skip to main content
research-article

Towards Intelligent Attack Detection Using DNA Computing

Authors Info & Claims
Published:24 February 2023Publication History
Skip Abstract Section

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%.

REFERENCES

  1. [1] Mittal Meenakshi, Kumar Krishan, and Behal Sunny. 2022. Deep learning approaches for detecting DDoS attacks: A systematic review. Soft Computing. 137.Google ScholarGoogle Scholar
  2. [2] Zhou Jinyuan et al. 2022. Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors. Magnetic Resonance in Medicine 88 (2022), 546--574.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Cerrudo Cesar. 2015. An emerging US (and world) threat: Cities wide open to cyber attacks. Securing Smart Cities 17 (2017), 137151.Google ScholarGoogle Scholar
  4. [4] Smith D. C.. 2015. Cybersecurity in the energy sector: Are we really prepared. Journal of Energy & Natural Resources Law 39, 3 (2015), 265270.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Kilincer Ilhan Firat, Ertam Fatih, and Sengur Abdulkadir. 2021. Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks 188 (2021), 107840.Google ScholarGoogle Scholar
  6. [6] Almaiah Mohammed Amin. 2021. Classification of Cyber Security Threats on Mobile Devices and Applications. Artificial Intelligence and Blockchain for Future Cybersecurity Applications. Springer, Cham, 107123.Google ScholarGoogle Scholar
  7. [7] Zeng Zengri, Peng Wei, and Zhao Baokang. 2021. Improving the accuracy of network intrusion detection with causal machine learning. Security and Communication Networks, Vol. 2021, Article ID 8986243, 18 pages.Google ScholarGoogle Scholar
  8. [8] Cai Zhong Min Hong , Xiao Guan, et al. 2003. A new approach to intrusion detection based on rough set theory. Chinese Journal of Computers.Google ScholarGoogle Scholar
  9. [9] Injadat M. N., Moubayed A., and Nassif A. B.. 2020. Multi-stage optimized machine learning framework for network intrusion detection. IEEE Transactions on Network and Service Management 18, 2 (2020).Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Jianping X., Chun L., Jing Z., et al. 2021. A survey on network intrusion detection based on deep learning. Frontiers of Data and Computing 3, 3 (2021), 5974.Google ScholarGoogle Scholar
  11. [11] Bedi P., Gupta N., and Jindal V.. 2021. I-SiamIDS: An improved Siam-IDS for handling class imbalance in network-based intrusion detection systems. Applied Intelligence 51, 2 (2021), 11331151.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Soon Hui Fern, Amiza Amir, and Saidatul Norlyana Azemi. 2022. Multi-class imbalanced classification problems in network attack detections. In Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering. Springer, Singapore, 1057--1069.Google ScholarGoogle Scholar
  13. [13] Almseidin Mohammad, Al-Sawwa Jamil, and Alkasassbeh Mouhammd. 2022. Generating a benchmark cyber multi-step attacks dataset for intrusion detection. Journal of Intelligent & Fuzzy Systems. Preprint, 115.Google ScholarGoogle Scholar
  14. [14] Moizuddin M. D. and Jose M. V.. 2022. A bio-inspired hybrid deep learning model for network intrusion detection. Knowledge-Based Systems, 238 (2022), 107894.Google ScholarGoogle Scholar
  15. [15] Prasad M., Tripathi S., and Dahal K.. 2020. An efficient feature selection-based Bayesian and Rough set approach for intrusion detection. Applied Soft Computing 87, Article ID105980.Google ScholarGoogle Scholar
  16. [16] Azayeri N. and Sajedi H.. 2020. DNAVS: An algorithm based on DNA-computing and vortex search algorithm for task scheduling problem. Evolutionary Intelligence 14, 4 (2020), 17631773.Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Bollella P. and Katz E.. 2020. DNA computing-origination, motivation and goals. International Journal of Unconventional Computing 15, 3 (2020).Google ScholarGoogle Scholar
  18. [18] Zhou J., Zhao X., Zhang X., et al. 2020. Task allocation for multi-agent systems based on distributed many-objective evolutionary algorithm and greedy algorithm. IEEE Access 8 (2020), 1930619318.Google ScholarGoogle Scholar
  19. [19] Jing X., Jing-Jing L., and Xi-Xi H.. 2018. An improved MOEA/D based on reference distance for software project portfolio optimization. Complexity (2018), 116.Google ScholarGoogle Scholar
  20. [20] Sharafaldin I., Lashkari A. H., and Ghorbani A. A.. 2028. Toward generating a new intrusion detection dataset and intrusion traffic characterization. International Conference on Information Systems Security and Privacy. 1 (2028), 108116.Google ScholarGoogle Scholar
  21. [21] Chen S., Lang B., and Liu H.. 2021. DNS covert channel detection method using the LSTM model. Computers & Security. 104 (2021), 102095.Google ScholarGoogle Scholar
  22. [22] Mohamed Amine Ferrag, et al. 2020. Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications 50 (2020), 102419.Google ScholarGoogle Scholar
  23. [23] Zhang Jianwu et al. 2020. Model of the intrusion detection system based on the integration of spatial-temporal features. Computers & Security. 89, 101681.Google ScholarGoogle Scholar
  24. [24] Yan B. and Han G.. 2028. LA-GRU: Building combined intrusion detection model based on imbalanced learning and gated recurrent unit neural network. Security and Communication Networks (2018), 113.Google ScholarGoogle Scholar
  25. [25] Jadwal Pankaj Kumar et al. Improved resampling algorithm through a modified oversampling approach based on spectral clustering and SMOTE. In Microsystem Technologies. 19.Google ScholarGoogle Scholar
  26. [26] Liu L., Wang P., and Lin J.. 2020. Intrusion detection of imbalanced network traffic based on machine learning and deep learning. IEEE Access 99 (2020), 11.Google ScholarGoogle Scholar
