Abstract
The propagation of 5G, beyond 5G and Internet of Everything (IoE) networks are the key business force for future networks and its various applications. These networks have been constantly under various assaults by means of blocking and tracking information. Therefore, it is essential to develop a real-time recognition system to handle these assaults. But, not sufficient research has been conducted in this area so far. Hence we propose a model to recognize various assaults via online in 5G, beyond 5G and IoE networks using dominance based rough set and formal concept analysis. For analyzing the model, this paper incorporates legal and simulated 5G, beyond 5G and IoE network traffic, along with various types of assaults. The dominance based rough set is used to identify the assaults whereas chief features that are involved in various assaults are identified using formal concept analysis. The results acquired explain the capability of the projected research.
Similar content being viewed by others
References
Le NT, Hossain MA, Islam A, Kim DY, Choi YJ, Jang YM (2016) Survey of promising technologies for 5g networks. Mob Inf Syst 16(1):1–25
Du R, Santi P, Xiao M, Vasilakos AV, Fischione C (2019) The sensable city: A survey on the deployment and management for smart city monitoring. IEEE Communications Surveys Tutorials 21 (2):1533–1560
Atzori L, Iera A, Morabito G (2010) The internet of things: A survey. Comput Netw 54 (15):2787–2805
Andreev S, Galinina O, Pyattaev A, Gerasimenko M, Tirronen T, Torsner J, Sachs J, Dohler M, Koucheryavy Y (2015) Understanding the iot connectivity landscape: A contemporary m2m radio technology roadmap. IEEE Commun Mag 25(7):345–370
Mitton N, Papavassiliou S, Puliafito A, Trivedi KS (2012) Combining cloud and sensors in a smart city environment. EURASI Journal on Wireless Communications and Networking 17(5):245–260
Vermesan O, Eisenhauer M, Serrano M, Guillemin P, Sundmaekar H, Tragos EZ, Valino J, Copineaux B, Presser M, Aagaard A, Bahr R, Darmois EC (2019) The next generation internet of things – hyper connectivity and embedded intelligence at the edge. Journal of Networking and Internet of Things 67(3):19–102
Wei Y, Li Q, Gong X, Guo D, Zhang Y (2019) Spectrum prediction and aggregation strategy in multi-user cooperative relay networks. International Journal of High Performance Computing and Networking 13(2):241–250
Dutta UK, Razzaque MA, Al-Wadud MA, Islam MS, Hossain MS, Gupta BB (2018) Self-adaptive scheduling of base transceiver stations in green 5g network. IEEE Access 6(1):7958–7969
Ahmed NSS, Acharjya DP (2015) Detection of denial of service attack in wireless network using dominance based rough set. International Journal of Advanced Computer Science and Applications 6(12):267–278
Ahmed NSS, Acharjya DP (2015) A dominance based rough set approach for the detection of jamming attack. International Journal of Philosophies in Computer Science 1(2):45–66
Gurusamy D, Priya MD, Yibgeta B, Bekalu A (2019) DDos risk in 5g enabled iot and solutions. International Journal of Engineering and Advanced Technology 8(5):1574–1578
Ahmed NSS, Acharjya DP (2019) A framework for various attack identification in manet using multi-granular rough set. International Journal of Information Security and Privacy 13(4):28–52
Acharjya DP, Ahmed NSS (2017) Recognizing Attacks in Wireless Sensor Network in View of Internet of Things. In: Acharjya DP, Geetha MK (eds) Internet of things : novel advances and envisioned, studies in big data 25, pp. 173–191
Rezvy S, Luo Y, Petridis M, Lasebae A, Zebin T (2019) An efficient deep learning model for intrusion classification and prediction in 5g and iot networks. In: Proceedings of Annual Conference on Information Sciences and Systems, pp. 1–6
Tian Z, Sun Y, Su S, Li M, Du X (2019) Automated attack and defense framework for 5G Security on physical and logical layers. Int J Adv Comput Sci Appl 10(1):1–12
Fang D, Qian Y, Hu RQ (2017) Security for 5g mobile wireless networks. IEEE Access 17 (1):1–24
Sahoo SR, Gupta BB (2018) Security issues and challenges in online social networks (OSNs) based on user perspective: principles, algorithm, applications, and perspectives. In: Gupta BB (ed) computer and cyber security, taylor and francis group 25, pp. 1–16
Zhang Z, Sun R, Zhao C, Wang J, Chang CK, Gupta BB (2017) CyVOD: A novel trinity multimedia social network scheme. Multimedia Tools and Applications 76(1):18513–18529
Mamolar AS, Pervez Z, Wang Q, Alcaraz-Calero JM (2019) Towards the detection of mobile ddos attacks in 5g multi-tenant networks. In: Proceedings of European Conference on Networks and Detection, pp. 1–6
