Algorithmic means of ensuring network security and websites: trends, models, future cases
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https://doi.org/10.34069/AI/2023.65.05.15
Résumé
The purpose of the study is to establish probable trends in the development of algorithmic means of network security and the protection of web resources in the future. The research methods used in this publication are a bibliometric analysis of 500 relevant publications, which allowed us to establish probable trends in the future development of the subject field. The study found that currently the most likely algorithmic means of network security and website protection that will be intensively developed in the future are blockchain technologies (to protect inter-resource contact), deep and machine learning (to analyze and detect attacks and digital anomalies), artificial intelligence and neural networks (to develop complex security algorithms), and predictive analysis (to prevent possible attacks and malicious data injections). At the same time, technological development makes it possible to identify alternative security tools, including quantum and post-quantum cryptography (which is possible due to the development of quantum computing), augmented reality (which is the next iteration of the development of the interface between machine-human interaction), biometric identification (which is the next iteration of authentication and recognition systems) and DevSecOps (which is a promising technology for the production of digital tools and systems that have a relatively lower level of vulnerability to known digital threats). The correlative impact of Industry 4.0 technologies and solutions on the studied aspects of the security sector of the World Wide Web has been established. The growth of the network of devices requires the improvement of security algorithms in the paradigm of Industry 4.0 technologies, which will allow more effective detection and prevention of cyberattacks and protection of user data.
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Références
Akhtar, M. S., & Feng, T. (2023). Evaluation of Machine Learning Algorithms for Malware Detection. Sensors, 23(2), 946. https://doi.org/10.3390/s23020946
Alemami, Y., Al-Ghonmein, A. M., Al-Moghrabi, K. G., & Mohamed, M. A. (2023). Cloud data security and various cryptographic algorithms. International Journal of Electrical and Computer Engineering, 13(2), 1867-1879. https://doi.org/10.11591/ijece.v13i2.pp1867-1879
Al-Juboori, S. A. M., Hazzaa, F., Jabbar, Z. S., Salih, S., & Gheni, H. M. (2023). Man-in-the-middle and denial of service attacks detection using machine learning algorithms. Bulletin of Electrical Engineering and Informatics, 12(1), 418-426. https://doi.org/10.11591/eei.v12i1.4555
Alzahrani, N. M., & Alfouzan, F. A. (2022). Augmented reality (AR) and cyber-security for smart cities—A systematic literature review. Sensors, 22(7), 2792. https://doi.org/10.3390/s22072792
Bhagat, V., Kumar, S., Gupta, S. K., & Chaube, M. K. (2023). Lightweight cryptographic algorithms based on different model architectures: A systematic review and futuristic applications. Concurrency and Computation: Practice and Experience, 35(1), e7425. https://doi.org/10.1002/cpe.7425
Bhuvaneshwari, K. S. (2023). Smart System and Services Using Artificial Intelligence and Machine Learning Algorithms: Sky of AI. In P. Raj, K. Saini, & V. Pacheco (Eds.), Applying Drone Technologies and Robotics for Agricultural Sustainability (pp. 140-154). IGI Global. https://doi.org/10.4018/978-1-6684-6413-7.ch009
Birrane, E. J., Heiner, S., & McKeever, K. (2023). Using Security Contexts. In eds E.J. Birrane, S. Heiner, & K. McKeever (Eds.), Securing Delay-Tolerant Networks with BPSec (pp.178-198). Wiley. https://doi.org/10.1002/9781119823513.ch10
Brogan, J., Barber, N., Cornett, D., & Bolme, D. (2023). VDiSC: An Open Source Framework for Distributed Smart City Vision and Biometric Surveillance Networks, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshop. https://acortar.link/dL5s9l
