بررسی یک روش ترکیبی جدید سیستم تشخیص نفوذ بر روی مجموعه داده های مختلف

[1]    S. Akbar, K. N. Rao, & J. Chandulal, "Intrusion detection system methodologies based on data analysis," International Journal of Computer Applications, vol. 5, no. 2, pp. 10-20, 2010.

[2]    T. Ahmad & M. N. Aziz, "Data preprocessing and feature selection for machine learning intrusion detection systems," ICIC Express Lett, vol. 13, no. 2, pp. 93-101, 2019.

[3]    B. A. Tama, M. Comuzzi, & K.-H. Rhee, "TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system," IEEE Access, vol. 7, pp. 94497-94507, 2019.

[4]    Z. Chiba, N. Abghour, K. Moussaid, & M. Rida, "Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms," computers & security, vol. 86, pp. 291-317, 2019.

[5]    H. Liu & B. Lang, "Machine learning and deep learning methods for intrusion detection systems: A survey," applied sciences, vol. 9, no. 20, p. 4396, 2019.

[6]    R. Koch, M. Golling, & G. D. Rodosek, "Towards comparability of intrusion detection systems: New data sets," in TERENA Networking Conference, vol. 7, 2014.

[7]    A. Shiravi, H. Shiravi, M. Tavallaee, & A. A. Ghorbani, "Toward developing a systematic approach to generate benchmark datasets for intrusion detection," computers & security, vol. 31, no. 3, pp. 357-374, 2012.

[8]    H. Hindy et al., "A taxonomy and survey of intrusion detection system design techniques, network threats and datasets," 2018.

[9]    A. Thakkar & R. Lohiya, "A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions," Artificial Intelligence Review, pp. 1-111, 2021.

[10]  I. S. Thaseen, C. A. Kumar, & A. Ahmad, "Integrated intrusion detection model using chi-square feature selection and ensemble of classifiers," Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3357-3368, 2019.

[11]  Y. Shen, K. Zheng, C. Wu, M. Zhang, X. Niu, & Y. Yang, "An ensemble method based on selection using bat algorithm for intrusion detection," The Computer Journal, vol. 61, no. 4, pp. 526-538, 2018.

[12]  J. Kennedy, "Swarm intelligence," in Handbook of nature-inspired and innovative computing: Springer, pp. 187-219, 2006.

[13]  C. Khammassi & S. Krichen, "A GA-LR wrapper approach for feature selection in network intrusion detection," computers & security, vol. 70, pp. 255-277, 2017.

[14]  S. Parsa & s. H. R. Aarabi, "A New Approach to Network Intrusion Detection Based on Hybrid Methods," Scientific Journal of Electronic and Cyber Defense, vol. 5, no. 3, pp. 79-93, 2017. (Persian in)

[15]  M. Abdullah, A. Alshannaq, A. Balamash, & S. Almabdy, "Enhanced intrusion detection system using feature selection method and ensemble learning algorithms," International Journal of Computer Science and Information Security (IJCSIS), vol. 16, no. 2, pp. 48-55, 2018.

[16]  I. F. Kilincer, F. Ertam, & A. Sengur, "Machine learning methods for cyber security intrusion detection: Datasets and comparative study," Computer Networks, vol. 188, p. 107840, 2021.

[17]  W. Wang, X. Zhang, & S. Gombault, "Constructing attribute weights from computer audit data for effective intrusion detection," Journal of Systems and Software, vol. 82, no. 12, pp. 1974-1981, 2009.

[18]  F. Salo, A. B. Nassif, & A. Essex, "Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection," Computer Networks, vol. 148, pp. 164-175, 2019.

[19]  M. A. Khan, M. Karim, & Y. Kim, "A scalable and hybrid intrusion detection system based on the convolutional-LSTM network," Symmetry, vol. 11, no. 4, p. 583, 2019.

[20]  Y. Zhong et al., "HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning," Computer Networks, vol. 169, p. 107049, 2020.

[21]  I. Syarif, E. Zaluska, A. Prugel-Bennett, & G. Wills, "Application of bagging, boosting and stacking to intrusion detection," in International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 593-602, Springer. 2012.

[22]  A. J. Malik, W. Shahzad, & F. A. Khan, "Network intrusion detection using hybrid binary PSO and random forests algorithm," Security and Communication Networks, vol. 8, no. 16, pp. 2646-2660, 2015.

