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Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2026 / Cilt: 42 Sayı: 2

CNN Based Cyber Defense Architecture for 802.11 Networks

802.11 Kablosuz Ağları için CNN Tabanlı Siber Savunma Mimarisi

Mehmet Ali Ceylan(FIRAT ÜNİVERSİTESİ)0009-0005-7706-2757
İlhan Fırat Kılınçer(FIRAT ÜNİVERSİTESİ)0000-0001-8090-4998
Fatih Ertam(FIRAT ÜNİVERSİTESİ)0000-0002-9736-8068
Pages
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Publication Date
2026-06-18
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Moreover, the study serves to assess the effectiveness of feature selection in the new dataset comprising 8 classes and 64 features extracted from the AWID3 dataset.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The 802.11 protocol, which forms the basis of Wi-Fi technology, enables computers, smartphones and other devices to connect to each other wirelessly via wireless local area networks (WLAN).
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Key Findings & Data:The performance of the proposed method is evaluated using Wireshark files from the AWID3 (Aegean Wi-Fi Intrusion Dataset) dataset.
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Conclusion & Impact:Moreover, the study serves to assess the effectiveness of feature selection in the new dataset comprising 8 classes and 64 features extracted from the AWID3 dataset.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:802.11 Protokolü · Evrişimsel Sinir Ağları · Görüntü İşleme

Abstract

The 802.11 protocol, which forms the basis of Wi-Fi technology, enables computers, smartphones and other devices to connect to each other wirelessly via wireless local area networks (WLAN). In particular, the advent of the Internet of Things (IoT) has rendered Wi-Fi networks an indispensable component of modern connectivity. This situation is characterised by a multitude of security vulnerabilities. This study proposes a convolutional neural network (CNN) based method for the detection of cyber attacks on wireless networks. The performance of the proposed method is evaluated using Wireshark files from the AWID3 (Aegean Wi-Fi Intrusion Dataset) dataset. The following attacks were decomposed into 64 features with Tshark software; deauthentication, disassociation, (re)association, rogue AP, Krack, Kr00k, and evil twin attacks. Additionally, PCAP files belonging to the normal class in the AWID3 dataset were decomposed into 64 features. The most effective 36 features from the 64-feature dataset were selected with the InfoGain attribute evaluation algorithm. Each row of the dataset, comprising 36 features, was transformed into a 6x6 and 1-channel (grayscale) image. In the final stage of the process, the 6x6x1 dimensional images were classified using a convolutional neural network (CNN) deep learning model, resulting in an accuracy rate of 99.93%. Upon examination of the results and graphs obtained in the study, it becomes evident that the proposed model demonstrates remarkable efficacy in classifying the 8-class dataset extracted from the AWID3 dataset. Moreover, the study serves to assess the effectiveness of feature selection in the new dataset comprising 8 classes and 64 features extracted from the AWID3 dataset.

Özet

802.11 protokolü, Wi-Fi teknolojisinin temelini oluşturmakta olup bilgisayarlar, akıllı telefonlar ve diğer cihazların kablosuz yerel alan ağları (WLAN) üzerinden birbirleriyle iletişim kurmasını sağlamaktadır. Özellikle Nesnelerin İnterneti (IoT) teknolojilerinin yaygınlaşmasıyla birlikte Wi-Fi ağları modern bağlantı altyapısının vazgeçilmez bir bileşeni haline gelmiştir. Ancak bu durum beraberinde çok sayıda güvenlik açığını da ortaya çıkarmıştır. Bu çalışmada, kablosuz ağlara yönelik siber saldırıların tespiti için evrişimsel sinir ağı (CNN) tabanlı bir yöntem önerilmiştir. Önerilen yöntemin performansı, AWID3 (Aegean Wi-Fi Intrusion Dataset) veri setine ait Wireshark (PCAP) dosyaları kullanılarak değerlendirilmiştir. Deauthentication, disassociation, (re)association, rogue access point (AP), KRACK, KR00k ve evil twin saldırılarına ait veriler Tshark yazılımı ile 64 özelliğe ayrıştırılmıştır. Ayrıca normal sınıfa ait PCAP verileri de aynı şekilde işlenmiştir. Elde edilen 64 özellikten en etkili 36’sı InfoGain öznitelik değerlendirme algoritması ile seçilmiştir. Otuz altı özellikten oluşan her veri satırı 6x6 boyutunda ve tek kanallı (gri seviye) görüntülere dönüştürülmüştür. Son aşamada, 6x6x1 boyutlu bu görüntüler CNN tabanlı derin öğrenme modeli ile sınıflandırılmış ve %99,93 doğruluk oranı elde edilmiştir. Elde edilen bulgular, önerilen modelin AWID3 veri setinden türetilen 8 sınıflı veri üzerinde yüksek başarıyla sınıflandırma gerçekleştirdiğini göstermektedir. Ayrıca çalışma, 64 özellikten 36 özelliğe indirgenen veri setinde öznitelik seçiminin model performansı üzerindeki etkisini ortaya koymaktadır.