Chauhan, J. A., Patel, A. R., Parikh, S., & Modi, N. (2022). An Analysis of Lightweight Cryptographic Algorithms for IoT-Applications. In S. Rajagopal, P. Faruki, & K. Popat (Eds.), Advancements in Smart Computing and Information Security (pp. 201-216). ASCIS 2022. Communications in Computer and Information Science (Vol. 1760). Springer, Cham. https://doi.org/10.1007/978-3-031-23095-0_15
Chen, J. I.-Z., & Lee, C.-Y. (2023). Algorithms Based on Block-Chain Applied to Develop the IoT Applications [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-2331906/v1
Comparitech Limited (2023). Cybersecurity vulnerability statistics and facts of 2023. https://acortar.link/nCgpFD
Diaba, S. Y., & Elmusrati, M. (2023). Proposed algorithm for smart grid DDoS detection based on deep learning. Neural Networks, 159, 175-184. https://doi.org/10.1016/j.neunet.2022.12.011
Dupont, S., Yautsiukhin, A., Ginis, G., Iadarola, G., Fagnano, S., Martinelli, F., Ponsard, C., Legay, A., & Massonet, P. (2023, February). Product Incremental Security Risk Assessment Using DevSecOps Practices. In S. Katsikas et al. (Eds.), Computer Security. ESORICS 2022 International Workshops: CyberICPS 2022, SECPRE 2022, SPOSE 2022, CPS4CIP 2022, CDT&SECOMANE 2022, EIS 2022, and SecAssure 2022, Copenhagen, Denmark, September 26–30, 2022, Revised Selected Papers (pp. 666-685). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-25460-4_38
Edgescan. (2023). Vulnerability Stats Report. https://www.edgescan.com/intel-hub/stats-report
Erondu, U. I., Asani, E. O., Arowolo, M. O., Tyagi, A. K., & Adebayo, N. (2023). An Encryption and Decryption Model for Data Security Using Vigenere With Advanced Encryption Standard. In A. Tyagi (Ed.), Using Multimedia Systems, Tools, and Technologies for Smart Healthcare Services (pp. 141-159). IGI Global. https://doi.org/10.4018/978-1-6684-5741-2.ch009
Evdokimov, V., & Polukhin, A. (2022). Income optimization of market participants in the day ahead market by modeling of processes of price determination for day ahead market. Electronic modeling, 44(4), 121-129. https://doi.org/10.15407/emodel.44.04.121
Ferencz, K., Domokos, J., & Kovacs, L. (2021). Review of industry 4.0 security challenges, IEEE 15th international symposium on applied computational intelligence and informatics (SACI). Timisoara, Romania: IEEE. https://doi.org/10.1109/SACI51354.2021.9465613
Fernando, Y., Tseng, M.-L., Wahyuni-Td, I. S., de Sousa Jabbour, A. B. L., Chiappetta Jabbour, C. J., & Foropon, C. (2023). Cyber supply chain risk management and performance in industry 4.0 era: information system security practices in Malaysia. Journal of Industrial and Production Engineering, 40(2), 102-116. https://doi.org/10.1080/21681015.2022.2116495
Gazdag, S.-L., Grundner-Culemann, S., Heider, T., Herzinger, D., Schärtl, F., Cho, J. Y., Guggemos, T., & Loebenberger, D. (2023). Quantum-Resistant MACsec and IPsec for Virtual Private Networks. In F. Günther, & J. Hesse (Eds.), Security Standardisation Research (pp. 1-21). SSR 2023. Lecture Notes in Computer Science (Vol. 13895). Springer, Cham. https://doi.org/10.1007/978-3-031-30731-7_1
Gheni, H. Q., & Al-Yaseen, W. L. (2023). Using Ensemble Techniques Based on Machine and Deep Learning for Solving Intrusion Detection Problems: A Survey. Karbala International Journal of Modern Science, 9(1), 5. https://doi.org/10.33640/2405-609X.3277
Harris, D., Miknis, M., Smith, C., & Wilson, I. (2023). Metrics for Evaluating Cyber Security Data Visualizations in Virtual Reality. PRESENCE: Virtual and Augmented Reality, 29, 223-240. https://doi.org/10.1162/pres_a_00363
Hasan, M. K., Habib, A. A., Shukur, Z., Ibrahim, F., Islam, S., & Razzaque, M. A. (2023). Review on the cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations. Journal of Network and Computer Applications, 209, 103540. https://doi.org/10.1016/j.jnca.2022.103540
Herbert, B., Wigley, G., Ens, B., & Billinghurst, M. (2022). Cognitive load considerations for Augmented Reality in network security training. Computers & Graphics, 102, 566-591. https://doi.org/10.1016/j.cag.2021.09.001
Hrynchyshyn, Y. (2021). The infrastructure of the Internet services market of the future: analysis of the problems of formation. Futurity Economics & Law, 1(2), 12–16.
