With cyber incidents on the rise around the world, keeping your data secure has never been more important in 2024.
Cyber attacks can devastate your business, leaving your reputation in tatters and your users at risk of further attacks once their data is compromised.
When these incidents strike, there are different tools available to protect your IT infrastructure and keep your organizational data out of the hands of attackers.
One of these essential tools is an Intrusion Detection System (IDS), which allows companies to identify security breaches in an instant so that they can create a response plan to respond to an attack.
This article tells you everything you need to know about intrusion detection systems, exploring their types while giving real-world examples of IDS solutions.
What is an Intrusion Detection System (IDS)? Definition
An Intrusion Detection System (IDS) is a security tool designed to monitor network or system activities for suspicious behaviour, policy violations, or unauthorized access attempts. It's designed to detect potential security breaches, such as hacking, malware, or insider threats, and raise alerts when these anomalies are identified.
The primary goal of an IDS is to detect and respond to potential security threats, breaches, or vulnerabilities. It typically does this using signature-based detection, where known patterns of malicious activity are compared against incoming data, and anomaly-based detection, which looks for deviations from normal behaviour.
This makes it valuable for detecting zero-day attacks or subtle irregularities in system operations.
An IDS itself does not block or prevent attacks; it only detects them and alerts system administrators. These alerts allow security teams to investigate and respond to threats in real-time, reducing the damage caused by intrusions.
How do intrusion detection systems work?
IDS works by analyzing network traffic patterns and comparing them to known attack signatures or deviations from normal behaviour. When an IDS detects suspicious activity, it generates an alert that can be sent to a security administrator or logged for later review.
Two of the most common detection for IDS methods are signature-based detection and anomaly-based detection:
- In signature-based detection, IDS compares incoming data or system behavior against a database of known attack patterns, also called "signatures." These signatures are predefined sequences of actions, traffic patterns, or known malware fingerprints that are characteristic of particular attacks. When the IDS detects a match between current activity and a known signature, it triggers an alert.
- Anomaly-based detection works by creating a baseline of normal network or system behavior, typically through machine learning or statistical analysis. It then continuously compares current activities to this baseline. When a deviation occurs, such as unexpected spikes in network traffic, unusual access times, or strange user behaviors, the IDS flags the activity as potentially malicious.
Once an IDS identifies a suspicious activity, it generates an alert, providing details such as the nature of the anomaly or attack and its source. In some setups, IDS is paired with an Intrusion Prevention System (IPS), which can then automatically block or contain threats upon detection, enhancing the overall defence system.
Either way, an IDS gives security teams the necessary information to investigate and respond quickly. Once a breach is detected, teams can quarantine affected systems, shutting down compromised services, or updating security protocols to prevent further breaches.
Types of IDS
1. Network-based IDS (NIDS)
NIDs monitor the entire network by analyzing network traffic in real time. Positioned at strategic points within the network, like routers or switches, NIDS inspects data packets as they travel through the system.Its goal is to identify suspicious patterns, such as unusual traffic volumes, port scans, or attempts to exploit known vulnerabilities.
NIDS uses both signature-based detection, where traffic is compared against a database of known attack signatures, and anomaly-based detection, which looks for deviations from established network behavior. This makes it particularly effective at detecting threats that attempt to infiltrate or spread across a network. However, its effectiveness can be reduced in encrypted environments, as it cannot inspect encrypted traffic without decryption capabilities.
2. Host-based IDS (HIDS)
HIDS focuses on monitoring individual devices or systems. It tracks activities like file modifications, logins, and program executions on a specific host (e.g., a server or workstation). HIDS operates by analyzing logs and system behavior, looking for unauthorized changes or suspicious activities that could indicate malware infection, privilege escalation, or insider threats.
Unlike NIDS, which examines network traffic, HIDS has deeper visibility into the system itself, making it ideal for detecting attacks that target specific machines. However, because HIDS only monitors one host at a time, it is often deployed alongside NIDS to cover both network-wide and system-specific threats.
IDS vs Firewall: What’s the difference?
An IDS and a firewall serve distinct yet complementary roles in network security.
While firewalls are proactive measures designed to prevent attacks, IDSes are reactive tools that provide visibility into security incidents, enabling a swift response to detected intrusions.
A firewall primarily functions as a barrier that controls incoming and outgoing network traffic based on predetermined security rules, effectively blocking unauthorized access to and from a network. It focuses on preventing attacks by filtering traffic and can allow or deny connections based on IP addresses, protocols, and ports.
In contrast, an IDS monitors network or system activities to detect suspicious behavior or policy violations, alerting administrators to potential threats without actively blocking them. While firewalls are proactive measures designed to prevent attacks, IDS are reactive tools that provide visibility into security incidents, enabling a swift response to detected intrusions.
Examples of intrusion detection systems
1. Snort
Snort is a free and open-source NIDS and intrusion prevention system (IPS). It is one of the most popular IDSes in the world and is used by organizations of all sizes to protect their networks from attack. Snort uses both signature-based and anomaly-based detection methods. Signature-based detection involves looking for known attack patterns in network traffic.
3. Suricata
Suricata is a free and open-source NIDS and IPS capable of multi-threaded processing, allowing it to handle high-throughput networks effectively. It supports both intrusion detection and prevention, meaning it can not only alert on potential threats but also take action to block them. Suricata can analyze various protocols, including HTTP, TLS, and DNS, providing deep packet inspection capabilities that enhance its detection accuracy.
3. McAfee Network Security
McAfee Network Security is a network-based IDS that integrates advanced threat intelligence and machine learning algorithms to detect and respond to sophisticated attacks in real time. NSP offers extensive visibility into network traffic, along with automated response capabilities that help mitigate threats promptly. It is particularly well-suited for enterprise environments where scalability and integration with existing security infrastructure are critical.
4. OSSEC
OSSEC is a widely used host-based IDS that focuses on monitoring the integrity of system files, log files and real-time event correlation. OSSEC can detect various types of attacks, including file alterations, unauthorized logins, and system configuration changes. It is open-source and can be deployed in various environments, from small businesses to large enterprises, providing detailed security alerts and compliance monitoring.
5. Cisco Secure IPS
Cisco Secure IPS uses advanced analytics and threat intelligence to monitor network traffic for suspicious activities, enabling it to detect a wide range of threats in real-time. It leverages ML algorithms to improve its detection accuracy and reduce false positives, which is particularly beneficial for organizations dealing with high volumes of network traffic.
