Understanding Network Intrusion Detection and Why Explainability Matters
Why Network Intrusion Detection Is More Important Than Ever
Picture this: You’re running a company, managing sensitive data, keeping your systems up and running with the help of network intrusion detection systems. But behind the scenes, cybercriminals are constantly looking for ways to break in, steal information, or disrupt operations. Every day, hackers grow smarter, developing sneaky tactics to bypass security, making robust network intrusion detection
That’s where network intrusion detection comes into play. It’s like having a digital security guard that monitors your network, flagging suspicious activity before serious damage happens. Traditional systems rely on rules to recognize known threats, but attackers evolve quickly—meaning old defenses often struggle to keep up.
Enter AI-powered intrusion detection. AI can sift through mountains of network data at lightning speed, spotting threats that humans might miss. But there’s a catch—these AI models work like black boxes. They generate results, but security analysts have no idea how or why they’re making certain decisions. Without transparency, it’s hard to trust them fully.
The Problem with AI-Driven IDS
AI-powered network intrusion detection systems (IDSs) offer impressive accuracy, but their biggest weakness is interpretability. Here’s why that’s a problem:
- Security teams can’t verify AI decisions – If an AI model flags a harmless transaction as an attack, analysts waste time chasing false alarms. Worse, if it fails to detect an actual intrusion, the consequences could be disastrous.
- Uncertainty leads to hesitation – Without understanding why an AI made a decision, teams might hesitate to act, delaying response times and increasing risks.
- Regulatory compliance and audits suffer – Many industries require clear documentation of security measures. If companies can’t explain their AI model’s decisions, they risk non-compliance.
Clearly, AI alone isn’t enough. We need a way to understand how these systems work, which is where Explainable Artificial Intelligence (XAI) steps in.
Why Modern Cybersecurity Needs Explainable AI
Hackers Are Getting Smarter—Are We Keeping Up?
Gone are the days when simple firewalls and antivirus software were enough. Cyber threats now range from automated botnet attacks to denial-of-service (DoS) floods, ransomware, and advanced phishing scams. Traditional IDSs struggle to catch newer, more complex threats.
That’s why AI-driven network intrusion detection has become critical. AI learns from past attacks, making it much faster and more adaptable than outdated rule-based systems. However, these AI models are only effective if we can trust their predictions.
AI in IDS: Power vs. Transparency
When implementing AI in network intrusion detection, organizations often face a dilemma:
- Accuracy vs. Interpretability – Deep learning and random forest models can detect threats with high precision, but their internal processes are nearly impossible to decode.
- False Alarms vs. Missed Attacks – AI can flag legitimate traffic as harmful, overwhelming security teams. On the flip side, opaque AI models might overlook critical threats.
To bridge the gap between accuracy and interpretability, the XAI-IDS framework was developed. It combines AI-driven detection with explainability tools like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations), making AI-powered IDS systems more transparent and trustworthy.
Bringing Clarity to Network Intrusion Detection with XAI
Imagine security analysts investigating a flagged intrusion. Instead of blindly trusting an AI model’s verdict, they can now see exactly why it made a certain decision.
With XAI techniques, they get:
Detailed feature explanations – Understanding which network parameters influenced the AI’s decision. Global insights – Recognizing common patterns in threats across different datasets. Local explanations – Pinpointing specific reasons an AI model classified an event as an attack.
By combining AI efficiency with human-friendly explainability, XAI-IDS transforms network security—ensuring analysts can trust AI-powered decisions and respond more confidently to cyber threats.
Making AI-Powered Network Intrusion Detection Understandable
What is Explainable AI (XAI)?
You’ve probably heard that AI is being used to detect cyber threats in networks. AI models can analyze massive amounts of data, spotting malicious activity way faster than humans ever could. Sounds great, right? The problem is that these AI systems often work like black boxes. They tell you something is wrong, but they don’t explain why.
That’s where Explainable AI (XAI) comes in. It makes AI decisions clear and understandable. Instead of blindly trusting that an AI model knows what it’s doing, security analysts can actually see how and why it flagged a network event as suspicious. This is huge for cybersecurity because when analysts understand the reasoning behind AI alerts, they can react faster and more accurately.
Why XAI Matters in Network Intrusion Detection
Imagine an AI system detecting an attack. It throws up an alert, but the security team has no way of knowing if it’s a false alarm or a real threat. Should they take action or ignore it?
XAI solves this by providing explanations that break down AI decisions. It helps security analysts:
- Trust AI predictions instead of second-guessing them.
- Identify false positives that could waste time.
- See which features influenced a decision so they can refine detection models over time.
When an intrusion detection system has transparency, security teams can work smarter, not harder.
How XAI Explains AI Decisions
XAI tools like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) give security analysts insights into AI-driven alerts.
- SHAP assigns importance values to different features in an AI model. It helps security teams understand which network attributes contributed to an alert, giving both big-picture insights and case-by-case breakdowns.
- LIME creates a simplified version of the AI model to explain individual decisions. It helps analysts pinpoint exactly why a specific event was classified as an attack.
