More Web Proxy on the site http://driver.im/

research-article

Towards Intelligent Attack Detection Using DNA Computing

Authors:

Han-Chieh Chao,

Weizhi MengAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 3s

Article No.: 126, Pages 1 - 27

https://doi.org/10.1145/3561057

Published: 24 February 2023 Publication History

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]

Meenakshi Mittal, Krishan Kumar, and Sunny Behal. 2022. Deep learning approaches for detecting DDoS attacks: A systematic review. Soft Computing. 1–37.

[2]

Jinyuan Zhou 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.

[3]

Cesar Cerrudo. 2015. An emerging US (and world) threat: Cities wide open to cyber attacks. Securing Smart Cities 17 (2017), 137–151.

[4]

D. C. Smith. 2015. Cybersecurity in the energy sector: Are we really prepared. Journal of Energy & Natural Resources Law 39, 3 (2015), 265–270.

[5]

Ilhan Firat Kilincer, Fatih Ertam, and Abdulkadir Sengur. 2021. Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks 188 (2021), 107840.

[6]

Mohammed Amin Almaiah. 2021. Classification of Cyber Security Threats on Mobile Devices and Applications. Artificial Intelligence and Blockchain for Future Cybersecurity Applications. Springer, Cham, 107–123.

[7]

Zengri Zeng, Wei Peng, and Baokang Zhao. 2021. Improving the accuracy of network intrusion detection with causal machine learning. Security and Communication Networks, Vol. 2021, Article ID 8986243, 18 pages.

[8]

Cai Zhong Min Hong, Guan Xiao, et al. 2003. A new approach to intrusion detection based on rough set theory. Chinese Journal of Computers.

[9]

M. N. Injadat, A. Moubayed, and A. B. Nassif. 2020. Multi-stage optimized machine learning framework for network intrusion detection. IEEE Transactions on Network and Service Management 18, 2 (2020).

[10]

X. Jianping, L. Chun, Z. Jing, et al. 2021. A survey on network intrusion detection based on deep learning. Frontiers of Data and Computing 3, 3 (2021), 59–74.

[11]

P. Bedi, N. Gupta, and V. Jindal. 2021. I-SiamIDS: An improved Siam-IDS for handling class imbalance in network-based intrusion detection systems. Applied Intelligence 51, 2 (2021), 1133–1151.

Digital Library

[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.

[13]

Mohammad Almseidin, Jamil Al-Sawwa, and Mouhammd Alkasassbeh. 2022. Generating a benchmark cyber multi-step attacks dataset for intrusion detection. Journal of Intelligent & Fuzzy Systems. Preprint, 1–15.

[14]

M. D. Moizuddin and M. V. Jose. 2022. A bio-inspired hybrid deep learning model for network intrusion detection. Knowledge-Based Systems, 238 (2022), 107894.

[15]

M. Prasad, S. Tripathi, and K. Dahal. 2020. An efficient feature selection-based Bayesian and Rough set approach for intrusion detection. Applied Soft Computing 87, Article ID105980.

[16]

N. Azayeri and H. Sajedi. 2020. DNAVS: An algorithm based on DNA-computing and vortex search algorithm for task scheduling problem. Evolutionary Intelligence 14, 4 (2020), 1763–1773.

[17]

P. Bollella and E. Katz. 2020. DNA computing-origination, motivation and goals. International Journal of Unconventional Computing 15, 3 (2020).

[18]

J. Zhou, X. Zhao, X. Zhang, et al. 2020. Task allocation for multi-agent systems based on distributed many-objective evolutionary algorithm and greedy algorithm. IEEE Access 8 (2020), 19306–19318.

[19]

X. Jing, L. Jing-Jing, and H. Xi-Xi. 2018. An improved MOEA/D based on reference distance for software project portfolio optimization. Complexity (2018), 1–16.

[20]

I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani. 2028. Toward generating a new intrusion detection dataset and intrusion traffic characterization. International Conference on Information Systems Security and Privacy. 1 (2028), 108–116.

[21]

S. Chen, B. Lang, and H. Liu. 2021. DNS covert channel detection method using the LSTM model. Computers & Security. 104 (2021), 102095.

[22]

Ferrag, Mohamed Amine, et al. 2020. Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications 50 (2020), 102419.

[23]

Jianwu Zhang et al. 2020. Model of the intrusion detection system based on the integration of spatial-temporal features. Computers & Security. 89, 101681.

[24]

B. Yan and G. Han. 2028. LA-GRU: Building combined intrusion detection model based on imbalanced learning and gated recurrent unit neural network. Security and Communication Networks (2018), 1–13.

[25]

Pankaj Kumar Jadwal et al. Improved resampling algorithm through a modified oversampling approach based on spectral clustering and SMOTE. In Microsystem Technologies. 1–9.

[26]

L. Liu, P. Wang, and J. Lin. 2020. Intrusion detection of imbalanced network traffic based on machine learning and deep learning. IEEE Access 99 (2020), 1–1.

[27]

R. Abdulhammed, M. Faezipour, and A. Abuzneid. 2018. Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic. IEEE Sensors Letters 3, 1 (2018), 1–4.

[28]

P. Bedi, N. Gupta, and V. Jindal. 2020. Siam-IDS: Handling class imbalance problem in intrusion detection systems using Siamese neural network. Procedia Computer Science 171 (2020), 780–789.

[29]

G. Paun, G. Rozenberg, and A. Salomaa. 2005. DNA Computing: New Computing Paradigms. Springer Science & Business Media.

[30]

Y. S. Ding, L. H. Ren, and S. H. Shao. 2001. DNA Computing and Soft Computing. Acta Simulata Systematica Sinica.

[31]

W. Zang, L. Ren, W. Zhang, et al. 2018. A cloud model-based DNA genetic algorithm for numerical optimization problems. Future Generation Computer Systems 81 (2018), 465–477.

