Modern cyber threats rarely rely on obvious break-in attempts. Instead, they move through legitimate channels, encrypted traffic, and trusted applications, making visibility more important than ever. Understanding the different types of intrusion detection systems is essential for building an effective monitoring and response strategy.
Each type of IDS system serves a distinct purpose, from analyzing network traffic to monitoring individual hosts and application behavior.
In this guide, BTI Communications Group will explain how intrusion detection systems work and explore the different types of IDS systems used to identify suspicious activity across today’s complex IT environments.
What is an Intrusion Detection System (IDS)
An Intrusion Detection System (IDS) is a security tool that monitors network traffic, system behavior, or protocol interactions to identify unauthorized access or malicious activity. Unlike an Intrusion Prevention System (IPS), an IDS does not block threats on its own; it detects and alerts so analysts can respond.
How Does an Intrusion Detection System Work?
Unlike firewalls or antivirus tools that are primarily preventive, an IDS focuses on observation and detection. It helps teams identify intrusions that may have already bypassed perimeter controls.
Core Functions of an IDS
- Data Collection: IDS sensors gather information from network taps, SPAN ports, host logs, system calls, or application runtime data.
- Normalization & Preprocessing: Raw data is transformed into a consistent format, so patterns can be compared efficiently.
- Analysis: The IDS inspects this data using one or more detection methods to determine if an event looks normal or suspicious.
- Alerting & Logging: When a threat is detected, an alert is generated and logged. Alerts can be fed into a SIEM for correlation with other data.
- Contextual Enrichment: Modern IDS tools also add context (user, geo, time, device) to better prioritize alerts. (This is more advanced and often tied to SIEM/UEBA integrations.)
Types of Intrusion Detection Systems
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Network Intrusion Detection Systems (NIDS)
A Network IDS analyzes traffic flowing across network segments; it inspects packet headers, payloads, and metadata for malicious patterns or anomalies. These sensors are placed at strategic points like gateway choke points or core switches.
Host Intrusion Detection Systems (HIDS)
Host IDS runs on individual systems (servers, endpoints) and analyzes logs, system events, changes to critical files, and even process behavior. This gives deep visibility into what’s happening on the machine itself rather than across the network.
Hybrid Intrusion Detection Systems
A Hybrid IDS merges multiple IDS types, for instance, combining host and network data or blending detection methods. This results in better coverage and fewer blind spots.
Application Protocol-Based Intrusion Detection System (APIDS)
APIDS monitors communication at the application layer, not just packets or logs. It understands the semantics of a protocol (e.g., SQL queries, API requests) so it can detect abnormal application behavior that network IDS might miss.
Protocol-Based Intrusion Detection System (PIDS)
Protocol IDS inspects communication protocols closely, such as HTTP, FTP, or SMB. Placed near servers, it checks whether the protocol is being used properly or whether malicious sequences are present.
How Do Hackers Evade Intrusion Detection System?
Modern attacks are actively designed to bypass detection systems. Intrusion Detection Systems analyze traffic patterns, payloads, and behavior to identify threats, but skilled adversaries use evasion techniques to slip past these defenses unnoticed.
Below are the most common IDS evasion techniques and why they work.
Fragmentation
Fragmentation involves breaking malicious payloads into smaller packets so they appear harmless when inspected individually. Instead of sending a single SQL injection string in one request, an attacker splits it into multiple TCP fragments. If the IDS does not correctly reconstruct the session before analysis, it may fail to detect the attack.
Packet Encoding
Packet encoding disguises malicious content by altering its format without changing its functionality. Effective IDS programs normalize and decode traffic before inspection, reducing evasion.
How Packet Encoding Bypasses Different Types of IDS
If the IDS does not normalize the traffic before inspection, the attack may go undetected.
Traffic Obfuscation
Traffic obfuscation involves making malicious traffic appear legitimate or confusing enough to evade detection.
Techniques Include:
- Modifying packet timing (slow attacks)
- Using non-standard protocol implementations
- Manipulating TCP flags
- Injecting random or junk data into requests
- Changing case sensitivity in commands
Encryption (TLS blind sports)
Attackers increasingly hide payloads and command-and-control inside encrypted sessions. If your IDS cannot decrypt traffic, you may only see metadata, not content. The best mitigation is pairing network detection with endpoint telemetry (EDR/HIDS) and strong certificate and proxy policies.
How Do Intrusion Detection Systems Detect Anomalies?
Intrusion Detection Systems detect anomalies by establishing a baseline of normal network and system behavior, then identifying deviations that may indicate malicious activity. Instead of relying only on fixed rules, modern IDS platforms analyze traffic patterns, user behavior, and protocol activity to flag suspicious events for further investigation.
Understanding how IDS detects anomalies is essential for evaluating detection accuracy, false positive rates, and overall effectiveness within a security architecture.
Signature-Based Detection
Signature IDS compares activity against a library of known attack signatures. It’s fast and reliable for previously observed threats, but ineffective against unknown or zero-day attacks.
Anomaly-Based Detection
Anomaly systems build a statistical or behavioral baseline of “normal” activity, then flag deviations as suspicious. This method can spot novel attacks or unusual behavior, but may produce more false positives.
Hybrid-Based Detection
Hybrid-based detection combines signature and anomaly methods, yielding broader detection coverage with a balance of speed and depth. Some modern IDS platforms also use machine learning to refine baselines and improve signal quality over time.
Firewall vs Intrusion Detection System (IDS): What’s the Difference?
Firewalls and Intrusion Detection Systems (IDS) are often mentioned together, but they perform distinct roles within a security architecture. While both monitor network traffic, they operate with different objectives, inspection methods, and response capabilities.
