Network intrusion detection based on DNA spatial information
Tianhao Hou, Hongyan Xing, Xinyi Liang, Xin Su, Zenghui Wang · Computer Networks · 2022
There is an ever-increasing risk of illegal access-induced Network Intrusion (NI), which calls for prompt detection of illegal network behavior through profound Network Traffic (NT) analyses. However, current intrusion detection methods are limited in accuracy due to insufficient data standardization . This paper puts forward a deoxyribonucleic acid (DNA)-Spatial Information (SI) method to overcome these limitations. A DNA encoding model is formed, which defines a mapping relationship between NT attributes and nucleobases to reconstruct NT samples expressed as DNA sequences. Then, a feature extraction algorithm is constructed that deduces a Spatial Information Feature Matrix (SIFM) to represent sequence statistical features. A Random Forest (RF) algorithm is adopted as a matching process to determine NI behaviors considering the detection efficiency. Following experiments evaluate its method performance on two datasets, NSL-KDD and UNSW-NB15. Results demonstrate that DNA-SI obtains better results than state-of-the-art works, where the accuracy, F1-score, recall, far are 95.75%, 94.41%, 94.12%, 3.26% and 92.30%, 92.78%, 89.82%, 4.66% respectively. The fact that it is insusceptible to minority intrusion samples is another point worth attention. In sum, this quick and accurate network intrusion detection points to a new orientation for safeguarding network security .