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Improved dropping attacks detecting system in 5g networks using machine learning and deep learning approaches

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Abstract

Non Orthogonal Multiple Access (NOMA) successfully drew attention to the deployment of 5th Generation (5G) wireless communication systems, and it is now considered a significant technology in 5G communications. The primary enhancement in 5G is the speed, which may be 100 times faster than 4G. Due to the rising number of internal or external attacks on the Network, wireless intrusion detection systems are a vital aspect of any system connected to the Internet, and 5G will demand considerable improvements in data rate and security. In this paper, we have built a simulator for NOMA and applied a dropping attack to extract a dataset from the simulation model. The accuracy for detecting dropping attacks using the extracted data after applying ML algorithms was 95.7% for LR. Furthermore, this work suggests a methodology for wireless cyberattack detection in 5G networks based on applying several ML and DL techniques such as Decision Trees, KNN, Multi-class Decision Jungle, Multi-class Decision Forest, and Multi-class Neural Network. The proposed work is implemented and tested using a comprehensive Wi-Fi network benchmark dataset. The conducted experiments resulted in an outstanding performance with an accuracy of 99% for the KNN algorithm and 93% for DF and Neural Network.

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References

  1. Abusukhon A, AlZu’bi S (2020) New direction of cryptography: a review on text-to-image encryption algorithms based on rgb color value. In: 2020 seventh international conference on software defined systems, (SDS), IEEE, pp 235–239

  2. Ahmad R, Alsmadi I (2021) Machine learning approaches to iot security: A systematic literature review. Inter Things 14:100365

    Article  Google Scholar 

  3. Ahmad I, Kumar T, Liyanage M, Okwuibe J, Ylianttila M, Gurtov A (2018) Overview of 5g security challenges and solutions. IEEE Commun Mag 2(1):36–43

    Google Scholar 

  4. Akbar A, Jangsher S, Bhatti FA (2021) Noma and 5g emerging technologies: a survey on issues and solution techniques. Comput Netw 190:107950

    Article  Google Scholar 

  5. Al Ridhawi I, Aloqaily M, Boukerche A, Jararweh Y (2021) Enabling intelligent iocv services at the edge for 5g networks and beyond. IEEE Trans Intell Transp Syst 22(8):5190–5200

    Article  Google Scholar 

  6. Al Shinwan M, Abualigah L, Huy T-D, Younes Shdefat A, Altalhi M, Kim C, El-Sappagh S, Abd Elaziz M, Kwak KS (2022) An efficient 5g data plan approach based on partially distributed mobility architecture. Sensors 22 (1):349

    Article  Google Scholar 

  7. Al-Arjan A, Rasmi M, AlZu’bi S, et al. (2021) Intelligent security in the era of ai: the key vulnerability of rc4 algorithm. In: 2021 international conference on information technology, (ICIT), IEEE, pp 691–694

  8. Al-Falahy N, Alani OY (2017) Technologies for 5g networks: challenges and opportunities. IT Professional 19(1):12–20

    Article  Google Scholar 

  9. Al-Obiedollah H, Salameh HB, Abdel-Razeq S, Hayajneh A, Cumanan K, Jararweh Y (2022) Energy-efficient opportunistic multi-carrier noma-based resource allocation for beyond 5g (b5g) networks. Simul Model Pract Theory 116:102452

    Article  Google Scholar 

  10. Al-Zu’bi S, Hawashin B, Mughaid A, Baker T (2021) Efficient 3d medical image segmentation algorithm over a secured multimedia network. Multimed Tools Appl 80(11):16887–16905

    Article  Google Scholar 

  11. AlZu’bi S, Jararweh Y, Al-Zoubi H, Elbes M, Kanan T, Gupta B (2019) Multi-orientation geometric medical volumes segmentation using 3d multiresolution analysis. Multimed Tools Appl 78(17):24223–24248

    Article  Google Scholar 

  12. Alasal SA, Alsmirat M, Baker QB, Alzu’bi S, et al. (2020) Lumbar disk 3d modeling from limited number of mri axial slices. Inter J Electr Comput Eng 10(4):4101

    Google Scholar 

  13. Alkhatib AA, Abu Maria K, Alzu’bi S, Abu Maria E (2022) Novel system for road traffic optimisation in large cities, IET Smart Cities

