Performance estimation of honeynet system for network security enhancement via copula linguistic

Yıl 2024, Cilt: 42 Sayı: 4, 1169 - 1182, 01.08.2024

Muhammad Salihu Isa Jinbiao Wu İbrahim Yusuf Abdullah Sanusi

Öz

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious actors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evaluation, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multistate honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or nonlethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, reliability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment.

Anahtar Kelimeler

Cyber, Honeynet, Intrusion detection system (IDS), Network, Security

Kaynakça

• REFERENCES
• [1] Paryathia P, Chintab A, Patnala CM. A Honey Pot Implementation for Security Enhancement in IOT System using AES and Key management. Turk J Comput Math Educ 2021;12:52065214. [CrossRef]
• [2] Naik N, Jenkins P, Savage N. A computational intelligence enabled honeypot for chasing ghosts in the wires. Complex Intell Syst 2021;7:477494. [CrossRef]
• [3] Kondra JR, Bharti SK, Mishra SK, Babu KS. Honeypot-Based Intrusion Detection System: A Performance Analysis. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom); 2016. p. 23472351
• [4] Agrawal N, Tapaswi S. The performance analysis of honeypot based intrusion detection system for wireless network. Int J Wirel Inf Netw 2017;24:1421. [CrossRef]
• [5] Kasongo SM, Sun Y. Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset. J Big Data 2020;7:120. [CrossRef]
• [6] Disha RA, Waheed S. Performance analysis of machine learning models for intrusion detection system using gini impurity-based weighted random forest (GIWRF) feature selection technique. Cybersecurity 2022;5:1. [CrossRef]
• [7] Alazzam H, Sharieh A, Sabri KE. A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst Appl 2020;148:113249. [CrossRef]
• [8] Belgrana FZ, Benamrane N, Hamaida MA. Network Intrusion Detection System using Neural Network and Condensed Nearest Neighbors with Selection of NSL-KDD Influencing features. In: 2020 IEEE International Conference on Internet of Things and Intelligence System; 2020. p. 2329. [CrossRef]
• [9] Mauro DM, Galatro G, Liotta A. Experimental review of neural-based approaches for network intrusion management. IEEE Trans Netw Serv Manag 2020;17:24802495. [CrossRef]
• [10] Kelly C, Pitropakis N, Mylonas A, McKeown S, Buchanan WJ. A comparative analysis of honeypots on different cloud platforms. Sensors. 2021;21:2433. [CrossRef]
• [11] Sethia V, Jeyasekar A. Malware Capturing and Analysis using Dionaea Honeypot. In: 2019 International Carnahan Conference on Security Technology; 2019 Oct 1-3; Chennai, India. p. 14. [CrossRef]
• [12] Lee J, Pak J, Lee M. Network Intrusion Detection System using Feature Extraction Based on Deep Sparse Autoencoder. In: 2020 International Conference on Information and Communication Technology Convergence; 2020. p. 12821287. [CrossRef]
• [13] Gu J, Lu S. An effective intrusion detection approach using SVM with naive bayes feature embedding. Comput Secur 2021;103:102158. [CrossRef]
• [14] Isa MS, Yusuf I, Ali UA, Suleiman K, Yusuf B, Ismail AL. Reliability analysis of multi-workstation computer network configured as series-parallel system via gumbel - hougaard family copula. Int J Oper Res 2022;19:1326.
• [15] Isa MS, Abubakar MI, Ibrahim KH, Yusuf I, Tukur I. Performance analysis of complex series parallel computer network with transparent bridge using copula distribution. Int J Reliab Risk Saf Theory Appl 2021;4:4759. [CrossRef]
• [16] Xie L, Lundteigen MA, Liu YL. Common Cause Failures and Cascading Failures in Technical Systems, Similarities, Differences and Barriers. In Haugen S, Barros A, Gulijk C, Kongsvik T, Vinnem JE, (editors). Safety and Reliability – Safe Societies in a Changing World. Proceedings of ESREL 2018, June 17-21, 2018, Trondheim, Norway. [CrossRef]