[23]  N. T. Pham, E. Foo, S. Suriadi, H. Jeffrey, & H. F. M. Lahza, "Improving performance of intrusion detection system using ensemble methods and feature selection," in Proceedings of the Australasian Computer Science Week Multiconference, pp. 1-6, 2018.

[24]  A. Maroosi, E. Zabbah, & H. Ataei Khabbaz, "Network Intrusion Detection using a combination of artificial neural networks in a hierarchical manner," Scientific Journal of Electronic and Cyber Defense, vol. 8, no. 1, pp. 89-99, 2020. (in Persian)

[25]  V. Bukhtoyarov & V. Zhukov, "Ensemble-Distributed Approach in Classification Problem Solution for Intrusion Detection Systems," Cham, 2014: Springer International Publishing, in Intelligent Data Engineering and Automated Learning – IDEAL 2014, pp. 255-265, 2014.

[26]  N. Mohd, A. Singh, and H. S. Bhadauria, "A Novel SVM Based IDS for Distributed Denial of Sleep Strike in Wireless Sensor Networks," Wireless Personal Communications, vol. 111, no. 3, pp. 1999-2022, 2020.

[27]  M. Begli & F. Derakhshan, "A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems," arXiv preprint arXiv:2104.06498, 2021.

[28]  H. Zolfi, H. Ghorbani, & M. H. Ahmadzadegan, "Investigation and classification of cyber-crimes through IDS and SVM algorithm," in 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 180-187, 2019.

[29]  M. Masdari & H. Khezri, "A survey and taxonomy of the fuzzy signature-based Intrusion Detection Systems," Applied Soft Computing, pp. 106-301, 2020.

[30]  S. Manimurugan, A.-q. Majdi, M. Mohmmed, C. Narmatha, & R. Varatharajan, "Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system," Microprocessors and Microsystems, vol. 79, p. 103261, 2020.

[31]  M. Negnevitsky, "Artificial intelligence : a guide to intelligent systems / Michael Negnevitsky," (no. Accessed from https://nla.gov.au/nla.cat-vn3803044), New York: Addison-Wesley, 2005.

[32]  J.S. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE transactions on systems, man, and cybernetics, vol. 23, no. 3, pp. 665-685, 1993.

[33]  F. Kuang, W. Xu, & S. Zhang, "A novel hybrid KPCA and SVM with GA model for intrusion detection," Applied Soft Computing, vol. 18, pp. 178-184, 2014.

[34]  S.-J. Horng et al., "A novel intrusion detection system based on hierarchical clustering and support vector machines," Expert Systems with Applications, vol. 38, no. 1, pp. 306-313, 2011.

[35]  R. Eberhart & J. Kennedy, "A new optimizer using particle swarm theory," in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39-43, 1995, IEEE.

[36]  R. K. Vigneswaran, R. Vinayakumar, K. Soman, & P. Poornachandran, "Evaluating shallow and deep neural networks for network intrusion detection systems in cyber security," in 2018 9th International conference on computing, communication and networking technologies (ICCCNT), pp. 1-6, 2018, IEEE.

[37]  C. Brown, A. Cowperthwaite, A. Hijazi, & A. Somayaji, "Analysis of the 1999 darpa/lincoln laboratory ids evaluation data with netadhict," in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1-7, 2009, IEEE.

[38]  R. Bala & R. Nagpal, "A review on kdd cup99 and nsl nsl-kdd dataset," International Journal of Advanced Research in Computer Science, vol. 10, no. 2, 2019.

[39]  M. Tavallaee, E. Bagheri, W. Lu, & A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1-6, 2009, IEEE.

[40]  H. Ji, D. Kim, D. Shin, & D. Shin, "A study on comparison of KDD CUP 99 and NSL-KDD using artificial neural network," in Advances in computer science and ubiquitous computing: Springer, pp. 452-457, 2017.

[41]  I. Sharafaldin, A. Gharib, A. H. Lashkari, & A. A. Ghorbani, "Towards a reliable intrusion detection benchmark dataset," Software Networking, vol. 2018, no. 1, pp. 177-200, 2018.

[42]  O. Yavanoglu & M. Aydos, "A review on cyber security datasets for machine learning algorithms," in 2017 IEEE international conference on big data (big data), pp. 2186-2193, 2017.

[43]  S. Peisert et al., "Lbnl open power data," 2017.

[44]  B. Sangster et al., "Toward Instrumenting Network Warfare Competitions to Generate Labeled Datasets," in CSET, 2009.

