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Most businesses now require all (or at least most) staff to have some level of knowledge of cybersecurity. This includes understanding common terms and what they mean in practice.
Doing a quick search on a common term (e.g. IDS definition) may be enough in the short term. Realistically, however, it’s unlikely to be sufficient in the long term. With that in mind, here is a brief overview of what you need to know about IDS.
The most basic answer to the question “What is IDS (definition)?” is that IDS is exactly what its full name says it is. In other words, it’s an intrusion detection system.
A more complete answer to the question “What is IDS (definition)?” is that an IDS monitors and analyzes network traffic to detect potential threats. When an IDS detects suspicious activities, It alerts other security tools and/or human administrators. They will then choose an appropriate response.
An IDS monitors both the volume and the content of traffic. Determining the volume is done by recording the number of data packets sent per second. Determining the content is done by analyzing the information in the packet headers.
More specifically, the IDS uses a range of detection mechanisms to determine whether or not the packages are suspicious. Here is a quick overview of them.
Signature-based detection looks for unique characteristics or behaviors associated with known threats. It is quick, highly reliable, and makes economical use of computing resources. Unfortunately, it is only effective against known threats such as identified malware. With that said, this accounts for a large percentage of threats. As a result, signature-based detection methods are very much the front line of cybersecurity.
Anomaly-based detection is a cybersecurity approach that centers on identifying deviations from established baselines or normal behavior within a network or system. Because it looks beyond predefined patterns, it can detect innovative attacks such as zero-day attacks. These are often missed by signature-based detection.
There are two main downsides to anomaly-based detection. The first is that it is slower and more resource-intensive than signature-based detection. This is why signature-based detection is generally used first. The second is that anomaly-based detection can be off-balanced by sudden changes in network behavior. It will, however, adapt to changes that take place over time.
Heuristic-based detection assesses the overall behavior of traffic. It looks for patterns or behaviors commonly associated with malicious activities. This approach is very effective against emerging threats such as variations of known attack techniques. It may therefore identify threats missed by both signature-based and anomaly-based detection. It is, however, more resource-intensive than either. This is why it is generally used after them.
Behavioral-based detection involves continuous monitoring of the typical behavior of users, systems, or networks. It establishes baselines for normal activities and triggers alerts when significant deviations from these norms are detected.
The main difference between behavioral-based detection and anomaly-based detection is that behavior-based detection works at a more granular level. In other words, anomaly-based detection primarily detects activities that are statistically rare or significantly different from the norm. It excels in identifying outliers or unusual occurrences.
By contrast, behavior-based detection can recognize deviations that may not be statistically rare but are considered abnormal within the context of broader behavioral patterns. This means that behavior-based detection is more effective at detecting subtle and persistent threats. Behavior-based detection is, however, very resource-intensive. This is why it’s generally used after other methods.
Network Behavior Analysis (NBA) is very similar to anomaly-based detection. The key difference is that NBA goes beyond individual packets or events. It also considers the relationships and interactions between various components and the patterns they create.
NBA is much more dynamic than anomaly-based detection and hence can alert quicker to emerging threats. As with heuristic- and behavioral-based detection, however, it is quite resource-intensive. This is why, like them, it tends to be used after other detection strategies.
If you want to take a closer look at the question “What is IDS (definition)?”, then you need to look into the different types of IDS. Here is a quick overview of them.
Hardware-based IDS: A dedicated physical appliance designed for intrusion detection, providing robust security through hardware mechanisms.
Software-based IDS: Utilizes software applications to implement intrusion detection functionalities, offering flexibility and scalability in security solutions.
Network-based IDS (NIDS): Monitors network traffic at strategic points, such as routers or switches, to identify suspicious patterns or activities indicating potential security threats.
Host-based IDS (HIDS): Installed on individual devices (hosts), like servers or endpoints, focusing on monitoring and preventing unauthorized activities specific to the host.
Cloud-based IDS (CIDS): Delivers intrusion detection services through cloud platforms, providing scalable and centralized security solutions for cloud-centric environments.
Wireless IDS (WIDS): Specialized in monitoring wireless networks, WIDS analyzes wireless traffic to detect and mitigate potential intrusions or unauthorized access.
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