IBM (2023). Cost of a data breach 2022. A million-dollar race to detect and respond. https://www.ibm.com/reports/data-breach
Jabbar, A. A., & Bhaya, W. S. (2023). Security of private cloud using machine learning and cryptography. Bulletin of Electrical Engineering and Informatics, 12(1), 561-569. https://doi.org/10.11591/eei.v12i1.4383
Jose, J., & Jose, D. V. (2023). Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 1134-1141. https://doi.org/10.11591/ijece.v13i1.pp1134-1141
Khobragade, P., & Turuk, A.K. (2023). Blockchain Consensus Algorithms: A Survey. In J. Prieto, F.L. Benítez Martínez, S. Ferretti, D. Arroyo Guardeño, & P. Tomás Nevado-Batalla (Eds.), Blockchain and Applications, 4th International Congress (pp. 198-210). BLOCKCHAIN 2022. Lecture Notes in Networks and Systems (Vol. 595). Springer, Cham. https://doi.org/10.1007/978-3-031-21229-1_19
Lakshmi Narayanan, K., & Naresh, R. (2023). An efficient key validation mechanism with VANET in real-time cloud monitoring metrics to enhance cloud storage and security. Sustainable Energy Technologies and Assessments, 56, 102970. https://doi.org/10.1016/j.seta.2022.102970
Li, T., & Zalialetdzinau, K. (2022). Attemps of scientific reflection on the role of e-learning of the future in the area of digital transformation: nеw opportunities and experiences with DevSecOps. Futurity Education, 2(4), 52–63. https://doi.org/10.57125/FED.2022.25.12.06
Martelleur, J., & Hamza, A. (2022). Security Tools in DevSecOps: A Systematic Literature Review. [File PDF]. http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Alnu%3Adiva-118400
Monika, D., Singh, S., & Wason, A. (2023). Performance investigations on data protection algorithms in generalized multi protocol label switched optical networks. Scientific Reports, 13(1), 425. https://doi.org/10.1038/s41598-022-26942-0
Montasari, R. (2023). Artificial Intelligence and the Internet of Things Forensics in a National Security Context. In R. Montasari (Eds.), Countering Cyberterrorism: The Confluence of Artificial Intelligence, Cyber Forensics and Digital Policing in US and UK National Cybersecurity (pp. 57-80), Vol. 101. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21920-7_4
Mughaid, A., AlZu’bi, S., Alnajjar, A., AbuElsoud, E., Salhi, S. E., Igried, B., & Abualigah, L. (2023). Improved dropping attacks detecting system in 5g networks using machine learning and deep learning approaches. Multimedia Tools and Applications, 82(9), 13973-13995. https://doi.org/10.1007/s11042-022-13914-9
National Institute of Standards and Technology (2023). National Vulnerability Database (NVD) Dashboard. (2023). https://nvd.nist.gov/general/nvd-dashboard#
Pawełoszek, I., Kumar, N., & Solanki, U. (2022). Artificial intelligence, digital technologies and the future of law. Futurity Economics & Law, 2(2), 22–32. https://doi.org/10.57125/FEL.2022.06.25.03
Pradhan, D., Sahu, P. K., Rajeswari, Tun, H. M., & Wah, N. K. S. (2023). Integration of AI/Ml in 5G Technology toward Intelligent Connectivity, Security, and Challenges. In P. Chatterjee, M. Yazdani, F. Fernández-Navarro, & J. Pérez-Rodríguez (Eds.), Machine Learning Algorithms and Applications in Engineering. CRC Press. https://doi.org/10.1201/9781003104858-14