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Cite This Article

Ceylan, M. A., Kılınçer, İ. F., Ertam, F. (2026). CNN Based Cyber Defense Architecture for 802.11 Networks. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 42 Sayı: 2. https://doi.org/10.65520/erciyesfen.1934250

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References (48)

  1. M. Çakmak, Z. Albayrak, and C. Torun, “Performance comparison of queue management algorithms in lte networks using NS-3 simulator,” Teh. Vjesn., 2021, doi: 10.17559/TV-20200411071703.
  2. Z. ALBAYRAK, H. MUSA, and M. ÇAKMAK, “Performance Evaluation of WBANs MAC Protocols in Different dBm and OMNet++,” J. Intell. Syst. Theory Appl., 2021, doi: 10.38016/jista.758312.
  3. S. Dwivedi, “Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm,” Cogn. Syst. Res., 2021, doi: 10.1016/j.cogsys.2021.04.003.
  4. E. Mahdavi, A. Fanian, A. Mirzaei, and Z. Taghiyarrenani, “ITL-IDS: Incremental Transfer Learning for Intrusion Detection Systems,” Knowledge-Based Syst., 2022, doi: 10.1016/j.knosys.2022.109542.
  5. M. Mohammadi et al., “A comprehensive survey and taxonomy of the SVM-based intrusion detection systems,” Journal of Network and Computer Applications. 2021, doi: 10.1016/j.jnca.2021.102983.
  6. P. Reddy and N. Shariff, “An anomaly-based intrusion detection system using recursive feature elimination technique for improved attack detection,” Theor. Comput. Sci., vol. 1, pp. 1–9, 2022, doi: 10.1016/j.tcs.2022.07.030.
  7. F. Nie, W. Liu, G. Liu, and B. Gao, “M2VT-IDS: A multi-task multi-view learning architecture for designing IoT intrusion detection system,” Internet of Things (Netherlands), vol. 25, no. January, p. 101102, 2024, doi: 10.1016/j.iot.2024.101102.
  8. E. Caville, W. W. Lo, S. Layeghy, and M. Portmann, “Anomal-E: A self-supervised network intrusion detection system based on graph neural networks,” Knowledge-Based Syst., 2022, doi: 10.1016/j.knosys.2022.110030.
  9. N. O. Aljehane, H. A. Mengash, S. B. H. Hassine, F. A. Alotaibi, A. S. Salama, and S. Abdelbagi, “Optimizing intrusion detection using intelligent feature selection with machine learning model,” Alexandria Eng. J., vol. 91, no. January, pp. 39–49, 2024, doi: 10.1016/j.aej.2024.01.073.
  10. M. Sangeen, N. A. Bhatti, K. Kifayat, A. A. Alsadhan, and H. Wang, “Blind-trust: Raising awareness of the dangers of using unsecured public Wi-Fi networks,” Comput. Commun., 2023, doi: 10.1016/j.comcom.2023.07.011.
  11. R. G. Brody, K. Gonzales, and D. Oldham, “Wi-Fi Hotspots: Secure or Ripe for Fraud?,” J. Forensic Investig. Account., 2013.
  12. S. Wang, B. Li, M. Yang, and Z. Yan, “Intrusion detection for wifi network: A deep learning approach,” 2019, doi: 10.1007/978-3-030-06158-6_10.
  13. Vrizlynn L. L. Thing, “IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach,” IEEE Wirel. Commun. Netw. Conf., 2017.
  14. T. Wang, D. Yang, S. Zhang, Y. Wu, and S. Xu, “Wi-alarm: Low-cost passive intrusion detection using WiFi,” Sensors (Switzerland), 2019, doi: 10.3390/s19102335.