Hussain SR, Echeverria M, Chowdhury O, Li N, Bertino E (2019) Privacy attacks to the 4g and 5g cellular paging protocol s using side channel information. International Journal of Computer Application 17(1):1–15
Alquhayz H, Alalwan N, Alzahrani AI, Al-Bayatti AH, Sharif MS (2019) Policy-based security management system for 5g heterogeneous networks. Wirel Commun Mob Comput 19(1):1–14
Abdul-Ghani HA, Konstantas D, Mahyoub M (2018) A comprehensive iot attacks survey based on a building-block reference model. Int J Adv Comput Sci Appl 9(3):355–373
Abdul-Ghani HA, Konstantas D (2019) A comprehensive study of security and privacy guidelines, threats, and countermeasures : an iot perspective. Journal of Sensor and Actuator Networks 8(22):1–38
Idris MY, Malik RF, Nurmaini S, Alsharif N, Budiarto R (2019) Investigating brute force attack patterns in iot network. Journal of Electrical and Computer Engineering 19(1):1–14
Deogirikar J, Vidhate A (2017) Security attacks in iot : A survey. In: Proceedings of International Conference on IoT in Social, Mobile, Analytics and Cloud, pp. 32–37
Yu W, Kose S (2017) A lightweight masked AES implementation for securing iot against cpa attacks. IEEE Transactions on Circuits and Systems 64(11):2934–2944
Strielkina A, Kharchenko V, Uzun D (2018) Availability models for healthcare iot systems: classification and research considering attacks on vulnerabilities. In: Proceedings of IEEE 9th International Conference on Dependable Systems, Services and Technologies, pp. 58–62
Wang H, Zhang Z, Taleb T (2018) Special issue on security and privacy of iot. World Wide Web 21(1):1–6
Pawlak Z (1982) Rough sets. International Journal of Computer and Information Sciences 11:341–356
Greco S, Matarazzo B, Slowinski R (1991) The use of rough sets and fuzzy sets in MCDM. In: Gal T, Stewart T, Hanne T (eds) Advances in multiple criteria decision making, kluwer academic publishers, pp. 14.1–14.59
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer academic publishers, dordrecht the netherlands
Greco S, Matarazzo B, Slowinski R, Stefanowski J (1999) An algorithm for induction of decision rules consistent with the dominance principle. Eur J Oper Res 117:63–83
Ahmed NSS, Acharjya DP, Sanyal S (2017) A framework for phishing attack identification using rough set and formal concept analysis. International Journal of Communication Networks and Distributed Systems 18(2):186–212
Jurga RE, Hulb MM, Procurve HP (2007) Technical report packet sampling for network monitoring. Journal of Computational Engineering 34(2):234–246
Grzymala-Busse JW (1992). In: Slownski R (ed) LERS – A system for learning from examples based on rough set. Kluwer Academic Publishers, Handbook of Applications and Advances of Rough Sets Theory, pp 3–18
Rodrigues GAP, Albuquerque RDO, Deus FEG (2017) Cybersecurity and network forensics: Analysis of malicious traffic towards a honeypot with deep packet inspection. Application Science 7(10):1–1082
Moustafa N, Creech G, Slay J (2108) Flow aggregator module for analyzing network traffic. In: Proceedings of Computing, Analytics and Networking, pp. 19–29
Vel OD, Anderson A, Comey M, Mohay G (2001) Mining e-mail content for author identification forensicis. ACM SIGMOD Rec. 30(4):55–64
Koroniots N, Moustafa N, Sitnikova E, Turnbull B (2019) Towards the development of realistic botnet dataset in the internet of things for network forensic analysis : Bot-iot dataset. Future Generation Computer System 100(19):779–796
Chang YH, Huang HY (2008) An automatic document classifier system based on naive bayes classifier and ontology. In: Proceedings of IEEE International Conference on Machine Learning and Cybernetics, 6, 3144–3149
Belohlavek R, De Baets B, Outrata J, Vychodil V (2009) Inducing decision trees via concept lattices. International Journal of General Systems 38(4):455–467
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
First Author declares that he has no conflict of interest. Second Author declares that he has no conflict of interest.
Additional information
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed Consent
Informed consent was obtained from all individual participants included in the study.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection: Special Issue on P2P Computing for Beyond 5G Network and Internet-of-Everything
Guest Editors: Prakasam P, Ajayan John, Shohel Sayeed
Rights and permissions
About this article
Cite this article
Acharjya, D.P., Ahmed, N.S.S. Tracing of online assaults in 5G networks using dominance based rough set and formal concept analysis. Peer-to-Peer Netw. Appl. 14, 349–374 (2021). https://doi.org/10.1007/s12083-020-00983-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12083-020-00983-6