Priyanka, K., Skandan, S., Shakthi Saravanan, S., Chandramohanan, R., Darshan, M., & Raswanth, S.R. (2023). Unique and Secure Account Management System Using CNN and Blockchain Technology. In J. Singh, D. Das, L. Kumar, & A. Krishna (Eds.), Mobile Application Development: Practice and Experience. Studies in Systems, Decision and Control (pp. 131-140), Vol. 452. Singapore: Springer. https://doi.org/10.1007/978-981-19-6893-8_11
Sagu, A., Gill, N. S., Gulia, P., Singh, P. K., & Hong, W.-C. (2023). Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment. Sustainability, 15(3), 2204. https://doi.org/10.3390/su15032204
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2022). Evaluating security and privacy issues of social networks based information systems in Industry 4.0. Enterprise Information Systems, 16(10-11), 1694-1710. https://doi.org/10.1080/17517575.2021.1913765
Seh, A. H., Yirgaw, H., Ahmad, M., Faizan, M., Pathak, N., Zaman, M., & Agrawal, A. (2023). A Cybersecurity Perspective of Machine Learning Algorithms. In S. A. Khan, R. Kumar, O. Kaiwartya, R. A. Khan, & M. Faisal (Eds.), Computational Intelligent Security in Wireless Communications (pp. 221-240). CRC Press. https://doi.org/10.1201/9781003323426
Shalini, P. (2023). Multimodal biometric decision fusion security technique to evade immoral social networking sites for minors. Applied Intelligence, 53(3), 2751-2776. https://doi.org/10.1007/s10489-022-03538-9
Shalini, S., Selvi, M., Kannan, A., & Santhosh Kumar, S.V.N. (2023). Review of Security Methods Based on Classical Cryptography and Quantum Cryptography. Cybernetics and Systems. https://doi.org/10.1080/01969722.2023.2166261
Sharma, D., Mittal, R., Sekhar, R., Shah, P., & Renz, M. (2023). A bibliometric analysis of cyber security and cyber forensics research. Results in Control and Optimization, 10, 100204. https://doi.org/10.1016/j.rico.2023.100204
Shiau, W. L., Wang, X., & Zheng, F. (2023). What are the trend and core knowledge of information security? A citation and co-citation analysis. Information & Management, 60(3), 103774. https://doi.org/10.1016/j.im.2023.103774
Statista. (2023). Number of common IT security vulnerabilities and exposures (CVEs) worldwide from 2009 to 2023 YTD. https://www.statista.com/statistics/500755/worldwide-common-vulnerabilities-and-exposures
Upreti, K., Syed, M. H., Khan, M. A., Fatima, H., Alam, M. S., & Sharma, A. K. (2023). Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology. Optik, 272, 170309. https://doi.org/10.1016/j.ijleo.2022.170309
Vulnera (2023) The Vulnerability Stats, Data and Trends to Know in 2023. https://vulnera.com/2023/01/03/vulnerability-statistics-for-2023
WPScan. (2023) WordPress Vulnerability Statistics. https://wpscan.com/statistics
Yadav, P., Chaurasia, N., Gola, K. K., Semwan, V. B., Gomasta, R., & Dubey, S. (2023). A Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print. In Doriya, R., Soni, B., Shukla, A., Gao, XZ. (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering (pp. 315-331). (Vol. 946). Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_24
Yang, A., Lu, C., Li, J., Huang, X., Ji, T., Li, X., & Sheng, Y. (2023). Application of meta-learning in cyberspace security: A survey. Digital Communications and Networks, 9(1), 67-78. https://doi.org/10.1016/j.dcan.2022.03.007
Yarmoliuk, O. (2022). Information support of enterprises: problems, challenges, prospects. Futurity Economics & Law, 2(1), 12–22. https://doi.org/10.57125/FEL.2022.03.25.02
