Intrusion Detection and Prevention Systems and Techniques

By Dr. LaLanya Fair  |  03/07/2025

According to Merriam-Webster, an intrusion is “the act of wrongfully entering upon, seizing, or taking possession of the property of another.” Protecting the homeland is essential for any nation, and that protection includes defending assets, infrastructure, and citizens from various threats.

Intrusion Detection and Prevention Systems (IDPS) are fundamental to this protection. These systems are designed to identify security breaches or unauthorized user access, so that homeland security employees can take proactive or defensive measures to alleviate any potential harm.

What Are Intrusion Detection and Prevention Systems?

Intrusion detection and prevention systems consist of two types of systems:

• Intrusion Detection Systems (IDS), which search for suspicious activity in network traffic and send an alert when they detect suspicious activity
• Intrusion Prevention Systems (IPS), which monitor and prevent intrusions from internal or external sources

Both systems are the foundation of cybersecurity defenses in homeland security.

Types of IDPS

There are numerous types of intrusion detection and prevention systems. They include:

• Network-based IDPS: These systems examine network traffic for specific data segments or devices.
• Host-based IDPS: Installed on single devices, these systems observe encounters and status to detect any unfamiliar activity.
• Wireless IDPS: These systems are built specifically to monitor wireless network traffic and block unauthorized user access and attacks.

Network-Based IDPS

When data is exchanged between computers in a network, a network-based intrusion detection system analyzes that data and captures network traffic as it travels to a host. That information can be analyzed for unusual or abnormal behaviors or a particular signature. 

These types of computer systems are designed to monitor network traffic by using numerous sensors to sniff data packets traveling through the network. When anomalous or suspicious activity occurs, the system initiates an alarm, generates an automatic response, and sends a warning message to an administrator or the central computer system. 

Network-based intrusion detection systems (NIDS) are simple to safeguard and can be more complicated for an enemy to discover. However, NIDS analyze vast amounts of data. As a result, they can miss attacks in progress, may need manual involvement from administrators, and are often unable to analyze encrypted traffic on the network.

NIDS can also be subject to an excess of focus. When NIDS meticulously pursue an event on a network to determine whether there is an attack, other incidents may be granted a lower level of scrutiny. These features allow for security loopholes when unauthorized users are trying to dodge a network IDS.

For cloud computing, NIDS capture network traffic and analyze it to discover any potential intrusion. These types of intrusions include:

• Denial of Service (DoS) attacks: These attacks occur when legitimate users are unable to access information systems, devices, or other network resources due to the actions of a malicious cyber threat actor.
• Port scanning attacks: In port scanning attacks, hackers check open network ports to find vulnerabilities in a network.
• Botnet attacks: During botnet attacks, hackers take over internet-connected computers or mobile devices, using them to carry out very effective attacks. In fact, botnet attacks are responsible for some of the largest DDoS (distributed denial of service) attacks on record.

NIDS use a signature-based approach to evaluate collected information with a signature database to find internal matches with an intrusion. NIDS can also work as anomaly-based systems and compare current user behavior with normal behavior to determine whether there is an attack in progress.

Host-Based IDPS

Host-based intrusion detection systems analyze the activities on a particular machine and detect malicious activities. These systems operate on data collected from single computer systems and can reveal which processes and/or users are involved in malicious activities. 

These systems are used to safeguard the reliability of a cloud computing system. They can use information sources of two types: system logs and operating system audit trails. 

Most host-based IDPS software develops a “digital inventory” of files and the files’ contents; it then uses that inventory as a baseline for examining any system changes. The disadvantage to host-based IDPS use is that talented invaders who compromise a host can attack and undermine these host-based IDPS as well. 

Wireless IDPS

Wireless intrusion detection and prevention systems involve dedicated security devices or integrated software applications. These devices and software monitor a wireless local area network (WLAN) or a Wi-Fi network's radio spectrum for rogue access points and other wireless threats. These systems help to protect a network and ensure its operational continuity and integrity.

These types of systems are important because they are built for monitoring a wireless network's airspace to detect sudden, rogue, or unauthorized activities and frequencies. If any misconfigured or malicious device is operating or trying to operate on the network, the system can identify it and shut it down. Consequently, the system can prevent a device from implementing malicious attacks against a network and its users.

When wireless intrusion detection and prevention devices are advanced, they can discover and classify known wireless devices. They can also pinpoint their unique signal patterns to ascertain whether those devices could undermine the security of a wireless network.

These devices and software discover access points with configuration errors, monitor network usage and performance, and provide a layer of security for WLANs. As a result, users can enjoy a more secure, healthy, and available network.