  14. Alzubi S, Hawashin B, Mughaid A, Jararweh Y (2020) Whats trending? an efficient trending research topics extractor and recommender. In: 2020 11th international conference on information and communication systems, (ICICS), IEEE, pp 191–196

  15. Aminanto ME, Choi R, Tanuwidjaja HC, Yoo PD, Kim K (2017) Deep abstraction and weighted feature selection for wi-fi impersonation detection. IEEE Trans Inform Forensics Secur 13(3):621–636

    Article  Google Scholar 

  16. Aminanto ME, Tanuwidjaja HC, Yoo PD, Kim K (2017) Wi-fi intrusion detection using weighted-feature selection for neural networks classifier. In: 2017 international workshop on big data and information security, (IWBIS), IEEE, pp 99–104

  17. Aqel D, Al-Zubi S, Mughaid A, Jararweh Y (2021) Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Cluster Computing, pp 1–14

  18. Arachchillage USSS, Jayakody DNK, Biswash SK, Dinis R (2018) Recent advances and future research challenges in non-orthogonal multiple access for 5g networks. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), IEEE, pp 1–6

  19. Arfaoui G, Bisson P, Blom R, Borgaonkar R, Englund H, Félix E, Klaedtke F, Nakarmi PK, Näslund M, O’Hanlon P, Papay J, Suomalainen J, Surridge M, Wary J-P, Zahariev A (2018) A security architecture for 5g networks. IEEE Access 6:22466–22479

    Article  Google Scholar 

  20. Bengag A, Moussaoui O, Moussaoui M (2019) A new ids for detecting jamming attacks in wban. In: 2019 third international conference on intelligent computing in data sciences, (ICDS), IEEE, pp 1–5

  21. Bounouni M, Bouallouche-Medjkoune L, Beraza A, Daoud A (2021) Eliminating selective dropping attack in mobile ad hoc network. Wirel Pers Commun, pp 1–18

  22. Dawoud A, Shahristani S, Raun C (2018) Deep learning for network anomalies detection. In: 2018 International Conference on Machine Learning and Data Engineering (iCMLDE), IEEE, pp 149–153

  23. De Capitani di Vimercati S, Foresti S, Jajodia S, Livraga G, Paraboschi S, Samarati P (2021) An authorization model for query execution in the cloud. The VLDB Journal, pp 1–25

  24. Fang H, Wang X, Tomasin S (2019) Machine learning for intelligent authentication in 5g and beyond wireless networks. IEEE Wirel Commun 26(5):55–61

    Article  Google Scholar 

  25. Forland MK, Kralevska K, Garau M, Gligoroski D (2019) Preventing ddos with sdn in 5g. In: 2019 IEEE globecom workshops, (GC Wkshps), IEEE, pp 1–7

  26. Hnaif A, Jaber KM, Alia MA, Daghbosheh M (2021) Parallel scalable approximate matching algorithm for network intrusion detection systems. Int Arab J Inf Technol 18(1):77–84

    Google Scholar 

  27. Intelligence NM (2021) Ai reflections in 2021. Evol Comput 3:9–15

    Google Scholar 

  28. Khan R, Kumar P, Jayakody DNK, Liyanage M (2019) A survey on security and privacy of 5g technologies: Potential solutions, recent advancements, and future directions. IEEE Commun Surv Tut 22(1):196–248

    Article  Google Scholar 

  29. Koh PW, Steinhardt J, Liang P (2021) Stronger data poisoning attacks break data sanitization defenses. Machine Learning, pp 1–47

  30. Kumar A, Varadarajan V, Kumar A, Dadheech P, Choudhary SS, Kumar VA, Panigrahi BK, Veluvolu KC (2021) Black hole attack detection in vehicular ad-hoc network using secure aodv routing algorithm. Microprocess Microsyst 80:103352

    Article  Google Scholar 

  31. Lafi M, Hawashin B, AlZu’bi S (2021) Eliciting requirements from stakeholders’ responses using natural language processing. Comput Model Eng Sci 127 (1):99–116

    Google Scholar 

  32. Lam J, Abbas R (2020) Machine learning based anomaly detection for 5g networks, arXiv:2003.03474

  33. Maimó LF, Gómez Á LP, Clemente FJG, Pérez MG, Pérez GM (2018) A self-adaptive deep learning-based system for anomaly detection in 5g networks. IEEE Access 6:7700–7712

