknown threats by recognizing abnormal activities, making them useful in identifying zero-day exploits and other sophisticated attacks.

Comparison of Rule-based and Behavioral Detection Methods in IDS

Advantages of Behavioral Analysis in Network Security

Cybersecurity Detection Techniques

The exhibit shows a pcap file capturing multiple TCP SYN packets directed at the same destination IP address.

High volume of SYN packets with very little variance in time: This pattern is indicative of a SYN flood attack, a type of Denial of Service (DoS) attack where numerous SYN requests are sent to overwhelm the target system.

SYN packets acknowledged from several source IP addresses: This can be indicative of a Distributed Denial of Service (DDoS) attack where multiple compromised hosts (botnet) are used to generate traffic.

These characteristics suggest that the network is under a SYN flood or DDoS attack, aiming to exhaust the target's resources and disrupt service availability.

Understanding SYN Flood Attacks

Analysis of DDoS Attack Patterns

Wireshark Analysis Techniques for Intrusion Detection

Indicators of Attack (IoA) refer to observable behaviors or artifacts that suggest a security breach or ongoing attack.

When internal hosts communicate with countries outside the business range, it may indicate data exfiltration or command-and-control communication to an external threat actor.

Unlike Indicators of Compromise (IoC) which indicate that a system has already been compromised, IoAs are often used to identify malicious activity in its early stages.

Monitoring for unusual outbound connections is a crucial aspect of detecting advanced persistent threats (APTs) and other sophisticated attacks.

Difference Between Indicators of Compromise and Indicators of Attack

Cyber Threat Detection Using Indicators of Attack

Network Monitoring for Anomalous Behavior