• Açıkgözoğlu E. (2024). COMPARISON OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF DATA EXFILTRATION OVER DNS. Yalvaç Akademi Dergisi. 10.57120/yalvac.1507402. 9:2. (61-70).

    http://dergipark.org.tr/en/doi/10.57120/yalvac.1507402

  • Fahim A, Zhu S, Qian Z, Song C, Papalexakis E, Chakraborty S, Chan K, Yu P, Jaeger T and Krishnamurthy S. (2024). DNS Exfiltration Guided by Generative Adversarial Networks 2024 IEEE 9th European Symposium on Security and Privacy (EuroS&P). 10.1109/EuroSP60621.2024.00038. 979-8-3503-5425-6. (580-599).

    https://ieeexplore.ieee.org/document/10628994/

  • Nguyen T, Laborde R, Benzekri A, Oglaza A and Mounsif M. (2024). AutoRoC-DBSCAN: automatic tuning of DBSCAN to detect malicious DNS tunnels. Annals of Telecommunications. 10.1007/s12243-024-01025-5.

    https://link.springer.com/10.1007/s12243-024-01025-5

  • Chen H and Hu Z. Exploring Data Traceability Methods in Information Management Within Universities: An Action Research and Case Study Approach. IEEE Access. 10.1109/ACCESS.2024.3493860. 12. (175196-175217).

    https://ieeexplore.ieee.org/document/10746481/

  • Spathoulas G, Anagnostopoulos M, Papageorgiou K, Kavallieratos G and Theodoridis G. (2024). Improving DNS Data Exfiltration Detection Through Temporal Analysis. Ubiquitous Security. 10.1007/978-981-97-1274-8_9. (133-146).

    https://link.springer.com/10.1007/978-981-97-1274-8_9

  • Chougule M, K P, P. P A, Viswanathan S, Ravichandran K, Sethumadhavan M, Rahimi M and Gandomi A. (2023). Classifying DNS over HTTPS Malicious/Benign Traffic Using Deep Learning Models 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI). 10.1109/ISCMI59957.2023.10458486. 979-8-3503-5937-4. (1-5).

    https://ieeexplore.ieee.org/document/10458486/

  • Sobrero F, Clavarezza B, Ucci D and Bisio F. (2023). Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques. Journal of Cybersecurity and Privacy. 10.3390/jcp3040035. 3:4. (794-807).

    https://www.mdpi.com/2624-800X/3/4/35

  • Zhao L, Wang J, Liu S and Yang X. An adaptive multitask network for detecting the region of water leakage in tunnels. Journal of Intelligent & Fuzzy Systems. 10.3233/JIFS-224315. (1-15).

    https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-224315

  • Zhang S, Han Z and Jiang K. (2023). Detection of Data Leakage Based on DNS Traffic 2023 IEEE 5th International Conference on Power, Intelligent Computing and Systems (ICPICS). 10.1109/ICPICS58376.2023.10235404. 979-8-3503-3344-2. (33-38).

    https://ieeexplore.ieee.org/document/10235404/

  • Mitsuhashi R, Jin Y, Iida K, Shinagawa T and Takai Y. Malicious DNS Tunnel Tool Recognition Using Persistent DoH Traffic Analysis. IEEE Transactions on Network and Service Management. 10.1109/TNSM.2022.3215681. 20:2. (2086-2095).

    https://ieeexplore.ieee.org/document/9924534/

  • Liu X, Mao W, Wang A, Li Z, Xue H, Zhang Y, Lin J, Yang X, Chen Z and Sun B. (2023). DNS Tunnel Detection for Low Throughput Data Exfiltration via Time-Frequency Domain Analysis ICC 2023 - IEEE International Conference on Communications. 10.1109/ICC45041.2023.10279472. 978-1-5386-7462-8. (2331-2337).

    https://ieeexplore.ieee.org/document/10279472/

  • Zhao L, Wang J, Liu S and Yang X. (2023). An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels. Applied Sciences. 10.3390/app13106231. 13:10. (6231).

    https://www.mdpi.com/2076-3417/13/10/6231

  • Jiarong W, Zhongtian L, Fazhi Q, Tian Y, Jiahao L and Caiqiu Z. (2023). Unsupervised Anomaly Detection Method Based on DNS Log Data. Artificial Intelligence in China. 10.1007/978-981-99-1256-8_5. (32-43).

    https://link.springer.com/10.1007/978-981-99-1256-8_5

  • Han D, Dong P, Li N, Cui X, Diao J, Wang Q, Du D and Liu Y. (2022). DCC-Find: DNS Covert Channel Detection by Features Concatenation-Based LSTM 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). 10.1109/TrustCom56396.2022.00050. 978-1-6654-9425-0. (307-314).

    https://ieeexplore.ieee.org/document/10063732/

  • Ahmed J, Gharakheili H, Russell C and Sivaraman V. Automatic Detection of DGA-Enabled Malware Using SDN and Traffic Behavioral Modeling. IEEE Transactions on Network Science and Engineering. 10.1109/TNSE.2022.3173591. 9:4. (2922-2939).

    https://ieeexplore.ieee.org/document/9772333/

  • Mummadi A, Yadav B, Sadhwika R and Shitharth S. (2022). An Appraisal of Cyber-Attacks and Countermeasures Using Machine Learning Algorithms. Artificial Intelligence and Data Science. 10.1007/978-3-031-21385-4_3. (27-40).

    https://link.springer.com/10.1007/978-3-031-21385-4_3

  • Wang Y, Zhou A, Liao S, Zheng R, Hu R and Zhang L. (2022). A comprehensive survey on DNS tunnel detection. Computer Networks: The International Journal of Computer and Telecommunications Networking. 197:C. Online publication date: 9-Oct-2021.

    https://doi.org/10.1016/j.comnet.2021.108322

  • Ishikura N, Kondo D, Vassiliades V, Iordanov I and Tode H. DNS Tunneling Detection by Cache-Property-Aware Features. IEEE Transactions on Network and Service Management. 10.1109/TNSM.2021.3078428. 18:2. (1203-1217).

    https://ieeexplore.ieee.org/document/9426926/

  • Bai H, Liu G, Zhai J, Liu W, Ji X, Yang L and Dai Y. (2020). Refined identification of hybrid traffic in DNS tunnels based on regression analysis. ETRI Journal. 10.4218/etrij.2019-0299. 43:1. (40-52). Online publication date: 1-Feb-2021.

    https://onlinelibrary.wiley.com/doi/10.4218/etrij.2019-0299

  • Bai H, Liu W, Liu G, Dai Y and Huang S. Application Behavior Identification in DNS Tunnels Based on Spatial-Temporal Information. IEEE Access. 10.1109/ACCESS.2021.3085500. 9. (80639-80653).

    https://ieeexplore.ieee.org/document/9445115/

  • Mitsuhashi R, Satoh A, Jin Y, Iida K, Shinagawa T and Takai Y. (2021). Identifying Malicious DNS Tunnel Tools from DoH Traffic Using Hierarchical Machine Learning Classification. Information Security. 10.1007/978-3-030-91356-4_13. (238-256).

    https://link.springer.com/10.1007/978-3-030-91356-4_13

  • Wu K, Zhang Y and Yin T. (2020). FTPB: A Three-Stage DNS Tunnel Detection Method Based on Character Feature Extraction 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). 10.1109/TrustCom50675.2020.00044. 978-1-6654-0392-4. (250-258).

    https://ieeexplore.ieee.org/document/9343167/

  • MontazeriShatoori M, Davidson L, Kaur G and Habibi Lashkari A. (2020). Detection of DoH Tunnels using Time-series Classification of Encrypted Traffic 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00026. 978-1-7281-6609-4. (63-70).

    https://ieeexplore.ieee.org/document/9251211/

  • Pavlov S, Gromova A, Itkin I, Mamedov M, Libkov A, Novikov A and Tsymbalov E. (2020). Building a Classification System for Failed Test Reports: Industrial Experience 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). 10.1109/AITEST49225.2020.00021. 978-1-7281-6984-2. (91-98).

    https://ieeexplore.ieee.org/document/9176769/

  • Wu K, Zhang Y and Yin T. (2020). TDAE: Autoencoder-based Automatic Feature Learning Method for the Detection of DNS tunnel ICC 2020 - 2020 IEEE International Conference on Communications (ICC). 10.1109/ICC40277.2020.9149162. 978-1-7281-5089-5. (1-7).

    https://ieeexplore.ieee.org/document/9149162/

  • Liu Y and Gou X. (2020). Research on Application of Feature Analysis Method in DNS Tunnel Detection. Journal of Physics: Conference Series. 10.1088/1742-6596/1575/1/012069. 1575. (012069). Online publication date: 1-Jun-2020.

    https://iopscience.iop.org/article/10.1088/1742-6596/1575/1/012069

  • Chavis J, Buczak A, Kunz A, Rubin A and Watkins L. (2020). A Capability for Autonomous IoT System Security: Pushing IoT Assurance to the Edge 2020 IEEE Security and Privacy Workshops (SPW). 10.1109/SPW50608.2020.00058. 978-1-7281-9346-5. (256-261).

    https://ieeexplore.ieee.org/document/9283879/

  • Chavis J, Buczak A, Rubin A and Watkins L. (2020). Connected Home Automated Security Monitor (CHASM): Protecting IoT Through Application of Machine Learning 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). 10.1109/CCWC47524.2020.9031162. 978-1-7281-3783-4. (0684-0690).

    https://ieeexplore.ieee.org/document/9031162/

  • Liu Y and An C. (2019). Cost Efficient Internet Path Tracking Based on Routing Changes Prediction 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). 10.1109/IPCCC47392.2019.8958758. 978-1-7281-1025-7. (1-8).

    https://ieeexplore.ieee.org/document/8958758/

  • Liu C, Dai L, Cui W and Lin T. (2019). A Byte-level CNN Method to Detect DNS Tunnels 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). 10.1109/IPCCC47392.2019.8958714. 978-1-7281-1025-7. (1-8).

    https://ieeexplore.ieee.org/document/8958714/

  • Almusawi A, Amintoosi H and Di Pietro R. (2018). DNS Tunneling Detection Method Based on Multilabel Support Vector Machine. Security and Communication Networks. 2018. Online publication date: 1-Jan-2018.

    https://doi.org/10.1155/2018/6137098

  • Buczak A, Berman D, Yen S, Watkins L, Duong L and Chavis J. Using sequential pattern mining for common event format (CEF) cyber data. Proceedings of the 12th Annual Conference on Cyber and Information Security Research. (1-4).

    https://doi.org/10.1145/3064814.3064822

  • Watkins L, Beck S, Zook J, Buczak A, Chavis J, Robinson W, Morales J and Mishra S. (2017). Using semi-supervised machine learning to address the Big Data problem in DNS networks 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). 10.1109/CCWC.2017.7868376. 978-1-5090-4228-9. (1-6).

    http://ieeexplore.ieee.org/document/7868376/

  • Shafieian S, Smith D and Zulkernine M. (2017). Detecting DNS Tunneling Using Ensemble Learning. Network and System Security. 10.1007/978-3-319-64701-2_9. (112-127).

    http://link.springer.com/10.1007/978-3-319-64701-2_9