• [17] Xie L, Lundteigen MA, Liu YL. Performance analysis of safety instrumented systems against cascading failures during prolonged demands. Reliab Eng Syst Saf 2021;216. [CrossRef]
• [18] Yusuf I, Ismail AL, Singh VV, Ali UA, Sufi NA. Performance analysis of multi computer system consisting of three subsystems in series configuration using copula repair policy. SN Comput Sci 2020;1:241. [CrossRef]
• [19] Colledani M, Tolio T, Yemane A. Production Quality Improvement During manufacturing systems ramp-up. J Manuf Sci Technol 2019;23. [CrossRef]
• [20] Althubiti SA, Jones EM, Roy K. LSTM for Anomaly-Based Network Intrusion Detection. In: 28th International Telecommunication Networks and Applications Conference; 2018. [CrossRef]
• [21] AlHamouz S, Abu-Shareha A. Hybrid Classification Approach Using Self-Organizing Map and Back Propagation Artificial Neural Networks for Intrusion Detection. In: 10th International Conference on Developments in eSystems Engineering (DeSE); 2017. [CrossRef]
• [22] Albahar M, Alharbi A, Alsuwat M, Aljuaid H. A hybrid model based on radial basis function neural network for intrusion detection. Int J Adv Comput Sci Appl 2020;11:781791. [CrossRef]
• [23] Arqub OA, Singh J, Alhodaly M. Adaptation of kernel functions-based approach with Atangana–Baleanu–Caputo distributed order derivative for solutions of fuzzy fractional Volterra and Fredholm integrodifferential equations. Math Meth Appl Sci 2021;46:7228. [CrossRef]
• [24] Hammour ZA, Arqub OA, Momani S, Nabil S. Optimization Solution of Troesch’s and Bratu’s Problems of Ordinary Type Using Novel Continuous Genetic Algorithm. Discret Dyn Nat Soc 2014;2014:401696. [CrossRef]
• [25] Arqub OA, Hammour ZA. Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 2014;279:396415. [CrossRef]
• [26] Arqub OA, Singh J, Banan M, Alhodaly M. Reproducing kernel approach for numerical solutions of fuzzy fractional initial value problems under the Mittag–Leffler kernel differential operator. Math Meth Appl Sci 2021;46:79657986. [CrossRef]
• [27] Kenan E, Mustafa CK, Boru B. Comparison of gesture classification methods with contact and non-contact sensors for human-computer interaction. Sigma J Eng Nat Sci 2021;40:219226.
• [28] Şekerci AZ, Aydın N. A stochastic model for facility locations using the priority of fuzzy AHP. Sigma J Eng Nat Sci 2022;40:649662. [CrossRef]
• [29] Aydın Er B, Şişman A, Ardalı Y. Applicability of radial-based artificial neural networks (RBNN) on coliform calculation: A case of study. Sigma J Eng Nat Sci 2022;40:724731. [CrossRef]
• [30] Tolga B, Ali FG. BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model. Sigma J Eng Nat Sci 2022;40:877893.
• [31] Bakar O, Murat B. Applicability of radial-based artificial neural networks (RBNN) on coliform calculation: A case of study. Sigma J Eng Nat Sci 2021;40:235242.
• [32] Adem Y. Intuitionistic fuzzy hypersoft topology and its applications to multi-criteria decision-making. Sigma J Eng Nat Sci 2023;41:106118.
• [33] Maryam B, Rashid R, Karim S. On codes over product of finite chain rings. Sigma J Eng Nat Sci 2023;41:145155.
• [34] Isa MS, Yusuf I, Ali UA, Jinbiao W. Series-parallel computer system performance evaluation with human operator using gumbel hougaard family copula. In: Computational Intelligence in Sustainable Reliability Engineering. 2023. p. 109127. [CrossRef]
• [35] Yusuf I, Ismail AL, Sufi NA, Ambursa FU, Sanusi A, Isa MS. Reliability Analysis of Distributed System for Enhancing Data Replication using Gumbel Hougaard Family Copula Approach Joint Probability Distribution. J Ind Eng Int 2021;17:5978.