Yi, H. (2023). Machine Learning Method with Applications in Hardware Security of Post-Quantum Cryptography. Journal of Grid Computing, 21(2), 19. https://doi.org/10.1007/s10723-023-09643-4
Zoppi, T., Ceccarelli, A., Puccetti, T., & Bondavalli, A. (2023). Which Algorithm can Detect Unknown Attacks? Comparison of Supervised, Unsupervised and Meta-Learning Algorithms for Intrusion Detection. Computers & Security, 127, 103107. https://doi.org/10.1016/j.cose.2023.103107
Zubaydi, H. D., Varga, P., & Molnár, S. (2023). Leveraging Blockchain Technology for Ensuring Security and Privacy Aspects in Internet of Things: A Systematic Literature Review. Sensors, 23(2), 788. https://doi.org/10.3390/s23020788
Alemami, Y., Al-Ghonmein, A. M., Al-Moghrabi, K. G., & Mohamed, M. A. (2023). Cloud data security and various cryptographic algorithms. International Journal of Electrical and Computer Engineering, 13(2), 1867-1879. https://doi.org/10.11591/ijece.v13i2.pp1867-1879
Al-Juboori, S. A. M., Hazzaa, F., Jabbar, Z. S., Salih, S., & Gheni, H. M. (2023). Man-in-the-middle and denial of service attacks detection using machine learning algorithms. Bulletin of Electrical Engineering and Informatics, 12(1), 418-426. https://doi.org/10.11591/eei.v12i1.4555
Alzahrani, N. M., & Alfouzan, F. A. (2022). Augmented reality (AR) and cyber-security for smart cities—A systematic literature review. Sensors, 22(7), 2792. https://doi.org/10.3390/s22072792
Bhagat, V., Kumar, S., Gupta, S. K., & Chaube, M. K. (2023). Lightweight cryptographic algorithms based on different model architectures: A systematic review and futuristic applications. Concurrency and Computation: Practice and Experience, 35(1), e7425. https://doi.org/10.1002/cpe.7425
Bhuvaneshwari, K. S. (2023). Smart System and Services Using Artificial Intelligence and Machine Learning Algorithms: Sky of AI. In P. Raj, K. Saini, & V. Pacheco (Eds.), Applying Drone Technologies and Robotics for Agricultural Sustainability (pp. 140-154). IGI Global. https://doi.org/10.4018/978-1-6684-6413-7.ch009
Birrane, E. J., Heiner, S., & McKeever, K. (2023). Using Security Contexts. In eds E.J. Birrane, S. Heiner, & K. McKeever (Eds.), Securing Delay-Tolerant Networks with BPSec (pp.178-198). Wiley. https://doi.org/10.1002/9781119823513.ch10
Brogan, J., Barber, N., Cornett, D., & Bolme, D. (2023). VDiSC: An Open Source Framework for Distributed Smart City Vision and Biometric Surveillance Networks, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshop. https://acortar.link/dL5s9l
Chauhan, J. A., Patel, A. R., Parikh, S., & Modi, N. (2022). An Analysis of Lightweight Cryptographic Algorithms for IoT-Applications. In S. Rajagopal, P. Faruki, & K. Popat (Eds.), Advancements in Smart Computing and Information Security (pp. 201-216). ASCIS 2022. Communications in Computer and Information Science (Vol. 1760). Springer, Cham. https://doi.org/10.1007/978-3-031-23095-0_15
Chen, J. I.-Z., & Lee, C.-Y. (2023). Algorithms Based on Block-Chain Applied to Develop the IoT Applications [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-2331906/v1
Comparitech Limited (2023). Cybersecurity vulnerability statistics and facts of 2023. https://acortar.link/nCgpFD
Diaba, S. Y., & Elmusrati, M. (2023). Proposed algorithm for smart grid DDoS detection based on deep learning. Neural Networks, 159, 175-184. https://doi.org/10.1016/j.neunet.2022.12.011