[45]  M. Sato, H. Yamaki, & H. Takakura, "Unknown attacks detection using feature extraction from anomaly-based ids alerts," in 2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet, pp. 273-277, 2012. 

[46]  R. Chitrakar & C. Huang, "Anomaly based intrusion detection using hybrid learning approach of combining k-medoids clustering and naive Bayes classification," in 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1-5, 2012.

[47]  J. Song, H. Takakura, Y. Okabe, M. Eto, D. Inoue, & K. Nakao, "Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation," in Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, pp. 29-36, 2011. 

[48]  A. Sperotto, R. Sadre, F. Van Vliet, and A. Pras, "A labeled data set for flow-based intrusion detection," in International Workshop on IP Operations and Management, pp. 39-52, 2009.

[49]  "UMASS",http://traces.cs.umass.edu/index.php/Network/Network.

[50]  M. Nasr, A. Bahramali, and A. Houmansadr, "Deepcorr: Strong flow correlation attacks on Tor using deep learning," in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 1962-1976, 2018.

[51]  G. Bissias, B. N. Levine, M. Liberatore, and S. Prusty, "Forensic Identification of Anonymous Sources in OneSwarm," IEEE Transactions on Dependable and Secure Computing, vol. 14, no. 6, pp. 620-632, 2017.

[52]  G. Creech and J. Hu, "Generation of a new IDS test dataset: Time to retire the KDD collection," in 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 4487-4492, 2013.

[53]  T. Mouttaqi, T. Rachidi, and N. Assem, "Re-evaluation of combined Markov-Bayes models for host intrusion detection on the ADFA dataset," in 2017 Intelligent Systems Conference (IntelliSys), pp. 1044-1052, 2017.

[54]  Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), pp. 69-73, 1998.

[55]  A. Gharib, I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "An evaluation framework for intrusion detection dataset," in 2016 International Conference on Information Science and Security (ICISS), pp. 1-6, 2016.

[56]  M. Panda, A. Abraham, and M. R. Patra, "Discriminative multinomial naive bayes for network intrusion detection," in 2010 Sixth International Conference on Information Assurance and Security, pp. 5-10, 2010.

[57]  M. A. Salama, H. F. Eid, R. A. Ramadan, A. Darwish, and A. E. Hassanien, "Hybrid intelligent intrusion detection scheme," in Soft computing in industrial applications, pp. 293-303, 2011.

[58]  P. Gogoi, M. H. Bhuyan, D. Bhattacharyya, and J. K. Kalita, "Packet and flow based network intrusion dataset," in International Conference on Contemporary Computing, pp. 322-334, 2012.

[59]  E. De La Hoz, A. Ortiz, J. Ortega, and E. De la Hoz, "Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques," in International Conference on Hybrid Artificial Intelligence Systems, pp. 103-111, 2013.

[60]  Z. Tan, A. Jamdagni, X. He, P. Nanda, R. P. Liu, and J. Hu, "Detection of denial-of-service attacks based on computer vision techniques," IEEE transactions on computers, vol. 64, no. 9, pp. 2519-2553, 2014.

[61]  M. G. Raman, N. Somu, K. Kirthivasan, R. Liscano, and V. S. Sriram, "An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine," Knowledge-Based Systems, vol. 134, pp. 1-12, 2017.

[62]  H. Huang, R. S. Khalid, and H. Yu, "Distributed machine learning on smart-gateway network towards real-time indoor data analytics," in Data Science and Big Data: An Environment of Computational Intelligence, pp. 231-263, 2017.

[63]  R. Singh, H. Kumar, and R. Singla, "An intrusion detection system using network traffic profiling and online sequential extreme learning machine," Expert Systems with Applications, vol. 42, no. 22, pp. 8609-8624, 2015.

[64]  M. Jabbar and R. Aluvalu, "RFAODE: A novel ensemble intrusion detection system," Procedia computer science, vol. 115, pp. 226-234, 2017.

[65]  J. Bao, R. Li, Y. Liu, Y. Liu, and B. Shao, "Ionospheric anomaly detection to support the BDSBAS," IEEE Access, vol. 8,  pp. 1691-1704, 2019.

[66]  A. Bansal and S. Kaur, "Extreme gradient boosting based tuning for classification in intrusion detection systems," in International conference on advances in computing and data sciences, pp. 372-380, 2018.

[67]  M. Catillo, M. Rak, and U. Villano, "Discovery of DoS attacks by the ZED-IDS anomaly detector," Journal of High Speed Networks, vol. 25, no. 4, pp. 349-365, 2019.