## 4 Evaluation
### Experiments and Results
#### 4.4.1 Experiment 1: Detecting Mutants of Existing Attacks  
The objective of this experiment was to evaluate our approach in detecting mutants of existing attacks. Malicious users develop mutants of existing attacks to evade detection, as conventional supervised learning models detect such mutants poorly, especially when there is significant variation with the known existing attack. To accomplish this, we extracted categories of attacks, which we believed to comprise variants of one another in both the NSL-KDD and CIC-2017 IDS datasets. Tables 1 and 2 depict the compositions of such attack mutants. To evaluate our approach in detecting a particular mutant, we utilized all the other mutants of the attack as a data source for the meta-training stage. For instance, if the attack mutant we wanted to detect was DoS hulk, then our meta-training data source will comprise all the other DoS subclasses (golden eye, slow loris, slow http test). The DoS hulk would then serve as the data source for the meta-testing stage. We then trained our feature extractor using the meta-training set. Thereafter, in the few-shot detection stage, we selected 10 samples of the meta-testing set and trained our classifier on top of our feature extractor. We applied the same logic to all attacks in both the NSL-KDD and CIC-IDS2017 datasets. However, we conducted this experiment on classes with sufficient numbers of samples. Therefore, on the NSL-KDD set we were able to perform the experiment on DoS and probe classes. While on the CIC-IDS2017 dataset, only the DoS class has a sufficient number of samples. Figures 1-3 display the results of our experiment on both the CIC-IDS2017 and NSL-KDD datasets. As can be seen from the results, our model achieves the highest detection rate of 99.9% on DoS attack mutant Neptune (Figure 1), followed by 90.0% and 80.0% on tear drop and smurf attack, respectively. These are good results, considering that we employed few-shots examples to train our classifier. However, our model achieves a DR of 0.0% on DoS attack mutant back. In fact, our model scores 0.0% on all the other performance metrics (accuracy and precision). This indicates that the DoS attack mutant back has significant variations with other DoS attack subclasses. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Architectural Details** & \\ \hline \multirow{4}{*}{Encoder} & Layer 1 & Dense, output: 128, activation: Relu \\  & Layer 2 & Dense, output: 64, activation: Relu \\  & Layer 3 & Dense, output: 32, activation: Relu \\  & Layer 4 & Dense, output: 8, activation: Relu \\ \hline \multirow{4}{*}{Decoder} & Layer 1 & Dense, output: 32, activation: Relu \\  & Layer 2 & Dense, output: 32, activation: Relu \\  & Layer 3 & Dense, output: No, of features, activation: sigmoid \\ \hline Classifier & Layer 1 & Dense, output:1, activation: sigmoid \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Architectural details of our models. Figure 1: Results of few-shot detection of DoS attack mutant for NSL-KDD dataset. Figure 2: Result of few-shot detection of probe attack mutants for NSL-KDD dataset. For the probe attack mutants (Figure 2), our model also performs well, achieving the highest DR score of 99.8% on the port sweep attack, and a DR score of 99.6%, 80.0%, and 70.7% for the port sweep, Nmap and Satan, respectively. All the attack mutants achieved a good DR, which indicated that the attack mutants had many attributes in common, which our feature extractor model was able to discover. Similarly, with the CIC-IDS2017 dataset, our model's performance was good. We achieved the highest DR score of 99.1% on DoS http test attack (Figure 3), and DR scores of 93.5%, 86.7%, and 80.0% on DoS golden eye, DoS slow loris and DoS hulk attacks, respectively. The results follow a similar trend as in the NSL-KDD dataset. We also compared our model's DR with baseline models, and we employed a combination of classical and deep learning algorithms as our baselines.