COMPREHENSIVE REVIEW OF ALGORITHMS AND DATA STRUCTURES IN CYBERSECURITY

Authors

  • G. Pramod Kumar
  • Dr. J Bhavana
  • Dr. CV. Guru Rao

DOI:

https://doi.org/10.53555/ephijse.v9i4.243

Keywords:

Data Structures, Algorithms, Confidentiality, Integrity, Availability

Abstract

In the rapidly changing cybersecurity landscape, the implementation of robust algorithms and data structures is crucial for safeguarding digital assets and maintaining the integrity of information systems. This review examines the diverse applications of algorithms and data structures in cybersecurity, with a focus on their essential roles in detecting, preventing, and mitigating cyber threats. We explore classical algorithms such as encryption and hashing techniques, as well as modern advancements in areas such as machine learning and quantum cryptography. In addition, we discuss critical data structures, including trees, graphs, and hash tables, which form the basis of efficient data management and secure communication protocols. Our analysis aimed to provide a comprehensive understanding of these foundational elements and their contributions to cybersecurity. Furthermore, we address the challenges and future directions in integrating these algorithms and data structures into cybersecurity frameworks, emphasizing the need for continuous innovation to counteract the increasing sophistication of cyber-attacks.

Author Biographies

G. Pramod Kumar

Research Scholar, Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India.

 

Dr. J Bhavana

Assistant Professor, Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India.

Dr. CV. Guru Rao

Professor, Registrar, Dean (SoCS), Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India

References

A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210

Flah, M., Nunez, I., Ben Chaabene, W. et al. Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review. Arch Computat Methods Eng 28, 2621–2643 (2021)

K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Ikotun AM, Ezugwu AE, Abualigah L, Abuhaija B, Heming J Inf. Sci. (Ny), 2023

Machine learning and deep learning for safety applications: Investigating the intellectual structure and the temporal evolution, Safety Science, Volume 170,2024, 106363, ISSN 0925-7535

Balobaid, A. S Shaik & Komandur (2023). A Review on Cyber Security Issues in IOT-Based Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 278–285

J. Zhu, "Optimization of Large-Scale Intrusion Detection Algorithms for Digital Information Systems," 2023 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, 2023, pp. 1127-1132, 10.1109/ICICT57646.2023.10133960

A survey on data‐efficient algorithms in big data era. Adadi A, J. Big Data, 2021

A review of systematic selection of clustering algorithms and their evaluation. Wegmann M, Zipperling D, Hillenbrand J, Fleischer Jar Xiv [cs.LG], 2021

r-Reference points-based k-means algorithm Wang CL, Chan YK, Chu SW, Yu SS Inf. Sci. (Ny), 2022

Salar Askari,Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development, Expert Systems with Applications, Volume 165,2021,113856,ISSN 0957-4174

Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development Askari S Expert Syst. Appl., 2021

Machine learning algorithms in civil structural health monitoring: A systematic review Flah M, Nunez I, Ben Chaabene W, Nehdi ML Arch. Comput. Methods Eng., 2021

A review of principal Component Analysis algorithm for dimensionality reduction Salih Hasan BM, Duhok Polytechnic University, Abdulazeez AM, Duhok Polytechnic University Journal of Soft Computing and Data Mining, 2021

A survey of reinforcement learning algorithms for dynamically varying environments Padakandla S ACM Comput. Surv., 2022

Hyper-parameter optimization: A review of algorithms and applications Yu T, Zhu H arXiv [cs.LG], 2020

The Society of Algorithms, Annual Review of Sociology Vol. 47:213-237 (Volume publication date July 2021) First published as a Review in Advance on May 27, 2021

Applying machine learning algorithms for stuttering detection Filipowcz P, Kostek B J. Acoust. Soc. Am., 2023

Structural health monitoring of beam model based on swarm intelligence-based algorithms and neural networks employing FRF Achouri F, Khatir A, Smahi Z, Capozucca R, Ouled Brahim A J. Braz. Soc. Mech. Sci. Eng., 2023

Utilizing hash algorithms for NFT data file integrity checks Song H, Jeong S, Kim K J. Digit. Contents Soc., 2023

Adaptive bias-variance trade-off in advantage estimator for actor–critic algorithms Chen Y, Zhang F, Liu Z Neural Netw., 2024

Kilincer, I. F., Ertam, F., & Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188(107840), 107840

Kilincer, I. F., Ertam, F., & Sengur, A. (2022). A comprehensive intrusion detection framework using boosting algorithms. Computers & Electrical Engineering: An International Journal, 100(107869), 107869

X. A. Larriva-Novo, M. Vega-Barbas, V. A. Villagrá and M. Sanz Rodrigo, "Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies," in IEEE Access, vol. 8, pp. 9005-9014, 2020

Usoh, M., Asuquo, P., Ozuomba, S. et al. A hybrid machine learning model for detecting cybersecurity threats in IoT applications. Int. j. inf. tecnol. 15, 3359–3370 (2023)