Intrusion Detection and Prevention Techniques

Preventing intruders is more than just the use of systems, however. There are various techniques that can be used to detect unauthorized users or suspicious activity, such as:

• Network Behavior Analysis (NBA): This technique involves observing network traffic to discover threats that create uncommon traffic flows.
• Signature-Based Detection: This technique uses predetermined signatures of known security risks to uncover intrusions.
• Anomaly-Based Detection: This technique discovers deviations from standard user behavior to identify possible threats.

Network Behavior Analysis

Network behavior analysis, otherwise known as behavior analysis, involves the collection and analysis of internal network data to identify malicious or unusual activity.

NBA uses tools that analyze data from a wide range of sources. These tools also use machine learning to identify samples that could indicate an attack is in progress.

When network behavior analyses are completed over a continuous period, behavior monitoring allows companies to benchmark typical network behavior and recognize deviations. Any anomalies discovered can be escalated to a security team for advanced analysis.

These tools offer significant insight to help corporations protect against the latest cyber threats. NBA is especially good at catching new malware and mitigating vulnerabilities.

Signature-Based Detection

The process that is generally used to address software threats on a computer is known as signature-based detection. Some of those threats include viruses like a Trojan horse virus, worms, and malware.  

With signature-based detection, suitable signatures for each computer file are created. They are then compared with known signatures that have been stored and detected previously in signature-based detection.

The comparison process is continuous until it finds a match. Once this match occurs, a file is deemed a threat and is automatically blocked.

An example of real-world signature-based detection is the antivirus programs you install on your computer. These software programs use signature-based detection to check for malware.

Signature intrusion detection systems (SIDS) are based on pattern-matching techniques to find an attack. These systems can also use knowledge-based detection or misuse detection.

For example, SIDS are utilized by security personnel to find a prior intrusion. When an intrusion signature matches the signature of a preceding intrusion that is already stored in the signature database, it triggers an alarm signal. The host's logs can be examined to locate sequences of actions or instructions that have previously been recognized as malware in SIDS.

Anomaly-Based Detection

Anomaly-based detection revolves around defining typical network behavior and detecting deviations from that typical behavior. Network behavior typically aligns with a predefined behavior. Acceptable network behavior is developed or learned by the specifications of the network administrators.

An important phase in describing network behavior is an intrusion detection system’s capability to sort through various protocols at all levels. The system must be able to handle the protocols and understand its goal.

Although a protocol analysis involves considerable time, it can strengthen a rule set and create fewer false positive alarms.

A system's efficiency depends on how well it is tested and implemented on all protocols. The rule-defining process is also influenced by the various protocols that different vendors use. Additionally, there are custom protocols that also make defining a rule set a complicated job. 

For detection to occur accurately, thorough knowledge about acceptable network behavior needs to be created by system administrators. But once the rules are defined and protocol is built, then anomaly-based detection systems work well.

The malicious behavior of a user can be masked by acceptable behavior that goes unnoticed by system administrators monitoring an organization's outgoing and incoming network traffic. For instance, someone manipulating file paths (an action known as a directory traversal) on a targeted vulnerable server complying with network protocol can easily go unnoticed. This type of activity does not trigger any out-of-protocol, payload, or bandwidth limitation flags.

The main benefit of anomaly-based detection is that a novel attack for which a signature does not exist can be identified if it creates abnormal network activity patterns. One example would be when computer systems discover new, automated worms as the result of malicious behavior by hackers.

If a new system is infected with a worm, it starts scanning for other susceptible systems at an accelerated rate filling the network with malicious traffic. The bandwidth abnormality then triggers an alert.

Why Are Intrusion Detection and Prevention Systems Important to Homeland Security?

Integrating IDPS into homeland security policies is essential for various reasons:

• Timely threat detection: An intrusion detection system can recognize risks in real time, which allows for immediate action to alleviate possible damage and maintain a robust security environment.
• Automated response: Systems with defensive capabilities can autonomously deter revealed threats, decreasing the necessity for human involvement.
• Comprehensive monitoring: Intrusion detection and prevention systems offer broad oversight of network activities, guaranteeing no illicit actions go unnoticed.
• Data protection: An intrusion prevention system assists in protecting sensitive data against breaches by blocking intrusions from hackers.
• Regulatory compliance: An intrusion detection system or an intrusion prevention system and requires the implementation of vigorous security procedures.

Improving IDPS

There are various advanced techniques and technologies devoted to IDPS to improve their efficacy, including artificial intelligence (AI) and machine learning (ML).

Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning have significantly altered numerous sectors, including commercial security systems. These technological innovations provide advanced techniques for enhancing intrusion detection capabilities, leading to more proactive and vigorous security measures.

