Representation Learning (RL) plays a pivotal role in the success of many
problems including cyberattack detection. Most of the RL methods for
cyberattack detection are based on the latent vector of Auto-Encoder (AE)
models. An AE transforms raw data into a new latent representation that better
exposes the underlying characteristics of the input data. Thus, it is very
useful for identifying cyberattacks. However, due to the heterogeneity and
sophistication of cyberattacks, the representation of AEs is often
entangled/mixed resulting in the difficulty for downstream attack detection
models. To tackle this problem, we propose a novel mod called Twin Auto-Encoder
(TAE). TAE deterministically transforms the latent representation into a more
distinguishable representation namely the \textit{separable representation} and
the reconstructsuct the separable representation at the output. The output of
TAE called the \textit{reconstruction representation} is input to downstream
models to detect cyberattacks. We extensively evaluate the effectiveness of TAE
using a wide range of bench-marking datasets. Experiment results show the
superior accuracy of TAE over state-of-the-art RL models and well-known machine
learning algorithms. Moreover, TAE also outperforms state-of-the-art models on
some sophisticated and challenging attacks. We then investigate various
characteristics of TAE to further demonstrate its superiority.

表示学习对于包括网络攻击检测在内的许多问题的成功起到了关键作用。 大多数用于网络攻击检测的表示学习方法基于自动编码器（Auto-Encoder）模型的潜在向量。 为了解决这个问题，我们提出了一种新的模型，称为双自动编码器（Twin Auto-Encoder，TAE）。 TAE 确定性地将潜在表示转换为更可区分的表示，即分离表示，并在输出中重构该分离表示。我们广泛评估了 TAE 的有效性，并发现 TAE 在广泛的基准数据集上表现优于最先进的表示学习模型和著名的机器学习算法。 此外，TAE 在某些复杂和具有挑战性的攻击上也优于最先进的模型。 接下来，我们对 TAE 的各种特性进行了深入研究，以进一步证明其优越性。