  15. Z. Tian, Y. Li, M. Zhou, and Z. Li, “WiFi-Based Adaptive Indoor Passive Intrusion Detection,” 2018, doi: 10.1109/ICDSP.2018.8631613.
  16. P. Satam and S. Hariri, “WIDS: An Anomaly Based Intrusion Detection System for Wi-Fi (IEEE 802.11) Protocol,” IEEE Trans. Netw. Serv. Manag., 2021, doi: 10.1109/TNSM.2020.3036138.
  17. Z. Salah and E. Abu Elsoud, “Enhancing Network Security: A Machine Learning-Based Approach for Detecting and Mitigating Krack and Kr00k Attacks in IEEE 802.11,” Futur. Internet, 2023, doi: 10.3390/fi15080269.
  18. Y. Zhou, G. Cheng, S. Jiang, and M. Dai, “Building an efficient intrusion detection system based on feature selection and ensemble classifier,” Comput. Networks, 2020, doi: 10.1016/j.comnet.2020.107247.
  19. L. M. Da Silva, V. M. Andreghetti, R. A. F. Romero, and K. R. L. J. C. Branco, “Analysis and Identification of Evil Twin Attack through Data Science Techniques Using AWID3 Dataset,” 2023, doi: 10.1145/3635638.3635665.
  20. M. E. Aminanto and K. Kim, “Detecting impersonation attack in wifi networks using deep learning approach,” 2017, doi: 10.1007/978-3-319-56549-1_12.
  21. Q. Duan, X. Wei, J. Fan, L. Yu, and Y. Hu, “CNN-based Intrusion Classification for IEEE 802.11 Wireless Networks,” 2020, doi: 10.1109/ICCC51575.2020.9345293.
  22. C. Kolias, G. Kambourakis, A. Stavrou, and S. Gritzalis, “Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset,” IEEE Commun. Surv. Tutorials, vol. 18, no. 1, pp. 184–208, 2016, doi: 10.1109/COMST.2015.2402161.
  23. S. Rezvy, Y. Luo, M. Petridis, A. Lasebae, and T. Zebin, “An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks,” 2019, doi: 10.1109/CISS.2019.8693059.
  24. M. E. Aminanto and K. Kim, “Improving Detection of Wi-Fi Impersonation by Fully Unsupervised Deep Learning,” 2018, doi: 10.1007/978-3-319-93563-8_18.
  25. E. Chatzoglou, G. Kambourakis, and C. Kolias, “Empirical Evaluation of Attacks against IEEE 802.11 Enterprise Networks: The AWID3 Dataset,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3061609.
  26. R. Cheema, D. Bansal, and S. Sofat, “Deauthentication/Disassociation Attack: Implementation and Security in Wireless Mesh Networks,” Int. J. Comput. Appl., 2011, doi: 10.5120/2901-3801.
  27. I. Attacks, M. Aye, C. Aung, and K. P. Thant, “IEEE 802.11 Attacks and Defenses,” pp. 186–191.
  28. İ. F. KILINÇER, F. ERTAM, and A. ŞENGÜR, “Automated Fake Access Point Attack Detection and Prevention System with IoT Devices,” Balk. J. Electr. Comput. Eng., 2020, doi: 10.17694/bajece.634104.
  29. T. Zhou, Z. Cai, B. Xiao, Y. Chen, and M. Xu, “Detecting Rogue AP with the Crowd Wisdom,” 2017, doi: 10.1109/ICDCS.2017.31.
  30. C. P. Kohlios and T. Hayajneh, “A comprehensive attack flow model and security analysis for Wi-Fi and WPA3,” Electron., 2018, doi: 10.3390/electronics7110284.
  31. “CVE-2019-15126.” https://nvd.nist.gov/vuln/detail/CVE-2019-15126.
  32. H. Gonzales, K. Bauer, J. Lindqvist, D. McCoy, and D. Sicker, “Practical defenses for evil twin attacks in 802.11,” 2010, doi: 10.1109/GLOCOM.2010.5684213.
