Improving Intrusion Detection with Visual Insights

Intrusion detection systems (IDS) are like the neighborhood watch of the digital world. They keep an eye on what’s happening on networks and computers, checking for any signs of trouble or attacks from cybercriminals. Think of it as having a security guard who makes sure everything is running smoothly and no one is trying to break in. They look out for various threats, such as denial of service attacks (where the system is overwhelmed with requests), spoofing (where someone pretends to be someone else), and others that could cause harm.

But here’s the kicker: even the best security guards can make mistakes. In the world of IDS, these mistakes show up as False Positives (FP) and False Negatives (FN). A false positive is when the system mistakenly thinks something is a threat when it’s not. It’s like thinking your friendly neighbor is a burglar just because he’s wearing a hoodie. On the flip side, a false negative is when the system misses a real threat. Imagine a thief sneaking past the security guard because they blended in too well.

#The Problem with Misclassifications

One of the toughest challenges with using Machine Learning (ML) and deep learning (DL) models for intrusion detection is these misclassifications. When an IDS gets something wrong, it makes the job of human analysts much harder. They need to make decisions based on the information provided, and if that information isn’t accurate, it could lead to serious consequences.

In this context, our goal is to help analysts easily spot the false positives and false negatives. We do this using a method called explainable artificial intelligence (XAI). With XAI, we make it easier to see why an IDS made a certain prediction. By using visual tools, such as SHAP plots, we can illustrate what features contributed to the system's decision.

#How We Approach the Solution

We use several Datasets of network traffic in our work. These datasets include a mix of benign (safe) traffic and attack traffic. To make sense of everything, we focus on the binary classification scenario where traffic is labeled as either 'benign' or 'attack'.

1. Data Collection and Preparation: First, we gather data from previous attacks and normal traffic. This data is cleaned and organized to ensure it’s ready for analysis. We deal with imbalances in data because often there are way more benign instances than attacks. We might apply techniques like oversampling (adding more attack examples) or undersampling (removing some benign examples) to balance everything out.

2. Training the Models: After preparation, we train our machine learning models. We use different tree-based classifiers like Decision Trees, XGBoost, and Random Forests to classify the traffic. The models learn from the data, aiming to accurately predict whether a given traffic instance is benign or an attack.

3. Using SHAP for Insights: Once our models are trained, we apply SHAP to get insights into how they make decisions. SHAP uses concepts from cooperative game theory to explain the contribution of each feature to the model's predictions. This helps analysts understand why a certain prediction was made, making the decision process easier.

#Visualizing the Analysis

Imagine you’re a security guard checking a suspicious person. Instead of just relying on your gut feeling, you have a detailed report showing how they act in various situations. That’s what SHAP plots do-they provide insights into the model’s predictions and help establish trust.

Here's how it works:

• Generating SHAP Plots: We create SHAP plots for true positives (correctly identified attacks), true negatives (correctly identified benign traffic), false positives, and false negatives. These plots allow us to compare feature contributions visually.

• Overlapping SHAP Plots: The clever part comes when we overlap these plots. For example, if we have an instance that the model thinks is an attack (a positive prediction), we can compare its features with those from true-positive and false-positive groups. If it looks more like the false-positive group, we know it’s likely a mistake.

#The Importance of Raw Probabilities

Besides using SHAP plots, we also consider the raw probability of our predictions. This is like having a hunch about the likelihood of someone being a burglar based on their actions. A high probability might mean the analyst has more confidence in the prediction, while a lower probability could raise some eyebrows.

By evaluating the overlapping plots and raw probabilities, analysts can decide if a prediction is trustworthy. If everything points towards a false positive, they can act accordingly and treat that instance as benign.

#Case Studies to Test the Methodology

We conducted case studies using different publicly available datasets to show how our method works in real-life scenarios. Each dataset presented its own challenges, but the aim remained the same: to accurately identify false positives and false negatives.

1. CIC-IoT-2023 Dataset: This dataset is a goldmine for testing as it’s filled with instances of attacks and benign traffic. We noticed that a significant majority of the instances were attacks, making it essential to balance the data before analysis. Once everything was balanced, we applied our methodology and analyzed the results.

2. NF-UQ-NIDS-v2 Dataset: This dataset had a variety of network-based anomalies. By applying our method, we saw a clear picture of how well the model performed in differentiating between benign and attack traffic. The visual plots were instrumental in helping analysts understand the model’s predictions.

3. HIKARI-2021 Dataset: This dataset contained both benign and attack instances. We applied our method and found that overlapping plots illuminated the distinctions between false positives and false negatives. The clarity these visualizations brought was remarkable.

#Evaluating the Effectiveness of the Method

After running our experiments, we evaluated the outcomes based on how well the analysts could identify false positives and false negatives accurately. We introduced a few random instances into the mix and had analysts work with them using the SHAP plots we generated.

The results were encouraging. Many analysts successfully identified false positives and false negatives based on the visual cues from the plots. They made informed decisions that helped reduce the overall misclassification rates.

#Limitations of the Study

While we found our method effective, it’s not without its limitations. For starters, we focused on tree-based models and didn’t explore deep learning options, which might have added another layer of analysis.

Also, even with our systematic approach, analysts still need to interpret the SHAP plots. This reliance on human evaluation can sometimes lead to mistakes. We may not have considered complex scenarios of multi-class classification fully, leaving room for future investigations.

Lastly, our model needs to be periodically updated. If it doesn’t adapt to changing patterns in data, the decisions made based solely on historical information could lead to misclassifications.

#Conclusion: A Step Toward Better Decision-Making

Ultimately, our work showcases how visual analysis combined with explainable AI can significantly improve decision-making in intrusion detection systems. By using SHAP plots, we provided analysts with tools to dissect the model’s predictions, enabling them to navigate through the complexities of false positives and false negatives more confidently.

As technology continues to evolve, so too will the threats we face in the digital landscape. By strengthening our intrusion detection systems today, we pave the way for a more secure tomorrow.