Dupont, S., Yautsiukhin, A., Ginis, G., Iadarola, G., Fagnano, S., Martinelli, F., Ponsard, C., Legay, A., & Massonet, P. (2023, February). Product Incremental Security Risk Assessment Using DevSecOps Practices. In S. Katsikas et al. (Eds.), Computer Security. ESORICS 2022 International Workshops: CyberICPS 2022, SECPRE 2022, SPOSE 2022, CPS4CIP 2022, CDT&SECOMANE 2022, EIS 2022, and SecAssure 2022, Copenhagen, Denmark, September 26–30, 2022, Revised Selected Papers (pp. 666-685). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-25460-4_38
Edgescan. (2023). Vulnerability Stats Report. https://www.edgescan.com/intel-hub/stats-report
Erondu, U. I., Asani, E. O., Arowolo, M. O., Tyagi, A. K., & Adebayo, N. (2023). An Encryption and Decryption Model for Data Security Using Vigenere With Advanced Encryption Standard. In A. Tyagi (Ed.), Using Multimedia Systems, Tools, and Technologies for Smart Healthcare Services (pp. 141-159). IGI Global. https://doi.org/10.4018/978-1-6684-5741-2.ch009
Evdokimov, V., & Polukhin, A. (2022). Income optimization of market participants in the day ahead market by modeling of processes of price determination for day ahead market. Electronic modeling, 44(4), 121-129. https://doi.org/10.15407/emodel.44.04.121
Ferencz, K., Domokos, J., & Kovacs, L. (2021). Review of industry 4.0 security challenges, IEEE 15th international symposium on applied computational intelligence and informatics (SACI). Timisoara, Romania: IEEE. https://doi.org/10.1109/SACI51354.2021.9465613
Fernando, Y., Tseng, M.-L., Wahyuni-Td, I. S., de Sousa Jabbour, A. B. L., Chiappetta Jabbour, C. J., & Foropon, C. (2023). Cyber supply chain risk management and performance in industry 4.0 era: information system security practices in Malaysia. Journal of Industrial and Production Engineering, 40(2), 102-116. https://doi.org/10.1080/21681015.2022.2116495
Gazdag, S.-L., Grundner-Culemann, S., Heider, T., Herzinger, D., Schärtl, F., Cho, J. Y., Guggemos, T., & Loebenberger, D. (2023). Quantum-Resistant MACsec and IPsec for Virtual Private Networks. In F. Günther, & J. Hesse (Eds.), Security Standardisation Research (pp. 1-21). SSR 2023. Lecture Notes in Computer Science (Vol. 13895). Springer, Cham. https://doi.org/10.1007/978-3-031-30731-7_1
Gheni, H. Q., & Al-Yaseen, W. L. (2023). Using Ensemble Techniques Based on Machine and Deep Learning for Solving Intrusion Detection Problems: A Survey. Karbala International Journal of Modern Science, 9(1), 5. https://doi.org/10.33640/2405-609X.3277
Harris, D., Miknis, M., Smith, C., & Wilson, I. (2023). Metrics for Evaluating Cyber Security Data Visualizations in Virtual Reality. PRESENCE: Virtual and Augmented Reality, 29, 223-240. https://doi.org/10.1162/pres_a_00363
Hasan, M. K., Habib, A. A., Shukur, Z., Ibrahim, F., Islam, S., & Razzaque, M. A. (2023). Review on the cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations. Journal of Network and Computer Applications, 209, 103540. https://doi.org/10.1016/j.jnca.2022.103540
Herbert, B., Wigley, G., Ens, B., & Billinghurst, M. (2022). Cognitive load considerations for Augmented Reality in network security training. Computers & Graphics, 102, 566-591. https://doi.org/10.1016/j.cag.2021.09.001
Hrynchyshyn, Y. (2021). The infrastructure of the Internet services market of the future: analysis of the problems of formation. Futurity Economics & Law, 1(2), 12–16.
