[1] Nikolskaya K Y, Ivanov S A, Golodov V A, et al. Review of modern DDoS-attacks, methods and means of counteraction[C]//2017 International Conference “Quality Management, Transport and Information Security, Information Technologies”(IT&QM&IS), Piscataway: IEEE, 2017: 87-89.
[2] Venkatesan S, Albanese M, Amin K, et al. A moving target defense approach to mitigate DDoS attacks against proxy-based architectures[C]//Communications & Network Security, Piscataway: IEEE, 2016: 196-206.
[3] Sattar D, Matrawy A. Towards secure slicing: Using slice isolation to mitigate DDoS attacks on 5G core network slices[J]. arXiv preprint arXiv:1901.01443, 2019.
[4] Yuan X, Li C, Li X. Deepdefense: Identifying DDoS attack via deep learning[C]//2017 IEEE International Conference on Smart Computing (SMARTCOMP), Piscataway: IEEE, 2017: 1-8.
[5] Kotenko I, Ulanov A. Agent-based simulation of DDOS attacks and defense mechanisms[J]. International Journal of Computing, 2014, 4(2): 113-123.
[6] Kao C N, Chang Y C, Huang N F, et al. A predictive zero-day network defense using long-term port-scan recording[C]//2015 IEEE Conference on Communications and Network Security (CNS), Piscataway: IEEE, 2015: 695-696.
[7] Fan C I, Hsiao H W, Chou C H, et al. Malware detection systems based on API Log data mining[C]//Computer Software & Applications Conference, Piscataway: IEEE, 2015, 3: 255-260.
[8] Khalaf B A, Mostafa S A, Mustapha A, et al. Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods[J]. IEEE Access, 2019, 7: 51691-51713.
[9] Prasad K M, Reddy D A R M, Rao D K V. DoS and DDoS attacks: Defense, detection and traceback mechanisms——A survey[J]. Global Journal of Computer Science and Technology, 2014, 14(7): 1-19.
[10]Nadiammai G V, Hemalatha M. Effective approach toward intrusion detection system using data mining techniques[J]. Egyptian Informatics Journal, 2014, 15(1): 37-50.
[11]Aggarwal A, Gupta A. Survey on data mining and IP traceback technique in DDoS attack[J]. International Journal of Engineering and Computer Science, 2015, 4(6): 12595-12598.
[12]Zargar S T, Joshi J, Tipper D. A survey of defense mechanisms against Distributed Denial of Service (DDoS) flooding attacks[J]. IEEE Communications Surveys & Tutorials, 2013, 15(4): 2046-2069.
[13]Villeneuve N, Bennett J. Detecting apt activity with network traffic analysis[R]. Trend Micro Incorporated Research Paper, 2012: 1-13.
[14]Paz R D. The heartbeat APT campaign[R]. Trend Micro Incorporated Research Paper, 2012.
[15]Zhang K, Qiu X. CMD: A convincing mechanism for MITM detection in SDN[C]//2018 IEEE International Conference on Consumer Electronics (ICCE), Piscataway: IEEE, 2018: 1-6.
[16]Fouladi R F, Kayatas C E, Anarim E. Frequency based DDoS attack detection approach using naive Bayes classification[C]//2016 39th International Conference on Telecommunications and Signal Processing (TSP), Piscataway: IEEE, 2016: 104-107.
[17]Alzahrani S, Hong L. Detection of Distributed Denial of Service (DDoS) attacks using artificial intelligence on cloud[C]//2018 IEEE World Congress on Services (SERVICES), Piscataway: IEEE, 2018: 35-36.
[18]Chambers N, Fry B, Mcmasters J. Detecting denial-of-service attacks from social media text: Applying NLP to computer security[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, USA: the Association for Computational Linguistics, 2018, 1: 1626-1635.
[19]Barrionuevo M, Lopresti M, Miranda N, et al. An anomaly detection model in a LAN using K-NN and high performance computing techniques[C]//Computer Science-CACIC 2017, Berlin: Springer, 2018: 219-228.
[20]Alanazi S, Al-Muhtadi J, Derhab A, et al. On resilience of wireless mesh routing protocol against DoS attacks in IoT-based ambient assisted living applications[C]//2015 17th International Conference on E-health Networking, Application & Services (HealthCom), Piscataway: IEEE, 2015: 205-210.
