Research Article
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Year 2024, Volume: 42 Issue: 4, 1160 - 1168, 01.08.2024

Abstract

References

  • 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.

Cognitive activity detection and tracing system

Year 2024, Volume: 42 Issue: 4, 1160 - 1168, 01.08.2024

Abstract

Cognitive problems like Dementia and Alzheimer’s are usually challenging to diagnose but can be noticed by some signs of their symptoms. The most common symptoms are confu-sion, trouble finding the right word, memory loss, and difficulty concentrating. This study aims to design a cognitive activity detection and tracing system that contains games and an-alyzes users’ performances then displays detailed statistics to the users. The proposed Cogni-tive Activity Detection and Tracing System (CADTS) is software that contains different kinds of games from different categories inside its body that aims to measure cognitive activity by utilizing formulations in the context of the games and give feedback to users concerning the performance analyses done. The purpose of these analyses is to catch the signs of symptoms. An insight into a possible scoring system is provided, and as our results, several descriptive statistics are shared based on the tests conducted.

References

  • 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.
There are 35 citations in total.

Details

Primary Language English
Subjects Clinical Sciences (Other)
Journal Section Research Articles
Authors

Onur Yildirim This is me 0000-0002-2405-1537

Çağla Kandemir This is me 0000-0001-6651-933X

Emre Kardaşlar This is me 0000-0003-2249-530X

Emre Sümer 0000-0001-8502-9184

Publication Date August 1, 2024
Submission Date January 19, 2023
Published in Issue Year 2024 Volume: 42 Issue: 4

Cite

Vancouver Yildirim O, Kandemir Ç, Kardaşlar E, Sümer E. Cognitive activity detection and tracing system. SIGMA. 2024;42(4):1160-8.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/