Comparison of shallow and deep neural networks for network intrusion detection
Daniel E. Kim, Mikhail I. Gofman · 2018
The increasing complexity and malice of modern computer and network attacks drives a need and search for more adaptive and smarter intrusion detection methods. Neural networks can provide a useful, self-learning approach to threat detection for network intrusion. After testing a variety of simple shallow and deep neural networks on the well-known NSL-KDD dataset comprised of network traffic capture containing 148,000 observations and 41 features with 22 specific attacks, we confirm the findings of previous researchers [15] that shallow neural networks are better suited for network intrusion detection than deep neural networks. Shallow networks were able to more accurately classify network data and produced lower error rates compared to deep networks.