The detection of low-rate DoS attacks using the SADBSCAN algorithm
Low-rate denial-of-service (DoS) attacks, which can exploit vulnerabilities in Internet protocols to deteriorate the quality of service, are variants of DoS attacks. It is challenging to identify low-rate DoS attacks using traditional DoS defence mechanisms due to their low attack rate and stealthy...
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| Vydané v: | Information sciences Ročník 565; s. 229 - 247 |
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01.07.2021
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Low-rate denial-of-service (DoS) attacks, which can exploit vulnerabilities in Internet protocols to deteriorate the quality of service, are variants of DoS attacks. It is challenging to identify low-rate DoS attacks using traditional DoS defence mechanisms due to their low attack rate and stealthy nature. Most of the existing attack detection techniques are based on statistical analysis and signal processing. They usually show a high false negative rate and are only applicable to small-scale data. We propose a new low-rate DoS attack detection scheme based on the self-adaptive density-based spatial clustering of applications with noise (SADBSCAN) algorithm. The SADBSCAN algorithm provides a solution to adaptively identify clusters in multidensity datasets. We use the SADBSCAN algorithm to group network traffic according to the characteristics of the network traffic subject to low-rate DoS attacks. Then, we use cosine similarity to determine whether the groups contain low-rate DoS attacks. To evaluate performance, we conducted experiments and compared the results with those of other detection solutions. The experimental data include data generated by the NS-2 and TestBed simulations and the WIDE public dataset. The results show that our scheme improves the detection accuracy, reduces the false negative rate, and can be adapted to large-scale complex network environments. |
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| AbstractList | Low-rate denial-of-service (DoS) attacks, which can exploit vulnerabilities in Internet protocols to deteriorate the quality of service, are variants of DoS attacks. It is challenging to identify low-rate DoS attacks using traditional DoS defence mechanisms due to their low attack rate and stealthy nature. Most of the existing attack detection techniques are based on statistical analysis and signal processing. They usually show a high false negative rate and are only applicable to small-scale data. We propose a new low-rate DoS attack detection scheme based on the self-adaptive density-based spatial clustering of applications with noise (SADBSCAN) algorithm. The SADBSCAN algorithm provides a solution to adaptively identify clusters in multidensity datasets. We use the SADBSCAN algorithm to group network traffic according to the characteristics of the network traffic subject to low-rate DoS attacks. Then, we use cosine similarity to determine whether the groups contain low-rate DoS attacks. To evaluate performance, we conducted experiments and compared the results with those of other detection solutions. The experimental data include data generated by the NS-2 and TestBed simulations and the WIDE public dataset. The results show that our scheme improves the detection accuracy, reduces the false negative rate, and can be adapted to large-scale complex network environments. |
| Author | Wang, Xiyin Zhang, Siqi Tang, Dan Chen, Jingwen |
| Author_xml | – sequence: 1 givenname: Dan surname: Tang fullname: Tang, Dan – sequence: 2 givenname: Siqi surname: Zhang fullname: Zhang, Siqi email: zhangsiqi@hnu.edu.cn – sequence: 3 givenname: Jingwen surname: Chen fullname: Chen, Jingwen – sequence: 4 givenname: Xiyin surname: Wang fullname: Wang, Xiyin |
