## 4 Discussion  
The proposed unknown polymorphic pattern detection method introduces several novel elements. Firstly, we show how prior knowledge of rules, which generate polymorphic patterns, can be incorporated into a network and preserved during training. Another contribution concerns the proposal of measuring a distance between pattern-describing rules by evaluating Levenshtein distances between sequences generated by these rules. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\lozenge\) & **Motif No.** & **E-Value** & \(\mathbf{d_{1}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{d_{3}}\) \\ \hline False & 1. & \(1.4\times 10^{-56}\) & 3.65 & 2.43 & 2.54 \\ False & 2. & \(3.8\times 10^{-44}\) & 4.76 & 2.54 & 4.85 \\ False & 3. & \(8.4\times 10^{-38}\) & 2.87 & 5.43 & 4.03 \\ True & 1. & \(7.6\times 10^{-45}\) & 3.63 & 2.52 & 2.91 \\ True & 2. & \(4.7\times 10^{-34}\) & 3.45 & 1.93 & 4.72 \\ True & 3. & \(2.7\times 10^{-32}\) & 2.97 & 2.16 & 2.59 \\ \hline \hline \end{tabular} \(\lozenge\) Unknown regex present in data.  
\end{table} 
Table 4: DREME results. Three motifs with the lowest E-value are presented for each dataset. Columns \(d_{1}\), \(d_{2}\), and \(d_{3}\) represent distances from patterns considered in the study, with \(d_{3}\) being the unknown pattern. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backprop Modification** & \(\lozenge\) & \(\mathbf{d_{1}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{d_{3}}\) \\ \hline False & True & 3.43 & 3.46 & 2.91 \\ False & False & 3.40 & 3.43 & 2.87 \\ True & True & 3.52 & 3.49 & 2.91 \\ True & False & 3.44 & 3.45 & 2.94 \\ \hline \hline \end{tabular} \(\lozenge\) Unknown regex present in data.  
\end{table} 
Table 3: Averaged distances between regular expressions retrieved from filters and the unknown pattern. Columns \(d_{1}\) and \(d_{2}\) represent distances from FCMs with injected knowledge and \(d_{3}\) represents LCM, which is supposed to reconstruct the pattern. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\lozenge\) & **Motif No. ** & **E-Value** & \(\mathbf{d_{1}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{d_{3}}\) \\ \hline False & 1. & \(3.2\times 10^{-30}\) & 1.53 & 4.50 & 3.91 \\ False & 2. & \(3.5\times 10^{-27}\) & 1.76 & 2.93 & 3.34 \\ False & 3. & \(4.7\times 10^{-20}\) & 2.40 & 4.43 & 4.87 \\ True & 1. & \(3.2\times 10^{-30}\) & 3.43 & 3.75 & 3.91 The proposed network architecture is designed to enable the injection of complex knowledge. Furthermore, several constraints introduced to facilitate measurements (e.g., keeping unused parts of filters) can be removed for more general utilization of prior knowledge. It is also worth noting that the presented problem is complex and difficult to be solved by traditional approaches. As can be seen from the results, the proposed data processing pipeline built upon the introduced methodology can answer whether new, unknown patterns are present in the data. The proposed model is not yet capable of reconstructing the exact pattern. However, it learns a pattern more similar to the original unknown one that the fixed cascades present in the network. The introduced method's main contribution and advantage over existing solutions is the ability to utilize prior knowledge about complex polymorphic patterns. As can be seen from the results, the classical methods, although capable of motif finding, have two severe limitations. They cannot benefit from prior knowledge of regular expressions 1 and 2; so, they waste resources learning them on the same basis as in the case of the unknown pattern 3. Detection of another pattern can only be achieved by further analysis of motifs extracted from the data, which is additionally hindered by the inability to handle patterns' mutability; again, as can be seen from the comparison, classical methods find multiple motifs whereas the proposed methodology correctly discovers only a single mutable motif. Also, baseline methods are not capable of showing that a discovered pattern is unknown.