    Article  Google Scholar 

  34. Malekzadeh M, Ghani AAA, Subramaniam S, Desa JM (2011) Validating reliability of omnet++ in wireless networks dos attacks: Simulation vs. testbed. Int J Netw Secur 13(1):13–21

    Google Scholar 

  35. Malik A, Khan MZ, Faisal M, Khan F, Seo J-T (2022) An efficient dynamic solution for the detection and prevention of black hole attack in vanets. Sensors 22(5):1897

    Article  Google Scholar 

  36. Marashdeh Z, Suwais K, Alia M (2021) A survey on sql injection attack: detection and challenges. In: 2021 international conference on information technology, (ICIT), IEEE, pp 957–962

  37. Mitrokotsa A, Mavropodi R, Douligeris C (2006) Intrusion detection of packet dropping attacks in mobile ad hoc networks. In: Proceedings of the international conference on intelligent systems and computing: theory and applications, pp 111–118

  38. Mughaid A, Al-Zu’bi S, Al Arjan A, Al-Amrat R, Alajmi R, Zitar RA, Abualigah L (2022) An intelligent cybersecurity system for detecting fake news in social media websites. Soft Comput 26(12):5577–5591

    Article  Google Scholar 

  39. Mughaid A, AlZu’bi S, Hnaif A, Taamneh S, Alnajjar A, Elsoud EA (2022) An intelligent cyber security phishing detection system using deep learning techniques. Cluster Computing, pp 1–10

  40. Muro Barbé M (2020) Development of a MATLAB application to generate NOMA-CAP 5G signals (Bachelor’s thesis, Universitat Politècnica de Catalunya).

  41. Obeidat I, Mughaid A, Alzoubi S (2019) A Secure Encrypted Protocol for Clients’ Handshaking in the Same Network.

  42. Otair M, Ibrahim OT, Abualigah L, Altalhi M, Sumari P (2022) An enhanced grey wolf optimizer based particle swarm optimizer for intrusion detection system in wireless sensor networks. Wirel Netw 28(2):721–744

    Article  Google Scholar 

  43. Oyelade ON, Ezugwu AE, Almutairi MS, Saha AK, Abualigah L, Chiroma H (2022) A generative adversarial network for synthetization of regions of interest based on digital mammograms. Sci Rep 12(1):1–30

    Article  Google Scholar 

  44. Pathan F, Shringare K (2019) Analysis of 5g mobile technologies and ddos defense. Inter Res J Eng Technol 6(6):2973–2979

    Google Scholar 

  45. Ran J, Ji Y, Tang B (2019) A semi-supervised learning approach to ieee 802.11 network anomaly detection. In: 2019 IEEE 89th Vehicular technology conference (VTC2019-Spring), IEEE, pp 1–5

  46. Safaldin M, Otair M, Abualigah L (2021) Improved binary gray wolf optimizer and svm for intrusion detection system in wireless sensor networks. J Ambient Intell Human Comput 12(2):1559–1576

    Article  Google Scholar 

  47. Thanthrige USKPM, Samarabandu J, Wang X (2016) Machine learning techniques for intrusion detection on public dataset. In: 2016 IEEE canadian conference on electrical and computer engineering, (CCECE), IEEE, pp 1–4

  48. Vaca FD, Niyaz Q (2018) An ensemble learning based wi-fi network intrusion detection system (wnids). In: 2018 IEEE 17th international symposium on network computing and applications, (NCA), IEEE, pp 1–5

  49. Wang S, Li B, Yang M, Yan Z (2018) Intrusion detection for wifi network: A deep learning approach. In: International wireless internet conference, Springer, pp 95–104

  50. Zhang S, Wang Y, Zhou W (2019) Towards secure 5g networks: a survey. Comput Netw 162:106871

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Faculty of prince Al-Hussien bin Abdullah || for IT, The Hashemite University, PO Box 330127, Zarqa, 13133, Jordan

    Ala Mughaid, Asma Alnajjar, Esraa AbuElsoud, Subhieh El Salhi & Bashar Igried

  2. Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman, Jordan

    Shadi AlZu’bi

  3. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  4. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

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  1. Ala Mughaid
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Correspondence to Laith Abualigah.

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Mughaid, A., AlZu’bi, S., Alnajjar, A. et al. Improved dropping attacks detecting system in 5g networks using machine learning and deep learning approaches. Multimed Tools Appl 82, 13973–13995 (2023). https://doi.org/10.1007/s11042-022-13914-9

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