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| Cites_doi | 10.1109/TIFS.2009.2024719 10.3390/s20102932 10.1109/CC.2017.7961367 10.1109/TDSC.2015.2443807 10.1126/science.1242072 10.1016/j.comnet.2019.01.031 10.1109/JSYST.2020.2991168 10.1016/j.future.2018.07.017 10.1109/TIFS.2014.2321034 10.1016/j.patcog.2016.07.007 10.1016/j.jss.2012.07.065 10.1109/TCC.2014.2325045 10.1016/j.future.2019.12.034 10.1016/j.ins.2019.11.004 10.1016/j.compeleceng.2018.11.004 10.1016/j.adhoc.2020.102145 10.1016/j.ins.2019.08.062 10.1016/j.comnet.2019.01.007 10.1109/INFCOM.2005.1498361 10.1002/dac.2993 10.1109/CC.2014.7022532 10.1016/j.ins.2019.10.069 10.1016/j.ins.2018.04.065 10.1016/j.ssci.2020.104604 10.1109/TCNS.2016.2550858 10.1016/j.cose.2017.09.009 10.1109/TIFS.2011.2107320 10.1109/ACCESS.2019.2903816 10.1145/3183713.3196887 10.1155/2015/465402 10.1016/j.jpdc.2006.04.007 10.1016/j.comnet.2018.02.029 10.1016/j.dcan.2020.04.002 |
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| Keywords | SADBSCAN Cosine similarity Low-rate DoS Attack detection Network traffic analysis |
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| References | Tripathi, Hubballi (b0055) 2018; 72 Chen, Hwang (b0180) 2006; 66 Zhu, Xin, Wu, You (b0025) 2014; 11 Luo, Chang (b0015) 2005 Wu, Lei, Yao, Wang, Musa (b0185) 2013; 86 W. Project, Mawi working group traffic archive (2018). url:http://mawi.wide.ad.jp/mawi/. Maciá-Fernández, Díaz-Verdejo, García-Teodoro (b0195) 2009; 4 Chen, Meng, Shan, Fu, Bhargava (b0040) 2019; 7 Kuzmanovic, Knightly (b0010) 2003 M. Guirguis, A. Bestavros, I. Matta, Y. Zhang, Reduction of quality (roq) attacks on internet end-systems, in: Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, IEEE, vol. 2, 2005, pp. 1362–1372. Li, Liu, Obaidat, Wu, Vijayakumar, Kumar (b0140) 2020; 14 Xiang, Li, Zhou (b0090) 2011; 6 Shashidhara, Balaji (b0150) 2014; 4 Floyd, Henderson, Gurtov (b0065) 2004 Wu, Zhang, Yue (b0080) 2015; 13 Luo, Yang, Wang, Xu, Sun, Long (b0200) 2014; 9 Tang, Tang, Shi, Zhan, Yang (b0110) 2020; 7 Ficco, Rak (b0035) 2014; 3 Tang, Dai, Tang, Zhan, Man (b0125) 2018 Tang, Chen, Chen, Liu, Li (b0165) 2014; 2014 Bojović, Bašičević, Ocovaj, Popović (b0175) 2019; 73 Cambiaso, Chiola, Aiello (b0060) 2019; 150 Kang, Yang, Zhang (b0095) 2015; 2015 Paschos, Tassiulas (b0205) 2016; 4 M. DELIO, New breed of attack zombies lurk, http://www.acm.org/technews/articles/2001-3/0514m.html (2001). url:https://ci.nii.ac.jp/naid/10018698289/en/. Sun, Li, Bhuiyan, Wang, Li (b0210) 2019; 479 Vuttipittayamongkol, Elyan (b0135) 2020; 509 Chen, Yeo, Lee, Lau (b0100) 2018; 136 Hassan, Gumaei, Alsanad, Alrubaian, Fortino (b0145) 2020; 513 Tang, Man, Tang, Feng, Yang (b0130) 2020; 102 Zhang, Wu, Chen, Yue (b0085) 2017; 30 Huang, Peng, Wang, Zhao (b0160) 2013 H. Song, J.G. Lee, Rp-dbscan: A superfast parallel dbscan algorithm based on random partitioning, in: Proceedings of the 2018 International Conference on Management of Data, ACM, 2018, pp. 1173–1187. Sahoo, Puthal, Tiwary, Rodrigues, Sahoo, Dash (b0050) 2018; 89 Vaccari, Aiello, Cambiaso (b0045) 2020; 20 Tang, Tang, Dai, Chen, Li, Rodrigues (b0115) 2020; 106 Paxson, Allman (b0070) 2000 Fang, Tan, Wilbur (b0105) 2020; 124 Rodriguez, Laio (b0225) 2014; 344 Wu, Pan, Yue, Liu (b0170) 2019; 152 Liu, Wang, Wu, Yue (b0215) 2020; 6 Ucb/lbnl/vint network simulator—ns (version 2). url:http://www-mash.cs.berkeley.edu/ns/. Yue, Wang, Wu (b0030) 2019 D. Dua, C. Graff, UCI machine learning repository (2017). url:http://archive.ics.uci.edu/ml. Wu, Wang, Yan, Yue (b0190) 2017; 14 Yang, Lam (b0075) 2000 Thabtah, Hammoud, Kamalov, Gonsalves (b0120) 2020; 