[21]Ma X L, Chen Y H. DDoS detection method based on chaos analysis of network traffic entropy[J]. IEEE Communications Letters, 2014, 18(1): 114-117.
[22]Bhatia S, Behal S, Ahmed I. Distributed denial of service attacks and defense mechanisms: Current landscape and future directions[M]//Conti M, Somani G, Poovendran R, eds. Versatile Cybersecurity. Cham, Switzeland: Springer, 2018: 55-97.
[23]Wang X, Chellappan S, Boyer P, et al. On the effectiveness of secure overlay forwarding systems under intelligent distributed DoS attacks[J]. IEEE Transactions on Parallel & Distributed Systems, 2006, 17(7): 619-632.
[24]Ranjan S, Swaminathan R, Uysal M, et al. DDoS-resilient scheduling to counter application layer attacks under imperfect detection[C]//Infocom IEEE International Conference on Computer Communications, Piscataway: IEEE, 2006: 23-29.
[25]Kavita S, Gupta B B. Taxonomy of Distributed Denial of Service (DDoS) attacks and defense mechanisms in present era of smartphone devices[J]. International Journal of E-Services and Mobile Applications (IJESMA), 2018, 10(2): 58-74.
[26]Kansal V, Dave M. Proactive DDoS attack detection and isolation[C]//2017 International Conference on Computer, Communications and Electronics (Comptelix), Piscataway: IEEE, 2017: 334-338.
[27]Jiao J H, Ye B J, Zhao Y, et al. Detecting TCP-based DDoS attacks in Baidu Cloud Computing Data Centers[C]//2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), Piscataway: IEEE, 2017: 256-258.
[28]He Z, Zhang T, Lee R B. Machine learning based DDoS attack detection from source side in cloud[C]//2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), Piscataway: IEEE, 2017: 114-120.
[29]Katkar V, Zinjade A, Dalvi S, et al. Detection of DoS/DDoS attack against HTTP servers using Naive Bayesian[C]//2015 International Conference on Computing Communication Control and Automation, Piscataway: IEEE, 2015: 280-285.
[30]Ajagekar S K, Jadhav P V. Automated approach for DDOS attacks detection based on Naive Bayes multinomial classifier[C]//2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Piscataway: IEEE, 2018: 1-5.
[31]Kato K, Klyuev V. Large-scale network packet analysis for intelligent DDoS attack detection development[C]//The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014), Piscataway: IEEE, 2014: 360-365.
[32]Yang L, Zhao H. DDoS attack identification and defense using SDN based on machine learning method[C]//2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN), Piscataway: IEEE, 2018: 174-178.
[33]Phan T V, Park M. Efficient distributed denial-of-service attack defense in SDN-based cloud[J]. IEEE Access, 2019, 7: 18701-18714.
[34]Lakshminarasimman S, Ruswin S, Sundarakantham K. Detecting DDoS attacks using decision tree algorithm[C]//2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), Piscataway: IEEE, 2017: 1-6.
[35]Zekri M, Kafhali S E, Aboutabit N, et al. DDoS attack detection using machine learning techniques in cloud computing environments[C]//2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Piscataway: IEEE, 2017: 1-7.
[36]Pramana M I W, Purwanto Y, Suratman F Y. DDoS detection using modified K-means clustering with chain initialization over landmark window[C]//2015 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), Piscataway: IEEE, 2015: 7-11.
[37]Dincalp U, Güzel M S, Sevine O, et al. Anomaly based distributed denial of service attack detection and prevention with machine learning[C]//2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Piscataway: IEEE, 2018: 1-4.
[38]Kaur G. A novel distributed machine learning framework for semi-supervised detection of botnet attacks[C]//2018 Eleventh International Conference on Contemporary Computing (IC3), Piscataway: IEEE, 2018: 1-7.
[39]Gu Y, Li K, Guo Z, et al. Semi-supervised K-means DDoS detection method using hybrid feature selection algorithm[J]. IEEE Access, 2019, 7: 64351-64365.