  27. [27] Abdulhammed R., Faezipour M., and Abuzneid A.. 2018. Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic. IEEE Sensors Letters 3, 1 (2018), 14.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Bedi P., Gupta N., and Jindal V.. 2020. Siam-IDS: Handling class imbalance problem in intrusion detection systems using Siamese neural network. Procedia Computer Science 171 (2020), 780789.Google ScholarGoogle Scholar
  29. [29] Paun G., Rozenberg G., and Salomaa A.. 2005. DNA Computing: New Computing Paradigms. Springer Science & Business Media.Google ScholarGoogle Scholar
  30. [30] Ding Y. S., Ren L. H., and Shao S. H.. 2001. DNA Computing and Soft Computing. Acta Simulata Systematica Sinica.Google ScholarGoogle Scholar
  31. [31] Zang W., Ren L., Zhang W., et al. 2018. A cloud model-based DNA genetic algorithm for numerical optimization problems. Future Generation Computer Systems 81 (2018), 465477.Google ScholarGoogle Scholar
  32. [32] Jatoth C., Gangadharan G. R., and Buyya R.. 2019. Optimal fitness aware cloud service composition using an adaptive genotypes evolution based genetic algorithm. Future Generation Computer Systems 94 (2019), 185198.Google ScholarGoogle Scholar
  33. [33] Shukla A., Pandey H. M., and Mehrotra D.. 2015. Comparative review of selection techniques in genetic algorithm. 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE). IEEE, 515519.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] L. Y. Chuang, C. H. Yang, and K. C. Wu, et al. 2011. A hybrid feature selection method for DNA microarray data. Computers in Biology and Medicine 41, 4 (2011), 228--237.Google ScholarGoogle Scholar
  35. [35] Deb K.. 2014. Multi-objective optimization. In Search Methodologies. Springer, Boston, MA, (2014), 403449.Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Deb Kalyanmoy and Jain Himanshu. 2014. An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18, 4 (2014), 577601.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Deb K., Pratap A., and Agarwal S. A.. 2002. Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182197.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. [38] Aguilar-Rivera A.. 2020. A GPU fully vectorized approach to accelerate performance of NSGA-2 based on stochastic non-domination sorting and grid-crowding. Applied Soft Computing 88 (2020), 106047.Google ScholarGoogle Scholar
  39. [39] Usman A. M., Yusof U. K., and Naim S.. 2020. Multi-objective wrapper-based feature selection using binary cuckoo optimisation algorithm: A comparison between NSGAII and NSGAIII. The International Conference on Emerging Applications and Technologies for Industry 4.0. Springer, Cham, 124136.Google ScholarGoogle Scholar
  40. [40] Yingying Zhu et al. 2017. An improved NSGA-III algorithm for feature selection used in intrusion detection. Knowledge-Based Systems 116 (2017), 74--85.Google ScholarGoogle Scholar
  41. [41] Adleman L. M.. 1994. Molecular computation of solutions to combinatorial problems. Science 266, 5187 (1994), 10211024.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Jianhua Xiao et al. 2009. A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding. Computers & Mathematics with Applications 57, 11--12 (2009), 1949--1958.Google ScholarGoogle Scholar
  43. [43] Bergstra J. and Bengio Y.. 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13, 1 (2012), 281305.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Das and Dennis J. E.. 1998. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM Journal on Optimization 8, 3 (1998), 631657.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Prada A., Gasparella A., and Baggio P. A.. 2019. Comparison of three evolutionary algorithms for the optimization of building design. Applied Mechanics and Materials 887 (2019), 140147.Google ScholarGoogle Scholar
  46. [46] Sharafaldin Iman et al. 2019. Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. 2019 International Carnahan Conference on Security Technology (ICCST’19). 18.Google ScholarGoogle Scholar
  47. [47] MontazeriShatoori Mohammadreza, Davidson Logan, and Kaur Gurdip. 2020. Detection of DoH tunnels using time-series classification of encrypted traffic. 2020. 5th IEEE Cyber Science and Technology Congress, Calgary, Alberta, Canada.Google ScholarGoogle Scholar
  48. [48] Ibrahim L. M., Basheer D. T., and Mahmod M. S. A.. 2013. Comparison study for intrusion database (KDD99, NSL-KDD) based on self-organization map (SOM) artificial neural network. Journal of Engineering Science and Technology 8, 1 (2013), 107119.Google ScholarGoogle Scholar
  49. [49] Systematic ensemble model selection approach for educational data mining. 2020. Knowledge-Based Systems 200 (2020), 105992.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Li J., Cheng K., Wang S., and Morstatter F.. 2018. Feature selection: A data perspective. ACM Computing Surveys 50, 6 (2018), 94.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Towards Intelligent Attack Detection Using DNA Computing

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 3s
      June 2023
      270 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3582887
      • Editor:
      • Abdulmotaleb El Saddik
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 24 February 2023
      • Online AM: 8 September 2022
      • Accepted: 24 August 2022
      • Revised: 30 July 2022
      • Received: 20 February 2022
      Published in tomm Volume 19, Issue 3s

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
    • Article Metrics

      • Downloads (Last 12 months)209
      • Downloads (Last 6 weeks)16

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text

    HTML Format

    View this article in HTML Format .

    View HTML Format
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!