513 Wu, Pei (b0155) 2011; 39 Zhu, Ting, Carman (b0230) 2016; 60 M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: KDD, vol. 96, 1996, pp. 226–231. Maciá-Fernández (10.1016/j.ins.2021.02.038_b0195) 2009; 4 Bojović (10.1016/j.ins.2021.02.038_b0175) 2019; 73 Fang (10.1016/j.ins.2021.02.038_b0105) 2020; 124 Luo (10.1016/j.ins.2021.02.038_b0015) 2005 Huang (10.1016/j.ins.2021.02.038_b0160) 2013 Wu (10.1016/j.ins.2021.02.038_b0170) 2019; 152 Kuzmanovic (10.1016/j.ins.2021.02.038_b0010) 2003 Wu (10.1016/j.ins.2021.02.038_b0155) 2011; 39 Xiang (10.1016/j.ins.2021.02.038_b0090) 2011; 6 Vaccari (10.1016/j.ins.2021.02.038_b0045) 2020; 20 Zhu (10.1016/j.ins.2021.02.038_b0025) 2014; 11 Paschos (10.1016/j.ins.2021.02.038_b0205) 2016; 4 10.1016/j.ins.2021.02.038_b0240 Liu (10.1016/j.ins.2021.02.038_b0215) 2020; 6 Cambiaso (10.1016/j.ins.2021.02.038_b0060) 2019; 150 Tang (10.1016/j.ins.2021.02.038_b0165) 2014; 2014 Floyd (10.1016/j.ins.2021.02.038_b0065) 2004 Tang (10.1016/j.ins.2021.02.038_b0110) 2020; 7 Wu (10.1016/j.ins.2021.02.038_b0080) 2015; 13 Wu (10.1016/j.ins.2021.02.038_b0185) 2013; 86 Chen (10.1016/j.ins.2021.02.038_b0040) 2019; 7 Chen (10.1016/j.ins.2021.02.038_b0100) 2018; 136 Thabtah (10.1016/j.ins.2021.02.038_b0120) 2020; 513 Tang (10.1016/j.ins.2021.02.038_b0115) 2020; 106 10.1016/j.ins.2021.02.038_b0235 Tripathi (10.1016/j.ins.2021.02.038_b0055) 2018; 72 Zhu (10.1016/j.ins.2021.02.038_b0230) 2016; 60 Hassan (10.1016/j.ins.2021.02.038_b0145) 2020; 513 10.1016/j.ins.2021.02.038_b0250 Luo (10.1016/j.ins.2021.02.038_b0200) 2014; 9 Chen (10.1016/j.ins.2021.02.038_b0180) 2006; 66 Li (10.1016/j.ins.2021.02.038_b0140) 2020; 14 Sahoo (10.1016/j.ins.2021.02.038_b0050) 2018; 89 Tang (10.1016/j.ins.2021.02.038_b0125) 2018 Vuttipittayamongkol (10.1016/j.ins.2021.02.038_b0135) 2020; 509 10.1016/j.ins.2021.02.038_b0245 10.1016/j.ins.2021.02.038_b0005 Yue (10.1016/j.ins.2021.02.038_b0030) 2019 10.1016/j.ins.2021.02.038_b0220 Kang (10.1016/j.ins.2021.02.038_b0095) 2015; 2015 10.1016/j.ins.2021.02.038_b0020 Tang (10.1016/j.ins.2021.02.038_b0130) 2020; 102 Shashidhara (10.1016/j.ins.2021.02.038_b0150) 2014; 4 Zhang (10.1016/j.ins.2021.02.038_b0085) 2017; 30 Paxson (10.1016/j.ins.2021.02.038_b0070) 2000 Wu (10.1016/j.ins.2021.02.038_b0190) 2017; 14 Ficco (10.1016/j.ins.2021.02.038_b0035) 2014; 3 Yang (10.1016/j.ins.2021.02.038_b0075) 2000 Sun (10.1016/j.ins.2021.02.038_b0210) 2019; 479 Rodriguez (10.1016/j.ins.2021.02.038_b0225) 2014; 344 |
| References_xml | – reference: D. Dua, C. Graff, UCI machine learning repository (2017). url:http://archive.ics.uci.edu/ml. – volume: 2015 year: 2015 ident: b0095 article-title: Accurately identifying new qos violation driven by high-distributed low-rate denial of service attacks based on multiple observed features publication-title: Journal of Sensors – volume: 89 start-page: 685 year: 2018 end-page: 697 ident: b0050 article-title: An early detection of low rate ddos attack to sdn based data center networks using information distance metrics publication-title: Future Generation Computer Systems – volume: 60 start-page: 983 year: 2016 end-page: 997 ident: b0230 article-title: Density-ratio based clustering for discovering clusters with varying densities publication-title: Pattern Recognition – start-page: 92 year: 2018 end-page: 104 ident: b0125 article-title: Low-rate dos attack detection based on two-step cluster analysis publication-title: in: International Conference on Information and Communications Security – volume: 30 start-page: e2993 year: 2017 ident: b0085 article-title: An adaptive kpca approach for detecting ldos attack publication-title: International Journal of Communication Systems – reference: W. Project, Mawi working