The table below highlights the key differences between firewalls and intrusion detection systems (IDS), including their primary function, inspection depth, response capability, and deployment strategy within a layered security architecture.
A firewall decides who is allowed to communicate.
An IDS determines whether that communication is malicious.
In a mature security program, IDS becomes significantly more valuable when its alerts are correlated with identity, endpoint, and cloud activity, not evaluated in isolation.
Most modern breaches occur over legitimate, allowed channels (like HTTPS on port 443), which is why organizations deploy IDS alongside firewalls as part of a layered defense strategy.
How to Integrate the Different Types of Intrusion Detection Systems
An Intrusion Detection System is most effective when it operates as part of a broader detection and response architecture. On its own, IDS generates alerts. Integrated properly, it becomes a high-fidelity signal source within a coordinated security ecosystem.
SIEM (Security Information and Event Management)
IDS feeds structured alert data into a SIEM, where it is:
- Correlated with logs from firewalls, servers, cloud platforms, and identity systems
- Enriched with threat intelligence (IP/domain reputation, IOC feeds)
- Prioritized based on risk scoring and asset criticality
This correlation reduces false positives and helps distinguish isolated anomalies from active incidents.
UEBA (User & Entity Behavior Analytics)
While traditional IDS focuses on network or host activity, UEBA adds behavioral context by analyzing:
- User login patterns
- Privilege escalation
- Lateral movement
- Abnormal data access
When IDS detects suspicious traffic and UEBA detects abnormal user behavior tied to the same entity, confidence in the alert increases significantly.
EDR (Endpoint Detection & Response)
Network IDS identifies suspicious traffic patterns, but it cannot see process-level activity on endpoints. EDR fills that gap by providing:
- Process execution details
- File modifications
- Registry changes
- Memory-based attack visibility
For example, if IDS detects outbound command-and-control traffic, EDR can confirm which process initiated the connection — enabling faster containment.
Firewalls, IPS, and SOAR
Although IDS does not block traffic by default, its alerts can trigger automated responses when integrated with:
- Intrusion Prevention Systems (IPS)
- Next-generation firewalls
- SOAR (Security Orchestration, Automation, and Response) platforms <br></br>
This allows organizations to convert detection into action, such as isolating a host, blocking an IP address, or disabling a compromised account.
BTI designs these integrations so alerts become verified incidents with documented response, reducing false positives and improving audit defensibility.
Architectural Perspective
In a mature security stack:
- Firewalls enforce policy
- IDS detects suspicious activity
- SIEM correlates signals
- EDR provides endpoint depth
- SOAR automates response
IDS acts as a visibility layer within a layered defense model, increasing detection coverage without replacing preventive controls.
Pros and Cons of Intrusion Detection Systems
Intrusion Detection Systems provide critical visibility into malicious activity, but they are not standalone security solutions. Understanding both their strengths and limitations is essential for proper deployment.
Pros
1. Visibility Into Allowed Traffic
Most modern attacks occur over legitimate, allowed channels (e.g., HTTPS on port 443). Firewalls may permit this traffic based on policy, but IDS can analyze it for malicious behavior.
This makes IDS particularly valuable for detecting:
- Command-and-control communications
- Data exfiltration
- Exploit payloads over trusted ports
- Lateral movement within internal networks
2. Detection of Advanced and Insider Threats
Host-based IDS (HIDS) can detect:
- Unauthorized file modifications
- Privilege escalation attempts
- Suspicious process behavior
- Configuration tampering
Because IDS operates inside the environment, it can detect threats that have already bypassed perimeter defenses, including insider threats.
3. Forensic and Investigative Value
IDS generates detailed logs of suspicious activity, which are critical for:
- Incident response
- Root cause analysis
- Compliance audits
- Post-breach investigations
In many cases, IDS logs provide the first indication that a breach occurred.
4. Complements Preventive Controls
IDS enhances a layered security model. It does not replace firewalls or endpoint protection; it increases detection depth and coverage.
Properly integrated, IDS strengthens overall detection capability across the network.
Cons
1. No Native Blocking Capability (Traditional IDS)
A standard IDS detects and alerts but does not automatically block malicious traffic. Without integration into IPS or automated response workflows, threats may continue after detection.
2. False Positives and Alert Fatigue
Signature tuning and anomaly thresholds require continuous adjustment. Poorly configured IDS deployments often produce:
- Excessive alerts
- Low-priority noise
- Analyst fatigue
High false positive rates reduce trust in alerts and slow incident response.
3. Encrypted Traffic Blind Spots
Network-based IDS cannot inspect encrypted payloads without TLS decryption. As encrypted traffic now dominates internet communication, this creates visibility gaps.
Organizations often compensate by combining NIDS with endpoint telemetry (EDR or HIDS).
4. Resource and Performance Overhead
IDS can introduce operational complexity:
- Network IDS requires strategic sensor placement
- Host IDS consumes system resources
- Continuous tuning and rule updates are required
Without skilled management, IDS effectiveness declines over time.
5. Limited Context Without Integration
An IDS alert alone may lack sufficient context to determine severity. Without correlation via SIEM or behavioral analytics, alerts can be difficult to prioritize accurately.
The solution is not more alerts. It is better placement, tuning, and correlation, backed by operational ownership.
When to Deploy Different Types of Intrusion Detection Systems
The table below outlines common security scenarios and the most appropriate type of Intrusion Detection System (IDS) to deploy in each case.
Is Your Intrusion Detection Strategy Actually Detecting Threats?
An IDS that isn’t properly tuned or integrated can create a false sense of security. Encrypted traffic, lateral movement, and alert fatigue may allow real threats to go undetected. BTI can quickly assess your detection gaps and help you close them before they become incidents.