  33. J. Park, J. Kim, B. B. Gupta, and N. Park, “Network Log-Based SSH Brute-Force Attack DetectionModel,” Comput. Mater. Contin., vol. 68, no. 1, pp. 887–901, 2021, doi: 10.32604/cmc.2021.015172.
  34. N. Hoque, D. K. Bhattacharyya, and J. K. Kalita, “Botnet in DDoS Attacks: Trends and Challenges,” IEEE Commun. Surv. Tutorials, 2015, doi: 10.1109/COMST.2015.2457491.
  35. M. Anagnostopoulos, S. Lagos, and G. Kambourakis, “Large-scale empirical evaluation of DNS and SSDP amplification attacks,” J. Inf. Secur. Appl., 2022, doi: 10.1016/j.jisa.2022.103168.
  36. I. S. Crespo-Martínez, A. Campazas-Vega, Á. M. Guerrero-Higueras, V. Riego-DelCastillo, C. Álvarez-Aparicio, and C. Fernández-Llamas, “SQL injection attack detection in network flow data,” Comput. Secur., 2023, doi: 10.1016/j.cose.2023.103093.
  37. M. Ahmed et al., “PhishCatcher: Client-Side Defense Against Web Spoofing Attacks Using Machine Learning,” IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3287226.
  38. D. Akgun, S. Hizal, and U. Cavusoglu, “A new DDoS attacks intrusion detection model based on deep learning for cybersecurity,” Comput. Secur., 2022, doi: 10.1016/j.cose.2022.102748.
  39. N. Li, A. Iosifidis, and Q. Zhang, “Collaborative edge computing for distributed CNN inference acceleration using receptive field-based segmentation,” Comput. Networks, 2022, doi: 10.1016/j.comnet.2022.109150.
  40. S. Wang, W. Xu, and Y. Liu, “Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things,” Comput. Networks, 2023, doi: 10.1016/j.comnet.2023.109982.
  41. K. Hassini, S. Khalis, O. Habibi, M. Chemmakha, and M. Lazaar, “An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things,” Knowledge-Based Syst., vol. 294, no. April, p. 111785, 2024, doi: 10.1016/j.knosys.2024.111785.
  42. F. Özyurt, T. Tuncer, E. Avci, M. Koç, and İ. Serhatlioğlu, “A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network,” Arab. J. Sci. Eng., 2019, doi: 10.1007/s13369-018-3454-1.
  43. “The AWID3 Dataset.” https://icsdweb.aegean.gr/awid/awid3.
  44. G. A. Pradipta, R. Wardoyo, A. Musdholifah, I. N. H. Sanjaya, and M. Ismail, “SMOTE for Handling Imbalanced Data Problem: A Review,” 2021, doi: 10.1109/ICIC54025.2021.9632912.
  45. A. B. Mohammed, E. Chamseddine, and A. ElAdel, “Enhancing real-time IoT intrusion detection using KAN-based frameworks with SMOTE,” J. Netw. Comput. Appl., vol. 249, no. February, p. 104461, 2026, doi: 10.1016/j.jnca.2026.104461.
  46. H. Zhang, L. Huang, C. Q. Wu, and Z. Li, “An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset,” Comput. Networks, 2020, doi: 10.1016/j.comnet.2020.107315.
  47. N. Omer, A. H. Samak, A. I. Taloba, and R. M. Abd El-Aziz, “A novel optimized probabilistic neural network approach for intrusion detection and categorization,” Alexandria Eng. J., 2023, doi: 10.1016/j.aej.2023.03.093.
  48. G. A. Pradipta, R. Wardoyo, A. Musdholifah, I. N. H. Sanjaya, and M. Ismail, “SMOTE for Handling Imbalanced Data Problem: A Review,” 2021, doi: 10.1109/ICIC54025.2021.9632912.