[40]Liu Y, Dong M, Ota K, et al. Deep reinforcement learning based smart mitigation of DDoS flooding in software-defined networks[C]//2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Piscataway: IEEE, 2018: 1-6.
[41]Ramadhan G, Kurniawan Y, Chang-Soo K. Design of TCP SYN Flood DDoS attack detection using artificial immune systems[C]//2016 6th International Conference on System Engineering and Technology (ICSET), Piscataway: IEEE, 2016: 72-76.
[42]Igbe O, Ajayi O, Saadawi T. Denial of service attack detection using dendritic cell algorithm[C]//2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), Piscataway: IEEE, 2017: 294-299.
[43]Igbe O, Ajayi O, Saadawi T. Detecting denial of service attacks using a combination of dendritic cell algorithm and the negative selection algorithm[C]//2017 IEEE International Conference on Smart Cloud (SmartCloud), Piscataway: IEEE, 2017: 72-77.
[44]Khalaf B A, Mostafa S A, Mustapha A, et al. An adaptive model for detection and prevention of DDoS and flash crowd flooding attacks[C]//2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR), Piscataway: IEEE, 2018: 1-6.
[45]Duraipandian M, Palanisamy C. An intelligent agent based defense architecture for DDoS attacks[C]//2014 International Conference on Electronics and Communication Systems (ICECS), Piscataway: IEEE, 2014: 1-7.
[46]Kesavamoorthy R, Soundar K R. Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system[J]. Cluster Computing, 2018(1): 1-8.
[47]Singh K, Dhindsa K S, Bhushan B. Performance analysis of agent based distributed defense mechanisms against DDOS attacks[J]. International Journal of Computing, 2018, 17(1): 15-24.
[48]Shiaeles S N, Katos V, Karakos A S, et al. Real time DDoS detection using fuzzy estimators[J]. Computers & Security, 2012, 31(6): 782-790.
[49]Mondal H S, Hasan M T, Hossain M B, et al. Enhancing secure cloud computing environment by detecting DDoS attack using fuzzy logic[C]//2017 3rd International Conference on Electrical Information and Communication Technology (EICT), Piscataway: IEEE, 2017: 1-4.
[50]Singh K J, Thongam K, De T. Detection and differentiation of application layer DDoS attack from flash events using fuzzy-GA computation[J]. IET Information Security, 2018, 12(6): 502-512.
[51]Chen J H, Zhong M, Chen F J, et al. DDoS defense system with turing test and neural network[C]//2012 IEEE International Conference on Granular Computing, Piscataway: IEEE, 2012: 38-43.
[52]Dayal N, Srivastava S. An RBF-PSO based approach for early detection of DDoS attacks in SDN[C]//2018 10th International Conference on Communication Systems & Networks (COMSNETS), Piscataway: IEEE, 2018: 17-24.
[53]Su Y Z, Meng X R, Meng Q W, et al. DDoS attack detection algorithm based on hybrid traffic prediction model[C]//2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Piscataway: IEEE, 2018: 1-5.
[54]Ghanbari M, Kinsner W, Ferens K. Detecting a distributed denial of service attack using a pre-processed convolutional neural network[C]//2017 IEEE Electrical Power and Energy Conference (EPEC), Piscataway: IEEE, 2017: 1-6.
[55]Ghanbari M, Kinsner W. Extracting features from both the input and the output of a convolutional neural network to detect distributed denial of service attacks[C]//2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Piscataway: IEEE, 2018: 138-144.
[56]Spaulding J, Mohaisen A. Defending internet of things against malicious domain names using D-FENS[C]//2018 IEEE/ACM Symposium on Edge Computing (SEC), Piscataway: IEEE, 2018: 387-392.
[57]Roopak M, Tian G Y, Chambers J. Deep learning models for cyber security in IoT networks[C]//2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Piscataway: IEEE, 2019: 452-457.
[58]Badve O P, Gupta B B, Yamaguchi S, et al. DDoS detection and filtering technique in cloud environment using GARCH model[C]//2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), Piscataway: IEEE, 2015: 584-586.
[59]Ishitaki T, Oda T, Barolli L. A neural network based user identification for Tor networks: Data analysis using Friedman test[C]//2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Piscataway: IEEE, 2016: 7-13.