Kumar, R.; K, D.; Dumka, a.; Loganathan, J. RFA Reinforced Firefly Algorithm to Identify Optimal Feature Subsets for Network IDS. Int. J. Grid High Perform. Comput. 2020, 12, 5

Thakkar, A.; Lohiya, R. Role of swarm and evolutionary algorithms for intrusion detection system: A survey. Swarm Evol. Comput. 2020, 53, 100631

Pervez, M.S.; Farid, D. Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs. In Proceedings of the SKIMA 2014—8th International Conference on Software, Knowledge, Information Management and Applications, Dhaka, Bangladesh, 15–17 December 2015

Çavuşoğlu, U. A new hybrid approach for intrusion detection using machine learning methods. Appl. Intell. 2019, 49, 2735–2761

Selvakumar, B.; Muneeswaran, K. Firefly algorithm-based feature selection for network intrusion detection. Comput. Secur. 2019, 81, 148–155

Chen, J.; Wu, D.; Zhao, Y.; Sharma, N.; Blumenstein, M.; Yu, S. Fooling intrusion detection systems using adversarially autoencoder. Digit. Commun. Netw. 2020, 7, 453–460

Nijim, M.; Goyal, A.; Mishra, A.; Hicks, D. A Review of Nature-Inspired Artificial Intelligence and Machine Learning Methods for Cybersecurity Applications. In Advances in Nature-Inspired Cyber Security and Resilience; Springer: Cham, Switzerland, 2022; pp. 109–118

Yang, X.S. Nature-Inspired Metaheuristic Algorithms; Luniver Press: Cambridge, UK, 2008; Volume 12, ISBN 978-1-905986-10-1

Ahmed, A.A.; Maheswari, D. Churn prediction on huge telecom data using hybrid firefly-based classification. Egypt. Inform. J. 2017, 18, 215–220]

Adaniya, M.H.; Carvalho, L.F.; Zarpelão, B.B.; Sampaio, L.D.; Abrão, T.; Jeszensky, P.J.E.; Proença, M.L., Jr. Firefly Algorithm in Telecommunications. In Bio-Inspired Computation in Telecommunications; Elsevier: Amsterdam, The Netherlands, 2015; pp. 43–72

Adaniya, M.H.; Lima, M.F.; Rodrigues, J.J.; Abrao, T.; Proença, M.L. Anomaly detection using dsns and firefly harmonic clustering algorithm. In Proceedings of the 2012 IEEE International Conference on Communications (ICC), Ottawa, ON, Canada, 10–15 June 2012; pp. 1183–1187

Tuba, E.; Tuba, M.; Beko, M. Two stage wireless sensor node localization using firefly algorithm. In Smart Trends in Systems, Security and Sustainability; Springer: Singapore, 2018; pp. 113–120

Mahdi, M.S.; Hassan, N.F. Design of keystream Generator utilizing Firefly Algorithm. J. Al-Qadisiyah Comput. Sci. Math. 2018, 10, 91

Yu, G. A modified firefly algorithm based on neighborhood search. Concurr. Comput. Pract. Exp. 2020, 33, e6066

Liaquat, S.; Saleem, O.; Azeem, K. Comparison of Firefly and Hybrid Firefly-APSO Algorithm for Power Economic Dispatch Problem. In Proceedings of the IEEE 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP), Bandung, Indonesia, 23–24 September 2020; pp. 94–99

Lakshmana Rao, K.; Sireesha, R.; Shanti, C. On the convergence and optimality of the firefly algorithm for opportunistic spectrum access. Int. J. Adv. Intell. Paradig. 2021, 18, 119

Kolias, C.; Kambourakis, G.; Stavrou, A.; Gritzalis, S. Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset. IEEE Commun. Surv. Tutor. 2015, 18, 184–208

Zaid, M.; Agarwal, P. Intelligent Intrusion Detection System Optimized using Nature-Inspired Algorithms. In Proceedings of the IEEE 2022 1st International Conference on Informatics (ICI), Noida, India, 14–16 April 2022; pp. 80–85

Najeeb, R.F.; Dhannoon, B.N. A feature selection approach using binary firefly algorithm for network intrusion detection system. ARPN J. Eng. Appl. Sci. 2018, 13, 2347–2352

Ram, B.; Rao, B. An Efficient Ids Based on Fuzzy Firefly Optimization and Fast Learning Network. Int. J. Eng. Technol. 2018, 7, 557–561

Dhanarao, S.; Kumar, M. Efficient IDs for MANET Using Hybrid Firefly with a Genetic Algorithm. In Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 11–12 July 2019

Albadran, M. A new Firefly-Fast Learning Network model based Intrusion-Detection System. Int. J. Innov. Technol. Explor. Eng. 2020, 8, 146–152

Downloads

Published

2023-12-27