IBM (2023). Cost of a data breach 2022. A million-dollar race to detect and respond. https://www.ibm.com/reports/data-breach
Jabbar, A. A., & Bhaya, W. S. (2023). Security of private cloud using machine learning and cryptography. Bulletin of Electrical Engineering and Informatics, 12(1), 561-569. https://doi.org/10.11591/eei.v12i1.4383
Jose, J., & Jose, D. V. (2023). Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 1134-1141. https://doi.org/10.11591/ijece.v13i1.pp1134-1141
Khobragade, P., & Turuk, A.K. (2023). Blockchain Consensus Algorithms: A Survey. In J. Prieto, F.L. Benítez Martínez, S. Ferretti, D. Arroyo Guardeño, & P. Tomás Nevado-Batalla (Eds.), Blockchain and Applications, 4th International Congress (pp. 198-210). BLOCKCHAIN 2022. Lecture Notes in Networks and Systems (Vol. 595). Springer, Cham. https://doi.org/10.1007/978-3-031-21229-1_19
Lakshmi Narayanan, K., & Naresh, R. (2023). An efficient key validation mechanism with VANET in real-time cloud monitoring metrics to enhance cloud storage and security. Sustainable Energy Technologies and Assessments, 56, 102970. https://doi.org/10.1016/j.seta.2022.102970
Li, T., & Zalialetdzinau, K. (2022). Attemps of scientific reflection on the role of e-learning of the future in the area of digital transformation: nеw opportunities and experiences with DevSecOps. Futurity Education, 2(4), 52–63. https://doi.org/10.57125/FED.2022.25.12.06
Martelleur, J., & Hamza, A. (2022). Security Tools in DevSecOps: A Systematic Literature Review. [File PDF]. http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Alnu%3Adiva-118400
Monika, D., Singh, S., & Wason, A. (2023). Performance investigations on data protection algorithms in generalized multi protocol label switched optical networks. Scientific Reports, 13(1), 425. https://doi.org/10.1038/s41598-022-26942-0
Montasari, R. (2023). Artificial Intelligence and the Internet of Things Forensics in a National Security Context. In R. Montasari (Eds.), Countering Cyberterrorism: The Confluence of Artificial Intelligence, Cyber Forensics and Digital Policing in US and UK National Cybersecurity (pp. 57-80), Vol. 101. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21920-7_4
Mughaid, A., AlZu’bi, S., Alnajjar, A., AbuElsoud, E., Salhi, S. E., Igried, B., & Abualigah, L. (2023). Improved dropping attacks detecting system in 5g networks using machine learning and deep learning approaches. Multimedia Tools and Applications, 82(9), 13973-13995. https://doi.org/10.1007/s11042-022-13914-9
National Institute of Standards and Technology (2023). National Vulnerability Database (NVD) Dashboard. (2023). https://nvd.nist.gov/general/nvd-dashboard#
Pawełoszek, I., Kumar, N., & Solanki, U. (2022). Artificial intelligence, digital technologies and the future of law. Futurity Economics & Law, 2(2), 22–32. https://doi.org/10.57125/FEL.2022.06.25.03
Pradhan, D., Sahu, P. K., Rajeswari, Tun, H. M., & Wah, N. K. S. (2023). Integration of AI/Ml in 5G Technology toward Intelligent Connectivity, Security, and Challenges. In P. Chatterjee, M. Yazdani, F. Fernández-Navarro, & J. Pérez-Rodríguez (Eds.), Machine Learning Algorithms and Applications in Engineering. CRC Press. https://doi.org/10.1201/9781003104858-14
Priyanka, K., Skandan, S., Shakthi Saravanan, S., Chandramohanan, R., Darshan, M., & Raswanth, S.R. (2023). Unique and Secure Account Management System Using CNN and Blockchain Technology. In J. Singh, D. Das, L. Kumar, & A. Krishna (Eds.), Mobile Application Development: Practice and Experience. Studies in Systems, Decision and Control (pp. 131-140), Vol. 452. Singapore: Springer. https://doi.org/10.1007/978-981-19-6893-8_11
Sagu, A., Gill, N. S., Gulia, P., Singh, P. K., & Hong, W.-C. (2023). Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment. Sustainability, 15(3), 2204. https://doi.org/10.3390/su15032204
