An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any malicious activities or violations are typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system.

There are two main types of intrusion detection systems – network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A NIDS is designed to sit on the network, usually as a separate system connected to a span or mirror port, and passively monitor all network traffic that passes through its network segments. It analyzes the network and transport layers of the network traffic to detect suspicious activity using signatures or anomaly detection methods. A HIDS is installed on individual hosts or end points like servers, workstations, firewalls etc. and monitors events occurring within those systems like access to critical files, changes to critical systems files and directories, signs of malware etc.

Some key aspects of how intrusion detection systems work:

• Signatures/Rules/Patterns – The IDS has a database of attack signatures, rules or patterns that it uses to compare network traffic and system events against to detect known malicious behavior. The signatures are constantly updated as new threats emerge.
• Anomaly detection – Some advanced IDS can detect anomalies or deviations from a defined baseline of normal user or system behavior. It builds up a profile of what is considered normal behavior and detect anomalies from that statistical norm. This helps catch previously unknown threats.
• Protocol analysis – The IDS analyzes the network traffic at different protocol levels like TCP/IP, HTTP etc. to detect protocol violations, suspicious traffic patterns and policy violations.
• Log file monitoring – The host-based IDS monitors system log files for events like unauthorized file access, changes to system files and processes that could indicate a compromise.
• Packet inspection – The network IDS can inspect the actual content of packets on the network at different layers to detect payload anomalies, malware signatures, suspicious URLs, file transfers etc.
• Real-time operation – Modern IDS work in real-time and flag any potential incidents immediately as they are detected to facilitate quick response.
• Alerts – When the IDS detects a potential incident, it generates an alert. The alert usually contains details like source/destination IPs, protocol used, rule/signature that triggered it, time of detection etc. Alerts are sent to a central management system.
• Incident response tools – Many IDS integrate with tools like network packet capture solutions to allow security teams to review captured network traffic associated with an alert for further analysis.

While IDS are very useful in detecting threats, they also have some limitations:

• Generate high false positives – Due to their very sensitive nature, IDS may detect normal benign traffic as attacks incorrectly resulting in high false alarms. Too many false alerts can desensitize security teams.
• Easily evaded – Experienced attackers know the common attack patterns and signatures monitored by IDS and are able to subtly modify their behavior or use obfuscation to evade detection.
• No prevention – IDS are passive, only generating alerts. They cannot actively block or prevent threats on their own. Response still depends on human security teams.
• Resource intensive – Monitoring all network and system activity continuously in real-time requires high compute and storage resources which increases infrastructure and management costs.
• Complex to deploy and manage at scale – As networks and infrastructures grow in size, deploying, correlating alerts from and managing multiple IDS poses operational challenges. A centralized SIEM is needed.

To mitigate these limitations, modern IDS have evolved and many organizations integrate them with other preventive security controls like firewalls, web gateways and endpoint protections that can block threats. Machine learning and AI analytics are also being used to enhance anomaly detection abilities to catch novel threats. Correlation of IDS alerts with data from other systems through SIEM platforms improves accuracy and reduces false alarms.

Despite some weaknesses, intrusion detection systems continue to play a critical role in most security programs by providing continuous monitoring capabilities and acting as early warning systems for threats and policy violations. When rigorously maintained and paired with preventive controls, they can significantly strengthen an organization’s security posture.