Artificial intelligence refers to the capability of a machine to imitate intelligent human behavior. AI encompasses a range of technologies from machine learning and natural language processing to deep learning. It aims to create systems that can function intelligently and independently.

Machine learning, a subset of AI, involves the use of algorithms and statistical models that enable computers to perform specific tasks without explicit instructions. By learning from data patterns, ML algorithms can make predictions or decisions based on new data inputs.

The Role of AI and ML in IDPS

Artificial intelligence provides advanced pattern recognition, decision-making capabilities, and adaptive defense mechanisms. Advanced pattern recognition helps identify small, difficult patterns that usually go unnoticed with traditional detection.

AI helps intrusion detection systems make real-time decisions using large datasets, which reduces the opportunity for security risks and cyber incidents. AI also aids intrusion detection systems in automatically adjusting response tactics based on the difficulty and context of identified threats, increasing their ability to neutralize complex attack methods.

Machine learning aids in anomaly detection, behavioral analysis, and dynamic threat modeling. Through ML algorithms, a system continuously learns and adapts to the ever-changing threat landscape. With its self-learning capabilities, ML makes sure an intrusion detection system is defended against emerging and polymorphic threats.

The Benefits of AI and ML

There are numerous benefits to incorporating AI and ML into IDPS. A system will have reduced false positives, early threat detection capabilities, and greater data processing capabilities. In addition, the system will continuously learn and adjust to potential threats to improve an organization's security environment and security infrastructure.

The Challenges of Using AI and ML

Innovation does not come without its challenges. For computer systems to adapt and learn, they need large data sets.

To build trust in a system, people need to understand the justification behind its methodology, so they need to be able to explain how a detection method operates. Intrusion detection and prevention systems must be sound enough to endure adversarial intrusions.

Integration with Threat Intelligence

Intrusion detection and prevention systems can integrate with global threat intelligence feeds, which will allow these systems to stay up to date on the latest threats and vulnerabilities. This integration helps improve a system's ability to detect and prevent emerging threats.

Automated Incident Response

Intrusion detection and prevention systems allow computer systems to automate the response to detected threats, ensuring that defensive actions can be taken promptly to decrease the impact of any interference. These automated responses might include:

• Isolating the impacted system
• Stopping traffic from malicious IP addresses
• Informing security personnel

Encrypted Traffic Analysis

As encryption becomes more widely used, a system must be able to analyze encrypted data without decrypting it.

Challenges of Implementing an Intrusion Detection System or an Intrusion Prevention System

While an intrusion detection system or an intrusion prevention system is vitally important to protecting data and an organization's internal networks, there are various challenges associated with their implementation:

• High volume of alerts: Intrusion detection and prevention systems tend to cause a substantial number of alerts, most of which may be false positives. Monitoring and correctly replying to these alerts can be resource-intensive.
• Resource intensity: Using and sustaining intrusion detection and prevention systems involves considerable resources. For instance, it's not only hardware and software, but also skilled security teams who are familiar with an organization's internal networks.
• Encrypted traffic: Ensuring threats are real and remain encrypted is a complex challenge for security teams.
• Integration with existing systems: Integration with other systems and infrastructure can be difficult.

Future IDPS Directions and Innovations

The field of intrusion detection and prevention systems is swiftly changing, and innovations are just around the corner:

• Advanced threat detection algorithms: Researchers are continuously working to create more elaborate algorithms that can detect a broader range of threats with higher accuracy and improve network security.
• Improved anomaly detection: Developments in anomaly detection will key in on decreasing false positives while accurately distinguishing legitimate threats, improving the response efficiency of security teams.
• Quantum computing: Quantum computing can revolutionize intrusion detection and prevention systems by supporting faster and more complicated calculations, which can considerably enhance threat detection and prevention capabilities.
• Greater automation: Future versions of intrusion detection and prevention systems will most likely include higher levels of automation, decreasing the need for human involvement and allowing security personnel to focus on more tactical responsibilities.

Intrusion detection and prevention systems are essential tools in the arsenal of homeland security: they provide detailed capabilities to prevent, detect, and respond to a wide range of cyber threats. As technology progresses, intrusion detection and prevention systems will continue to advance, offering even more vigorous safeguards to defend national security interests.

Cybersecurity Degrees at American Military University

If you’re an adult learner seeking to learn more about cybersecurity, computer and network security, and information security, American Military University (AMU) offers several degrees:

• An online Associate of Science in Cybersecurity
• An online Bachelor of Science in Cybersecurity
• An online Master of Science in Cybersecurity Studies

Taught by AMU’s expert instructors, these degree programs include courses in intermediate computer systems, hardening operating systems, and red and blue team security. Other courses include cyber warfare, securing databases, security risk management, and cyber intelligence.

For more information, visit AMU’s information technology program page.