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2022). Evaluating security and privacy issues of social networks based information systems in Industry 4.0. Enterprise Information Systems, 16(10-11), 1694-1710. https://doi.org/10.1080/17517575.2021.1913765
Seh, A. H., Yirgaw, H., Ahmad, M., Faizan, M., Pathak, N., Zaman, M., & Agrawal, A. (2023). A Cybersecurity Perspective of Machine Learning Algorithms. In S. A. Khan, R. Kumar, O. Kaiwartya, R. A. Khan, & M. Faisal (Eds.), Computational Intelligent Security in Wireless Communications (pp. 221-240). CRC Press. https://doi.org/10.1201/9781003323426
Shalini, P. (2023). Multimodal biometric decision fusion security technique to evade immoral social networking sites for minors. Applied Intelligence, 53(3), 2751-2776. https://doi.org/10.1007/s10489-022-03538-9
Shalini, S., Selvi, M., Kannan, A., & Santhosh Kumar, S.V.N. (2023). Review of Security Methods Based on Classical Cryptography and Quantum Cryptography. Cybernetics and Systems. https://doi.org/10.1080/01969722.2023.2166261
Sharma, D., Mittal, R., Sekhar, R., Shah, P., & Renz, M. (2023). A bibliometric analysis of cyber security and cyber forensics research. Results in Control and Optimization, 10, 100204. https://doi.org/10.1016/j.rico.2023.100204
Shiau, W. L., Wang, X., & Zheng, F. (2023). What are the trend and core knowledge of information security? A citation and co-citation analysis. Information & Management, 60(3), 103774. https://doi.org/10.1016/j.im.2023.103774
Statista. (2023). Number of common IT security vulnerabilities and exposures (CVEs) worldwide from 2009 to 2023 YTD. https://www.statista.com/statistics/500755/worldwide-common-vulnerabilities-and-exposures
Upreti, K., Syed, M. H., Khan, M. A., Fatima, H., Alam, M. S., & Sharma, A. K. (2023). Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology. Optik, 272, 170309. https://doi.org/10.1016/j.ijleo.2022.170309
Vulnera (2023) The Vulnerability Stats, Data and Trends to Know in 2023. https://vulnera.com/2023/01/03/vulnerability-statistics-for-2023
WPScan. (2023) WordPress Vulnerability Statistics. https://wpscan.com/statistics
Yadav, P., Chaurasia, N., Gola, K. K., Semwan, V. B., Gomasta, R., & Dubey, S. (2023). A Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print. In Doriya, R., Soni, B., Shukla, A., Gao, XZ. (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering (pp. 315-331). (Vol. 946). Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_24
Yang, A., Lu, C., Li, J., Huang, X., Ji, T., Li, X., & Sheng, Y. (2023). Application of meta-learning in cyberspace security: A survey. Digital Communications and Networks, 9(1), 67-78. https://doi.org/10.1016/j.dcan.2022.03.007
Yarmoliuk, O. (2022). Information support of enterprises: problems, challenges, prospects. Futurity Economics & Law, 2(1), 12–22. https://doi.org/10.57125/FEL.2022.03.25.02
Yi, H. (2023). Machine Learning Method with Applications in Hardware Security of Post-Quantum Cryptography. Journal of Grid Computing, 21(2), 19. https://doi.org/10.1007/s10723-023-09643-4
Zoppi, T., Ceccarelli, A., Puccetti, T., & Bondavalli, A. (2023). Which Algorithm can Detect Unknown Attacks? Comparison of Supervised, Unsupervised and Meta-Learning Algorithms for Intrusion Detection. Computers & Security, 127, 103107. https://doi.org/10.1016/j.cose.2023.103107
Zubaydi, H. D., Varga, P., & Molnár, S. (2023). Leveraging Blockchain Technology for Ensuring Security and Privacy Aspects in Internet of Things: A Systematic Literature Review. Sensors, 23(2), 788. https://doi.org/10.3390/s23020788
Publiée
2023-06-30
Comment citer
Bekmagambetova, G., Polukhin, A., Evdokimov, V., Kasmin, D., & Dmytriienko, O. (2023). Algorithmic means of ensuring network security and websites: trends, models, future cases. Amazonia Investiga, 12(65), 149-163. https://doi.org/10.34069/AI/2023.65.05.15
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