These tools remove the guesswork, making AI-powered network intrusion detection easier to trust and act on.
How the XAI-IDS Framework Works
The XAI-IDS framework is designed to improve transparency in network intrusion detection. It combines AI-driven detection with explainability tools, ensuring security professionals understand every decision AI makes.
Key Components of the XAI-IDS Framework
Data Preprocessing and Feature Selection
Before AI models can detect threats, they need clean, well-organized data. The framework uses three major cybersecurity datasets—CICIDS-2017, NSL-KDD, and RoEduNet-SIMARGL2021—to train AI models effectively.
To ensure the models focus on the most relevant information, XAI-driven feature selection identifies the best network attributes for detecting intrusions.
Benchmarking AI Models
The framework tests seven AI models to find out which ones work best for intrusion detection:
- Random Forest (RF)
- Deep Neural Networks (DNN)
- Support Vector Machine (SVM)
- AdaBoost (ADA)
- K-Nearest Neighbor (KNN)
- Multi-Layer Perceptron (MLP)
- Light Gradient Boosting Machine (LightGBM)
Each model is measured not just for accuracy, but also for interpretability so that security analysts can actually understand what’s going on under the hood.
Local and Global Explanations Using XAI
By using SHAP and LIME, the framework provides two levels of explanations:
- Global explanations highlight overall patterns in AI decision-making. Security analysts get a bird’s-eye view of which features consistently impact intrusion detection.
- Local explanations focus on individual alerts. Analysts can zoom in on specific network events to see why AI flagged them as threats.
Feature Extraction for Different Attack Types
Different cyberattacks behave differently, so the framework identifies which network features matter most for each type of attack, including:
- Denial of Service (DoS) attacks
- Port scanning attempts
- Brute-force login attacks
- Remote-to-Local (R2L) intrusions
- User-to-Root (U2R) privilege escalation attacks
By understanding which features are most important for different threats, analysts can fine-tune AI models to detect intrusions more effectively.
The Key Features AI Uses to Detect Intrusions
Which Features Matter Most in Intrusion Detection?
AI models don’t just randomly flag network activity. They analyze specific attributes to determine whether an event looks suspicious. Here are some of the most important features that influence network intrusion detection decisions:
| Feature | Why It’s Important |
|---|---|
| TCP_WIN_SCALE_IN | Detects irregularities in TCP window scaling, a technique used in scanning attacks |
| Destination Port | Identifies attack patterns based on port usage |
| Packet Length Std | Highlights unusual packet size variations, a common attack indicator |
| Logged_in | Helps detect unauthorized access attempts |
| Flow Duration | Measures abnormal data transmission lengths that could signal an intrusion |
These features give analysts insight into how AI makes its decisions, improving both accuracy and transparency in detecting threats.
Attack-Specific Features That Help Spot Different Threats
Different types of cyberattacks impact network traffic in unique ways. By analyzing attack-specific features, security teams can improve detection accuracy.
| Attack Type | Top Features That Matter Most |
|---|---|
| Port Scanning | Destination Port, TCP_WIN_SCALE_IN |
| DoS Attack | Flow Duration, Packet Length Std |
| Brute Force | Logged_in, Password Attempt Tracking |
| Infiltration | Source IP Analysis, Suspicious Behavior Detection |
Knowing which features correspond to which attack types helps analysts fine-tune AI models for better threat detection.
Why XAI-Based Feature Selection Is Better Than Traditional Methods
Traditional network intrusion detection systems rely on static rules or predefined signatures to detect attacks. But cybercriminals are always evolving, tweaking their methods to bypass rigid security defenses.
XAI-powered feature selection is dynamic. Instead of sticking to a fixed set of rules, AI models continuously refine which features matter most based on new attack trends. This leads to:
- More accurate intrusion detection, even for emerging threats.
- Fewer false positives, so security teams aren’t overwhelmed by unnecessary alerts.
- Better interpretability, helping analysts trust AI-generated predictions.
By integrating XAI into network intrusion detection, organizations can build smarter security systems that stay ahead of cybercriminals while giving security teams the clarity they need to make informed decisions.
How Well Does XAI-IDS Actually Perform?
How Do Different AI Models Stack Up?
Network intrusion detection is all about finding and stopping threats before they wreak havoc. But an IDS is only as good as its ability to detect real threats accurately and help analysts understand what’s happening. That’s where accuracy and transparency come into play.
In testing the XAI-IDS framework, researchers compared seven different AI models to see how well they identified network intrusions across three major cybersecurity datasets—CICIDS-2017, NSL-KDD, and RoEduNet-SIMARGL2021. These datasets contain real-world attacks like DoS, Port Scanning, Brute Force, and Remote-to-Local intrusions.