group traffic archive (2018). url:http://mawi.wide.ad.jp/mawi/. – reference: Ucb/lbnl/vint network simulator—ns (version 2). url:http://www-mash.cs.berkeley.edu/ns/. – volume: 72 start-page: 255 year: 2018 end-page: 272 ident: b0055 article-title: Slow rate denial of service attacks against http/2 and detection publication-title: Computers & Security – start-page: 187 year: 2000 end-page: 198 ident: b0075 article-title: General aimd congestion control publication-title: Proceedings 2000 International Conference on Network Protocols, IEEE – year: 2005 ident: b0015 article-title: On a New Class of Pulsing Denial-of-Service Attacks and the Defense publication-title: Proceedings of the Network and Distributed System Security Symposium {NDSS} – reference: M. Guirguis, A. Bestavros, I. Matta, Y. Zhang, Reduction of quality (roq) attacks on internet end-systems, in: Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, IEEE, vol. 2, 2005, pp. 1362–1372. – reference: M. DELIO, New breed of attack zombies lurk, http://www.acm.org/technews/articles/2001-3/0514m.html (2001). url:https://ci.nii.ac.jp/naid/10018698289/en/. – start-page: 209 year: 2013 end-page: 222 ident: b0160 article-title: A study of ldos flows variations based on similarity measurement publication-title: International Conference on Internet and Distributed Computing Systems – volume: 4 year: 2014 ident: b0150 article-title: Low rate denial of service (ldos) attack–a survey publication-title: International Journal of Emerging Technology and Advanced Engineering – volume: 513 start-page: 429 year: 2020 end-page: 441 ident: b0120 article-title: Data imbalance in classification: Experimental evaluation publication-title: Information Sciences – reference: H. Song, J.G. Lee, Rp-dbscan: A superfast parallel dbscan algorithm based on random partitioning, in: Proceedings of the 2018 International Conference on Management of Data, ACM, 2018, pp. 1173–1187. – volume: 39 start-page: 1456 year: 2011 end-page: 1460 ident: b0155 article-title: The detection of ldos attack based on the model of small signal publication-title: Dianzi Xuebao(Acta Electronica Sinica) – volume: 14 start-page: 3547 year: 2020 end-page: 3557 ident: b0140 article-title: A lightweight privacy-preserving authentication protocol for vanets publication-title: IEEE Systems Journal – volume: 150 start-page: 234 year: 2019 end-page: 249 ident: b0060 article-title: Introducing the slowdrop attack publication-title: Computer Networks – year: 2000 ident: b0070 article-title: Rfc2988: computing tcp’s retransmission timer publication-title: IETF – volume: 6 start-page: 426 year: 2011 end-page: 437 ident: b0090 article-title: Low-rate ddos attacks detection and traceback by using new information metrics publication-title: IEEE Transactions on Information Forensics and Security – volume: 4 start-page: 519 year: 2009 end-page: 529 ident: b0195 article-title: Mathematical model for low-rate dos attacks against application servers publication-title: IEEE Transactions on Information Forensics and Security – volume: 509 start-page: 47 year: 2020 end-page: 70 ident: b0135 article-title: Neighbourhood-based undersampling approach for handling imbalanced and overlapped data publication-title: Information Sciences – volume: 3 start-page: 80 year: 2014 end-page: 94 ident: b0035 article-title: Stealthy denial of service strategy in cloud computing publication-title: IEEE Transactions on Cloud Computing – volume: 4 start-page: 749 year: 2016 end-page: 760 ident: b0205 article-title: Sustainability of service provisioning systems under stealth dos attacks publication-title: IEEE Transactions on Control of Network Systems – volume: 6 start-page: 504 year: 2020 end-page: 513 