Towards Intelligent Attack Detection Using DNA Computing

Zengri Zeng, Baokang Zhao, Han Chieh Chao*, Ilsun You, Kuo Hui Yeh, Weizhi Meng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In recent years, frequent network attacks have seriously threatened the interests and security of humankind. To address this threat, many detection methods have been studied, some of which have achieved good results. However, with the development of network interconnection technology, massive amounts of network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulty collecting samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of existing detection methods, and existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of a detection method in terms of overall accuracy, feature subset optimization, and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.

Original languageEnglish
Article number126
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume19
Issue number3
DOIs
StatePublished - 24 Feb 2023

Keywords

  • attack detection
  • DNA computing
  • Imbalance
  • multiclassification
  • nondominated ranking

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