Some models performed impressively, offering both strong detection rates and clear explanations, while others struggled with interpretability. Here’s how the AI models ranked in accuracy across different datasets:
| AI Model | Accuracy (RoEduNet-SIMARGL2021) | Accuracy (CICIDS-2017) | Accuracy (NSL-KDD) |
|---|---|---|---|
| Random Forest (RF) | 99% | 98% | 88% |
| Deep Neural Network (DNN) | 99% | 95% | 86% |
| Support Vector Machine (SVM) | 99% | 97% | 86% |
| K-Nearest Neighbor (KNN) | 99% | 99% | 81% |
| Multi-Layer Perceptron (MLP) | 99% | 97% | 84% |
Random Forest, DNN, and MLP stood out as top performers, balancing high detection accuracy with explainability. KNN and SVM were reliable in detection but had issues providing clear justifications for their decisions, making them less helpful for security analysts who need actionable insights.
How Does XAI Help Security Analysts?
A security analyst’s job is tough. They deal with hundreds—sometimes thousands—of alerts daily. If they can’t understand why an AI model flagged something as suspicious, they’re left guessing. That’s a problem because reacting to false positives wastes valuable time, while missing an actual attack can be disastrous.
That’s why XAI-powered explanations make a real difference.
With SHAP and LIME, analysts can break down why an AI model detected a threat. For example, if an intrusion detection system marks network traffic as part of a Denial of Service (DoS) attack, SHAP can show which factors contributed—like abnormally high packet transmission rates and flow duration. LIME zooms in on individual cases, helping analysts validate threats with ease.
These tools allow security teams to spot real threats faster instead of wasting time on false alerts, adjust IDS settings to make AI models more accurate over time, and improve cybersecurity strategies using insights from explainable AI.
Instead of working in the dark, security teams now have clear, interpretable reasons for every AI-driven alert.
Can XAI Work in Real-Time?
Accuracy and transparency are great, but none of it matters if an IDS is too slow to respond to real-time threats. Cyberattacks happen in seconds, and intrusion detection systems must keep up.
The XAI-IDS framework tested each AI model for training time and prediction speed to see how well they performed under real-world conditions.
| AI Model | Training Time (RoEduNet-SIMARGL2021) | Prediction Time (CICIDS-2017) | Prediction Time (NSL-KDD) |
|---|---|---|---|
| Random Forest (RF) | 11.57 min | 0.19 min | 0.73 min |
| Deep Neural Network (DNN) | 5.99 min | 0.58 min | 1.28 min |
| Support Vector Machine (SVM) | 828.51 min | 0.20 min | 6.48 min |
| K-Nearest Neighbor (KNN) | 1.24 min | 0.09 min | 0.01 min |
Random Forest and Deep Neural Networks proved to be the best choices, offering both strong accuracy and fast response times. SVM was powerful but far too slow for real-time applications, making it less practical for cybersecurity teams that need immediate threat detection.
Where is XAI-IDS Headed Next?
How Security Teams Can Make the Most of XAI
XAI-powered network intrusion detection isn’t just about making AI easier to understand—it’s about improving cybersecurity from the ground up. Security teams can use these insights to:
- Build smarter IDS systems that adapt to new threats faster
- Reduce false positives, saving time and improving efficiency
- Strengthen threat response, so analysts can act without hesitation
By combining AI detection with human oversight, organizations create cybersecurity strategies that work in real-world environments—not just in research labs.
What’s Next in Intrusion Detection?
Intrusion detection systems will continue evolving. Future advancements in XAI-IDS will focus on:
- Hybrid AI models that combine deep learning with explainability for even better accuracy
- Real-time attack detection using adaptive AI-powered IDS systems
- Automated security tuning, where IDS models refine themselves based on live network behavior
As AI continues to reshape cybersecurity, XAI will ensure intrusion detection remains trustworthy, understandable, and effective.
Why Human Expertise Still Matters
No matter how advanced AI intrusion detection becomes, human expertise is irreplaceable. Security teams must:
- Validate AI alerts before taking action
- Refine IDS models based on real-world conditions
- Use AI as a tool, not a replacement, ensuring balanced security strategies
The combination of AI-driven detection and human oversight is the future of network intrusion detection.
Final Thoughts on XAI-IDS
Why AI Needs Transparency
AI-powered intrusion detection systems are a game-changer for cybersecurity. They offer faster threat detection, better adaptability, and improved decision-making. But they need explainability to be truly effective. Without transparency, security analysts struggle to trust AI-driven alerts, leading to hesitation, inefficiencies, and missed threats.
XAI bridges that gap, providing interpretable explanations that make AI decisions clear. Security teams can trust AI predictions, improve cybersecurity strategies, and react to attacks with confidence.
Encouraging Organizations to Adopt XAI in IDS Frameworks
Now is the time for organizations to integrate XAI-powered IDS frameworks into their security infrastructure. Businesses can deploy AI models with built-in transparency, train analysts to use XAI explanations for smarter decision-making, and continuously refine AI models to improve detection accuracy.
Cyber threats are evolving, and XAI-driven intrusion detection is the best way to keep networks safe.
Reference: Arreche, O.; Guntur, T.; Abdallah, M. “XAI-IDS: Toward Proposing an Explainable Artificial Intelligence Framework for Enhancing Network Intrusion Detection Systems.” Applied Sciences, 2024, 14, 4170. DOI: 10.3390/app14104170.
Creative Commons Attribution License (CC BY 4.0): This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which allows unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.