ident: b0215 article-title: The detection method of low-rate dos attack based on multi-feature fusion publication-title: Digital Communications and Networks – volume: 124 year: 2020 ident: b0105 article-title: Application of intrusion detection technology in network safety based on machine learning publication-title: Safety Science – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: b0225 article-title: Clustering by fast search and find of density peaks publication-title: Science – start-page: 75 year: 2003 end-page: 86 ident: b0010 article-title: Low-rate tcp-targeted denial of service attacks: the shrew vs. the mice and elephants publication-title: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications – volume: 513 start-page: 386 year: 2020 end-page: 396 ident: b0145 article-title: A hybrid deep learning model for efficient intrusion detection in big data environment publication-title: Information Sciences – start-page: 1 year: 2019 end-page: 16 ident: b0030 article-title: Low-high burst: a double potency varying-rtt based full-buffer shrew attack model publication-title: IEEE Transactions on Dependable and Secure Computing – volume: 136 start-page: 80 year: 2018 end-page: 94 ident: b0100 article-title: Power spectrum entropy based detection and mitigation of low-rate dos attacks publication-title: Computer Networks – volume: 102 year: 2020 ident: b0130 article-title: Wedms: An advanced mean shift clustering algorithm for ldos attacks detection publication-title: Ad Hoc Networks – volume: 86 start-page: 211 year: 2013 end-page: 221 ident: b0185 article-title: Chaos-based detection of ldos attacks publication-title: Journal of Systems and Software – year: 2004 ident: b0065 article-title: Rfc3782: The newreno modification to tcp’s fast recovery algorithm publication-title: IETF – volume: 106 start-page: 347 year: 2020 end-page: 359 ident: b0115 article-title: Mf-adaboost: Ldos attack detection based on multi-features and improved adaboost publication-title: Future Generation Computer Systems – volume: 66 start-page: 1137 year: 2006 end-page: 1151 ident: b0180 article-title: Collaborative detection and filtering of shrew ddos attacks using spectral analysis publication-title: Journal of Parallel and Distributed Computing – reference: M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: KDD, vol. 96, 1996, pp. 226–231. – volume: 7 start-page: 1 year: 2020 end-page: 18 ident: b0110 article-title: Mf-cnn: a new approach for ldos attack detection based on multi-feature fusion and cnn publication-title: Mobile Networks and Applications – volume: 2014 year: 2014 ident: b0165 article-title: Adaptive ewma method based on abnormal network traffic for ldos attacks publication-title: Mathematical Problems in Engineering – volume: 152 start-page: 64 year: 2019 end-page: 77 ident: b0170 article-title: Sequence alignment detection of tcp-targeted synchronous low-rate dos attacks publication-title: Computer Networks – volume: 73 start-page: 84 year: 2019 end-page: 96 ident: b0175 article-title: A practical approach to detection of distributed denial-of-service attacks using a hybrid detection method publication-title: Computers & Electrical Engineering – volume: 14 start-page: 98 year: 2017 end-page: 112 ident: b0190 article-title: Low-rate dos attack flows filtering based on frequency spectral analysis publication-title: China Communications – volume: 7 start-page: 32853 year: 2019 end-page: 32866 ident: b0040 article-title: A novel low-rate denial of service attack detection approach in zigbee wireless sensor network by combining hilbert-huang transformation and trust evaluation publication-title: IEEE Access – volume: 9 start-page: 1069 year: 2014 end-page: 1083 