[32]

C. Jatoth, G. R. Gangadharan, and R. Buyya. 2019. Optimal fitness aware cloud service composition using an adaptive genotypes evolution based genetic algorithm. Future Generation Computer Systems 94 (2019), 185–198.

[33]

A. Shukla, H. M. Pandey, and D. Mehrotra. 2015. Comparative review of selection techniques in genetic algorithm. 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE). IEEE, 515–519.

[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.

[35]

K. Deb. 2014. Multi-objective optimization. In Search Methodologies. Springer, Boston, MA, (2014), 403–449.

[36]

Kalyanmoy Deb and Himanshu Jain. 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), 577–601.

[37]

K. Deb, A. Pratap, and S. A. Agarwal. 2002. Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182–197.

Digital Library

[38]

A. Aguilar-Rivera. 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.

[39]

A. M. Usman, U. K. Yusof, and S. Naim. 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, 124–136.

[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.

[41]

L. M. Adleman. 1994. Molecular computation of solutions to combinatorial problems. Science 266, 5187 (1994), 1021–1024.

[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.

[43]

J. Bergstra and Y. Bengio. 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13, 1 (2012), 281–305.

Digital Library

[44]

Das and J. E. Dennis. 1998. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM Journal on Optimization 8, 3 (1998), 631–657.

Digital Library

[45]

A. Prada, A. Gasparella, and P. A. Baggio. 2019. Comparison of three evolutionary algorithms for the optimization of building design. Applied Mechanics and Materials 887 (2019), 140–147.

[46]

Iman Sharafaldin et al. 2019. Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. 2019 International Carnahan Conference on Security Technology (ICCST’19). 1–8.

[47]

Mohammadreza MontazeriShatoori, Logan Davidson, and Gurdip Kaur. 2020. Detection of DoH tunnels using time-series classification of encrypted traffic. 2020. 5th IEEE Cyber Science and Technology Congress, Calgary, Alberta, Canada.

[48]

L. M. Ibrahim, D. T. Basheer, and M. S. A. Mahmod. 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), 107–119.

[49]

Systematic ensemble model selection approach for educational data mining. 2020. Knowledge-Based Systems 200 (2020), 105992.

[50]

J. Li, K. Cheng, S. Wang, and F. Morstatter. 2018. Feature selection: A data perspective. ACM Computing Surveys 50, 6 (2018), 94.

Digital Library

Cited By

Zeng ZLiu XDai MZheng JDeng XZeng DChen J(2024)Causal Genetic Network Anomaly Detection Method for Imbalanced Data and Information RedundancyIEEE Transactions on Network and Service Management10.1109/TNSM.2024.345576821:6(6937-6952)Online publication date: Dec-2024
https://doi.org/10.1109/TNSM.2024.3455768
Zelichenok IKotenko I(2024)L/STIM: A Framework for Detecting Multi-Stage Cyber Attacks2024 International Russian Smart Industry Conference (SmartIndustryCon)10.1109/SmartIndustryCon61328.2024.10516137(208-213)Online publication date: 25-Mar-2024
https://doi.org/10.1109/SmartIndustryCon61328.2024.10516137
Zhao KZhang ZRaymond Choo KZhang ZZhang T(2023)A Combinatorial Optimization Analysis Method for Detecting Malicious Industrial Internet Attack BehaviorsACM Transactions on Cyber-Physical Systems10.1145/36375548:1(1-20)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1145/3637554

Index Terms

Towards Intelligent Attack Detection Using DNA Computing
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

Recommendations

Low-Rate DoS Attack Detection Based on Two-Step Cluster Analysis
Information and Communications Security
Abstract
The low-rate denial of service (LDoS) attacks reduce the throughput of TCP traffic by sending high rate and short duration bursts periodically to the victim. Although many LDoS attack detection methods have been proposed, LDoS attacks are still ...
Parameter manipulation attack prevention and detection by using web application deception proxy
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

The attack abusing web application vulnerabilities are currently classified into traditional attack threats. However, security breaches by web application attacks are still reported via mass media. Although the vulnerabilities in popular products such ...
Attack Detection Application with Attack Tree for Mobile System using Log Analysis
Abstract
Recently, the use of smart phones has greatly increased because of the development of cheap high-performance hardware. The biggest threat to a smart phone user is the loss of his/her personal information by an attacker. To protect a user’s ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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: 08 September 2022

Accepted: 24 August 2022

Revised: 30 July 2022

Received: 20 February 2022

Published in TOMM Volume 19, Issue 3s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
382
Total Downloads

Downloads (Last 12 months)84
Downloads (Last 6 weeks)10

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zeng ZLiu XDai MZheng JDeng XZeng DChen J(2024)Causal Genetic Network Anomaly Detection Method for Imbalanced Data and Information RedundancyIEEE Transactions on Network and Service Management10.1109/TNSM.2024.345576821:6(6937-6952)Online publication date: Dec-2024
https://doi.org/10.1109/TNSM.2024.3455768
Zelichenok IKotenko I(2024)L/STIM: A Framework for Detecting Multi-Stage Cyber Attacks2024 International Russian Smart Industry Conference (SmartIndustryCon)10.1109/SmartIndustryCon61328.2024.10516137(208-213)Online publication date: 25-Mar-2024
https://doi.org/10.1109/SmartIndustryCon61328.2024.10516137
Zhao KZhang ZRaymond Choo KZhang ZZhang T(2023)A Combinatorial Optimization Analysis Method for Detecting Malicious Industrial Internet Attack BehaviorsACM Transactions on Cyber-Physical Systems10.1145/36375548:1(1-20)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1145/3637554

View Options

Login options

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

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents