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An efficient botnet detection approach based on feature learning and classification

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  • A., Vishal, & Sonawane, S. S. (2016). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications, 139(11), 5–15. https://doi.org/10.5120/ijca2016908625.
  • Abd El-Latif, A. A., Abd-El-Atty, B., Mazurczyk, W., Fung, C., & Venegas-Andraca, S. E. (2020). Secure data encryption based on quantum walks for 5g internet of things scenario. IEEE Transactions on Network and Service Management, 17(1), 118–131. https://doi.org/10.1109/TNSM.2020.2969863
  • Abou Daya, A., Salahuddin, M. A., Limam, N., & Boutaba, R. (2019). A graphbased machine learning approach for bot detection. In Proceedings of the IFIP/IEEE Symposium on Integrated Network and Service Management (IM).
  • Ahmed, A. A., Jantan, A., & Wan, T.-C. (2016). Filtration model for detecting malicious traffic in large-scale networks. Computer Commuications, 82, 59–70. https://doi.org/10.1016/j.comcom.2015.10.012
  • Ali, S. T., Mc Corry, P., Lee, P. H. J., & Hao, F. (2017). Zombie Coin 2.0: Managing next-generation Botnets using Bitcoin. International Journal of Information Security, 17(4), 1–12. https://doi.org/10.3390/su9122157
  • Al-Jarrah, O. Y., Alhussein, O., Yoo, P. D., Muhaidat, S., Taha, K., & Kim, K. (2016). Data randomization and cluster-based partitioning for botnet intrusion detection. IEEE Transactions on Cybernetics, 46(8), 1796–1806. https://doi.org/10.1109/TCYB.2015.2490802
  • AsSadhan, B., Bashaiwth, A., Al-Muhtadi, J., & Alshebeili, S. (2017). Analysis of P2P, IRC and HTTP traffic for botnets detection. Peer-to-Peer Networking and Applications, 11(5), 848–861. https://doi.org/10.1007/s12083-017-0586-0
  • Babak, R., Roberto, P., Andrea, L., & Kang, L. (2014). Peerrush: Mining for unwanted P2P traffic. Journal of Information Security and Applications, 19(3), 194–208. https://doi.org/10.1016/j.jisa.2014.03.002
  • Blaise, A., Bouet, M., Conan, V., & Secci, S. (2020). BotFP: FingerPrints clustering for bot detection. In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS).
  • Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018). A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. Journal of Internet Services and Applications, 9(1), 2018. https://doi.org/10.1186/s13174-018-0087-2
  • Cao, N., Li, G., Zhu, P., Sun, Q., Wang, Y., Li, J., & Zhao, Y. (2018). Handling the adversarial attacks. Journalof Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-018-0714-6
  • Chen, W., Luo, X., & Zincir-Heywood, A. N. (2017). Exploring a service-based normal behaviour profiling system for botnet detection. In Proceedings of the IFIP/IEEE Symposium on Integrated Network and Service Management (IM).
  • Chowdhury, S., Khanzadeh, M., Akula, R., Zhang, F., Zhang, S., Medal, H., Marufuzzaman, M., & Bian, L. (2017). Botnet detection using graph-based feature clustering. Journal of Big Data, 4(1). https://doi.org/10.1186/s40537-017-0074-7
  • Cui, Z., Xue, F., Cai, X., Cao, Y., Wang, G.-G., & Chen, J. (2018). Detection of malicious code variants based on deep learning. IEEE Transactions on Industrial Informatics, 14(7), 3187–3196. https://doi.org/10.1109/TII.2018.2822680
  • D'Alconzo, A., Drago, I., Morichetta, A., Mellia, M., & Casas, P. (2019). A survey on big data for network traffic monitoring and analysis. IEEE Transactions on Network and Service Management, 16(3), 800–813. https://doi.org/10.1109/TNSM.2019.2933358
  • Dhaya, M. A., & Ravi, R. (2020). Multi-feature behaviour approximation model based efficient botnet detection to mitigate financial frauds. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01677–w
  • Fong, E. H., Catagnus, R. M., Brodhead, M. T., Quigley, S., & Field, S. (2016). Developing the cultural awareness skills of behavior analysts. Behavior Analysis in Practice, 9(1), 84–94. https://doi.org/10.1007/s40617-016-0111-6.
  • Grill, M., Pevný, T., & Rehak, M. (2017). Reducing false positives of network anomaly detection by local adaptive multivariate smoothing. Journal of Computer and System Sciences, 83(1), 43–57. https://doi.org/10.1016/j.jcss.2016.03.007
  • Hang, H., Wei, X., Faloutsos, M., & Eliassi-Rad, T. (2013, May). Entelecheia: Detecting P2P botnets in their waiting stage. In IFIP networking conference, (pp. 1–9). IEEE.
  • Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2015). Botnet in DDoS attacks: Trends and challenges. IEEE Communication Surveys & Tutorials, 17(4), 2242–2270. https://doi.org/10.1109/COMST.2015.2457491
  • Jha, S., Kumar, R., Son, L., Abdel-Basset, M., Priyadarshini, I., Sharma, R., & Long, H. (2019). Deep learning approach for software maintainability metrics prediction. IEEE Access, 7, 61840–61855. https://doi.org/10.1109/ACCESS.2019.2913349
  • Marnerides, A. K., & Mauthe, A. U. (2016). Analysis and characterization of botnet scan traffic. In 2016 International conference on computing, networking and communications (ICNC), pp. 1–7. IEEE. https://doi.org/10.1109/ICCNC.2016.7440627
  • Moodi, M., & Ghazvini, M. (2019). A new method for assigning appropriate labels to create a 28 Standard Android Botnet Dataset (28-SABD). Journal of Ambient Intelligence and Humanized Computing, 10(11), 4579–4593. https://doi.org/10.1007/s12652-018-1140-5
  • Nour, M., & Slay, J. (2016). The evaluation of network anomaly detection systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Information Security Journal: A Global Perspective, 25(13), 18–31. https://doi.org/10.1080/19393555.2015.1125974
  • Reza, M., Sobouti, M., Raouf, S., & Javidan, R. (2016). Network traffic classification using Machine Learning techniques over software-defined networks. International Journal of Advanced Computer Science Applications, 8(7), 220–225. https://doi.org/10.14569/IJACSA.2017.080729
  • Shu, Z., Wan, J., Li, D., Lin, J., Vasilakos, A. V., & Imran, M. (2016). Security in software-defined networking: Threats and countermeasures. Mobile Networking Applications, 21(5), 764–776. https://doi.org/10.1007/s11036-016-0676-x
  • Sriram, A., Zbontar, J., Murrell, T., Defazio, A., Zitnick, C. L., Yakubova, N., Knoll, F., & Johnson, P. (2020, October). End-to-end variational networks for accelerated MRI reconstruction. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 64–73). Springer.
  • Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2017). Applying convolutional neural network for network intrusion detection. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, 1222–1228. https://doi.org/10.1109/ICACCI.2017.8126009
  • Wai, F. K., Lilei, Z., Wai, W. K., Le, S., & Thing, V. L. L. (2018). Automated Botnet traffic detection via Machine Learning. TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South) (pp. 0038-0043). https://doi.org/10.1109/TENCON.2018.8650466
  • Wang, J., & Paschalidis, I. C. (2017). Botnet detection based on anomaly and community detection. IEEE Transactions on Control of Network Systems, 4(2), 392–404. https://doi.org/10.1109/TCNS.2016.2532804
  • Wang, P., Wu, L., Aslam, B., & Zou, C. (2015). Analysis of peer-to-peer botnet attacks and defences. In D. Król, D. Fay, & B. Gabrys’ (Eds.), Propagation phenomena in real-world networks, vol 85 (pp. 183–214). Springer.
  • Zhang, J., Perdisci, R., Lee, W., Luo, X., & Sarfraz, U. (2014). Building a scalable system for stealthy P2P-Botnet detection. IEEE Trans. Inf. Forensics Secure, 9(1), 27–38. https://doi.org/10.1109/TIFS.2013.2290197
  • Zhao, D., Traore, I., Sayed, B., Lu, W., Saad, S., Ghorbani, A., & Garant, D. (2013). Botnet detection based on traffic behaviour analysis and flow intervals. Computers & Security, 39, 2–16. https://doi.org/10.1016/j.cose.2013.04.007

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