ident: b0200 article-title: On a mathematical model for low-rate shrew ddos publication-title: IEEE Transactions on Information Forensics and Security – volume: 20 year: 2020 ident: b0045 article-title: Slowite, a novel denial of service attack affecting mqtt publication-title: Sensors – volume: 11 start-page: 101 year: 2014 end-page: 107 ident: b0025 article-title: A novel distributed ldos attack scheme against internet routing publication-title: China Communications – volume: 479 start-page: 456 year: 2019 end-page: 471 ident: b0210 article-title: Modeling and clustering attacker activities in iot through machine learning techniques publication-title: Information Sciences – volume: 13 start-page: 559 year: 2015 end-page: 567 ident: b0080 article-title: Low-rate dos attacks detection based on network multifractal publication-title: IEEE Transactions on Dependable and Secure Computing – volume: 4 start-page: 519 issue: 3 year: 2009 ident: 10.1016/j.ins.2021.02.038_b0195 article-title: Mathematical model for low-rate dos attacks against application servers publication-title: IEEE Transactions on Information Forensics and Security doi: 10.1109/TIFS.2009.2024719 – ident: 10.1016/j.ins.2021.02.038_b0245 – volume: 20 issue: 10 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0045 article-title: Slowite, a novel denial of service attack affecting mqtt publication-title: Sensors doi: 10.3390/s20102932 – volume: 14 start-page: 98 issue: 6 year: 2017 ident: 10.1016/j.ins.2021.02.038_b0190 article-title: Low-rate dos attack flows filtering based on frequency spectral analysis publication-title: China Communications doi: 10.1109/CC.2017.7961367 – volume: 13 start-page: 559 issue: 5 year: 2015 ident: 10.1016/j.ins.2021.02.038_b0080 article-title: Low-rate dos attacks detection based on network multifractal publication-title: IEEE Transactions on Dependable and Secure Computing doi: 10.1109/TDSC.2015.2443807 – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 10.1016/j.ins.2021.02.038_b0225 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 152 start-page: 64 year: 2019 ident: 10.1016/j.ins.2021.02.038_b0170 article-title: Sequence alignment detection of tcp-targeted synchronous low-rate dos attacks publication-title: Computer Networks doi: 10.1016/j.comnet.2019.01.031 – volume: 14 start-page: 3547 issue: 3 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0140 article-title: A lightweight privacy-preserving authentication protocol for vanets publication-title: IEEE Systems Journal doi: 10.1109/JSYST.2020.2991168 – volume: 89 start-page: 685 year: 2018 ident: 10.1016/j.ins.2021.02.038_b0050 article-title: An early detection of low rate ddos attack to sdn based data center networks using information distance metrics publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2018.07.017 – volume: 9 start-page: 1069 issue: 7 year: 2014 ident: 10.1016/j.ins.2021.02.038_b0200 article-title: On a mathematical model for low-rate shrew ddos publication-title: IEEE Transactions on Information Forensics and Security doi: 10.1109/TIFS.2014.2321034 – volume: 60 start-page: 983 year: 2016 ident: 10.1016/j.ins.2021.02.038_b0230 article-title: Density-ratio based clustering for discovering clusters with varying densities publication-title: Pattern Recognition doi: 10.1016/j.patcog.2016.07.007 – volume: 86 start-page: 211 issue: 1 year: 2013 ident: 10.1016/j.ins.2021.02.038_b0185 article-title: Chaos-based detection of ldos attacks publication-title: Journal of Systems and Software doi: 10.1016/j.jss.2012.07.065 – start-page: 75 year: 2003 ident: 10.1016/j.ins.2021.02.038_b0010 article-title: Low-rate tcp-targeted denial of service attacks: the shrew vs. the mice and elephants – volume: 3 start-page: 80 issue: 1 year: 2014 ident: 10.1016/j.ins.2021.02.038_b0035 article-title: Stealthy denial of service strategy in cloud computing publication-title: IEEE Transactions on Cloud Computing doi: 10.1109/TCC.2014.2325045 – start-page: 1 year: 2019 ident: 10.1016/j.ins.2021.02.038_b0030 article-title: Low-high burst: a double potency varying-rtt based full-buffer shrew attack model publication-title: IEEE Transactions on Dependable and Secure Computing – volume: 106 start-page: 347 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0115 article-title: Mf-adaboost: Ldos attack detection based on multi-features and improved adaboost publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2019.12.034 – start-page: 209 year: 2013 ident: 10.1016/j.ins.2021.02.038_b0160 article-title: A study of ldos flows variations based on similarity measurement – volume: 513 start-page: 429 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0120 article-title: Data imbalance in classification: Experimental evaluation publication-title: Information Sciences doi: 10.1016/j.ins.2019.11.004 – volume: 73 start-page: 84 year: 2019 ident: 10.1016/j.ins.2021.02.038_b0175 article-title: A practical approach to detection of distributed denial-of-service attacks using a hybrid detection method publication-title: Computers & Electrical Engineering doi: 10.1016/j.compeleceng.2018.11.004 – volume: 102 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0130 article-title: Wedms: An advanced mean shift clustering algorithm for ldos attacks detection publication-title: Ad Hoc Networks doi: 10.1016/j.adhoc.2020.102145 – volume: 7 start-page: 1 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0110 article-title: Mf-cnn: a new approach for ldos attack detection based on multi-feature fusion and cnn publication-title: Mobile Networks and Applications – volume: 509 start-page: 47 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0135 article-title: Neighbourhood-based undersampling approach for handling imbalanced and overlapped data publication-title: Information Sciences doi: 10.1016/j.ins.2019.08.062 – volume: 150 start-page: 234 year: 2019 ident: 10.1016/j.ins.2021.02.038_b0060 article-title: Introducing the slowdrop attack publication-title: Computer Networks doi: 10.1016/j.comnet.2019.01.007 – ident: 10.1016/j.ins.2021.02.038_b0020 doi: 10.1109/INFCOM.2005.1498361 – ident: 10.1016/j.ins.2021.02.038_b0220 – volume: 30 start-page: e2993 issue: 4 year: 2017 ident: 10.1016/j.ins.2021.02.038_b0085 article-title: An adaptive kpca approach for detecting ldos attack publication-title: International Journal of Communication Systems doi: 10.1002/dac.2993 – volume: 11 start-page: 101 issue: 13 year: 2014 ident: 10.1016/j.ins.2021.02.038_b0025 article-title: A novel distributed ldos attack scheme against internet routing publication-title: China Communications doi: 10.1109/CC.2014.7022532 – year: 2004 ident: 10.1016/j.ins.2021.02.038_b0065 article-title: Rfc3782: The newreno modification to tcp’s fast recovery algorithm publication-title: IETF – volume: 513 start-page: 386 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0145 article-title: A hybrid deep learning model for efficient intrusion detection in big data environment publication-title: Information Sciences doi: 10.1016/j.ins.2019.10.069 – volume: 479 start-page: 456 year: 2019 ident: 10.1016/j.ins.2021.02.038_b0210 article-title: Modeling and clustering attacker activities in iot through machine learning techniques publication-title: Information Sciences doi: 10.1016/j.ins.2018.04.065 – year: 2000 ident: 10.1016/j.ins.2021.02.038_b0070 article-title: Rfc2988: computing tcp’s retransmission timer publication-title: IETF – volume: 124 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0105 article-title: Application of intrusion detection technology in network safety based on machine learning publication-title: Safety Science doi: 10.1016/j.ssci.2020.104604 – volume: 2014 issue: 7 year: 2014 ident: 10.1016/j.ins.2021.02.038_b0165 article-title: Adaptive ewma method based on abnormal network traffic for ldos attacks publication-title: Mathematical Problems in Engineering – volume: 4 issue: 6 year: 2014 ident: 10.1016/j.ins.2021.02.038_b0150 article-title: Low rate denial of service (ldos) attack–a survey publication-title: International Journal of Emerging Technology and Advanced Engineering – volume: 4 start-page: 749 issue: 4 year: 2016 ident: 10.1016/j.ins.2021.02.038_b0205 article-title: Sustainability of service provisioning systems under stealth dos attacks publication-title: IEEE Transactions on Control of Network Systems doi: 10.1109/TCNS.2016.2550858 – volume: 72 start-page: 255 year: 2018 ident: 10.1016/j.ins.2021.02.038_b0055 article-title: Slow rate denial of service attacks against http/2 and detection publication-title: Computers & Security doi: 10.1016/j.cose.2017.09.009 – volume: 6 start-page: 426 issue: 2 year: 2011 ident: 10.1016/j.ins.2021.02.038_b0090 article-title: Low-rate ddos attacks detection and traceback by using new information metrics publication-title: IEEE Transactions on Information Forensics and Security doi: 10.1109/TIFS.2011.2107320 – ident: 10.1016/j.ins.2021.02.038_b0250 – start-page: 187 year: 2000 ident: 10.1016/j.ins.2021.02.038_b0075 article-title: General aimd congestion control – volume: 7 start-page: 32853 year: 2019 ident: 10.1016/j.ins.2021.02.038_b0040 article-title: A novel low-rate denial of service attack detection approach in zigbee wireless sensor network by combining hilbert-huang transformation and trust evaluation publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2903816 – ident: 10.1016/j.ins.2021.02.038_b0235 doi: 10.1145/3183713.3196887 – volume: 39 start-page: 1456 issue: 6 year: 2011 ident: 10.1016/j.ins.2021.02.038_b0155 article-title: The detection of ldos attack based on the model of small signal publication-title: Dianzi Xuebao(Acta Electronica Sinica) – volume: 2015 year: 2015 ident: 10.1016/j.ins.2021.02.038_b0095 article-title: Accurately identifying new qos violation driven by high-distributed low-rate denial of service attacks based on multiple observed features publication-title: Journal of Sensors doi: 10.1155/2015/465402 – year: 2005 ident: 10.1016/j.ins.2021.02.038_b0015 article-title: On a New Class of Pulsing Denial-of-Service Attacks and the Defense – start-page: 92 year: 2018 ident: 10.1016/j.ins.2021.02.038_b0125 article-title: Low-rate dos attack detection based on two-step cluster analysis – volume: 66 start-page: 1137 issue: 9 year: 2006 ident: 10.1016/j.ins.2021.02.038_b0180 article-title: Collaborative detection and filtering of shrew ddos attacks using spectral analysis publication-title: Journal of Parallel and Distributed Computing doi: 10.1016/j.jpdc.2006.04.007 – volume: 136 start-page: 80 year: 2018 ident: 10.1016/j.ins.2021.02.038_b0100 article-title: Power spectrum entropy based detection and mitigation of low-rate dos attacks publication-title: Computer Networks doi: 10.1016/j.comnet.2018.02.029 – ident: 10.1016/j.ins.2021.02.038_b0240 – volume: 6 start-page: 504 year: 2020 ident: 10.1016/j.ins.2021.02.038_b0215 article-title: The detection method of low-rate dos attack based on multi-feature fusion publication-title: Digital Communications and Networks doi: 10.1016/j.dcan.2020.04.002 – ident: 10.1016/j.ins.2021.02.038_b0005 |
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