Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks
With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse...
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| Vydané v: | Arabian journal for science and engineering (2011) Ročník 46; číslo 4; s. 3749 - 3778 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2021
Springer Nature B.V |
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| ISSN: | 2193-567X, 1319-8025, 2191-4281 |
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| Abstract | With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse that reduces overall IoT systems efficiency. Hence, it is important to remove repetitive and irrelevant features while designing effective IDS. Motivated from aforementioned challenges, this paper presents an intelligent cyber attack detection system for IoT network using a novel hybrid feature reduced approach. This technique first performs feature ranking using correlation coefficient, random forest mean decrease accuracy and gain ratio to obtain three different feature sets. Then, features are combined using a suitably designed mechanism (AND operation), to obtain single optimized feature set. Finally, the obtained reduced feature set is fed to three well-known machine learning algorithms such as random forest, K-nearest neighbor and XGBoost for detection of cyber attacks. The efficiency of the proposed cyber attack detection framework is evaluated using NSL-KDD and two latest IoT-based datasets namely, BoT-IoT and DS2OS. Performance of the proposed framework is evaluated and compared with some recent state-of-the-art techniques found in literature, in terms of accuracy, detection rate (DR), precision and F1 score. Performance analysis using these three datasets shows that the proposed model has achieved DR up to 90%–100%, for most of the attack vectors that has close similarity to normal behaviors and accuracy above 99%. |
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| AbstractList | With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse that reduces overall IoT systems efficiency. Hence, it is important to remove repetitive and irrelevant features while designing effective IDS. Motivated from aforementioned challenges, this paper presents an intelligent cyber attack detection system for IoT network using a novel hybrid feature reduced approach. This technique first performs feature ranking using correlation coefficient, random forest mean decrease accuracy and gain ratio to obtain three different feature sets. Then, features are combined using a suitably designed mechanism (AND operation), to obtain single optimized feature set. Finally, the obtained reduced feature set is fed to three well-known machine learning algorithms such as random forest, K-nearest neighbor and XGBoost for detection of cyber attacks. The efficiency of the proposed cyber attack detection framework is evaluated using NSL-KDD and two latest IoT-based datasets namely, BoT-IoT and DS2OS. Performance of the proposed framework is evaluated and compared with some recent state-of-the-art techniques found in literature, in terms of accuracy, detection rate (DR), precision and F1 score. Performance analysis using these three datasets shows that the proposed model has achieved DR up to 90%–100%, for most of the attack vectors that has close similarity to normal behaviors and accuracy above 99%. |
| Author | Kumar, Prabhat Tripathi, Rakesh Gupta, Govind P. |
| Author_xml | – sequence: 1 givenname: Prabhat orcidid: 0000-0002-0723-0752 surname: Kumar fullname: Kumar, Prabhat email: pkumar.phd2019.it@nitrr.ac.in organization: Department of Information Technology, National Institute of Technology – sequence: 2 givenname: Govind P. surname: Gupta fullname: Gupta, Govind P. organization: Department of Information Technology, National Institute of Technology – sequence: 3 givenname: Rakesh surname: Tripathi fullname: Tripathi, Rakesh organization: Department of Information Technology, National Institute of Technology |
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| Cites_doi | 10.1109/JIOT.2020.3033131 10.1155/2017/6817627 10.1007/s42979-020-00201-310.1007/s42979-020-00201-310:1007/s42979-020-00201-3 10.1155/2019/7130868 10.1007/s10489-020-01694-4 10.1016/j.eswa.2017.07.005 10.1016/j.jnca.2020.102630 10.1016/j.compeleceng.2013.11.024 10.1007/s10489-018-01408-x 10.1109/ACCESS.2019.2923640 10.1007/978-81-322-2695-6_45 10.1007/s42452-020-2414-z 10.3390/electronics9010144 10.1021/ci034160g 10.1016/j.knosys.2012.09.004 10.1109/ACCESS.2020.2972627 10.1109/ACCESS.2018.2868993 10.1109/CISDA.2009.5356528 10.26483/ijarcs.v10i2.6395 10.1049/iet-net.2018.5080 10.1016/j.future.2019.05.041 10.1007/978-981-13-1921-1_35 10.1016/j.future.2020.02.017 10.1016/j.comcom.2020.02.008 10.1016/j.compbiomed.2019.103375 10.1504/IJHPCN.2019.097503 10.3390/app9020238 10.1109/CCDC.2013.6560973 10.1504/IJHPCN.2019.106095 10.1016/j.jnca.2018.12.006 10.1109/ACCESS.2019.2928048 10.1007/978-3-030-14070-0_13 10.1109/GCCE46687.2019.9015465 10.1016/j.eswa.2014.11.009 10.1016/j.eswa.2019.04.017 10.1007/s10844-015-0388-x 10.1007/s10489-019-01436-1 10.1109/COMST.2015.2494502 10.1109/TFUZZ.2017.2719619 10.1109/INISTA.2012.6247011 10.1145/2939672.2939785 10.1007/s12083-017-0630-0 10.1260/1748-3018.6.3.385 10.1016/j.iot.2019.100059 10.1109/ACCESS.2018.2869577 10.13052/jcsm2245-1439.414 10.1587/transinf.2016ICP0018 10.1016/j.neucom.2014.08.070 10.1186/1471-2105-8-25 10.1016/j.procs.2016.06.016 10.1016/j.comcom.2011.07.001 10.1109/COMST.2020.2988293 10.1007/s11277-019-06864-3 10.1016/j.patrec.2007.05.011 10.1007/s10489-018-1141-2 10.1109/TETC.2016.2633228 10.1109/SURV.2013.052213.00046 10.1016/j.future.2018.04.027 10.1504/IJHPCN.2019.103548 10.1007/978-981-32-9343-4_16 10.1109/ICASID.2019.8925239 |
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| Copyright | King Fahd University of Petroleum & Minerals 2021 King Fahd University of Petroleum & Minerals 2021. |
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| Keywords | Cyber attacks Internet of Things (IoT) Feature selection Intrusion detection system (IDS) Feature ranking |
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| References | Pajouh, Dastghaibyfard, Hashemi (CR38) 2017; 48 Su, Sun, Zhu, Wang, Li (CR59) 2020; 8 Tahsien, Karimipour, Spachos (CR13) 2020; 161 Tewari, Gupta (CR2) 2019; 15 Ghazy, El-Rabaie, Dessouky, El-Fishawy, Abd El-Samie (CR57) 2020; 111 Eesa, Orman, Brifcani (CR23) 2015; 42 CR37 Ren, Guo, Qian, Yuan, Hao, Jingjing (CR15) 2019; 2019 CR36 CR35 Chandak, Shukla, Wadhvani (CR55) 2019; 130 CR33 Remeseiro, Bolon-Canedo (CR28) 2019; 112 CR32 Koroniotis, Moustafa, Sitnikova, Turnbull (CR34) 2019; 100 Chapaneri, Shah, Satapathy, Bhateja, Das (CR19) 2019 CR73 Al-Qatf, Lasheng, Al-Habib, Al-Sabahi (CR52) 2018; 6 Sultana, Chilamkurti, Peng, Alhadad (CR11) 2019; 12 Moustafa, Hu, Slay (CR21) 2019; 128 CR6 CR7 CR9 CR47 Sangkatsanee, Wattanapongsakorn, Charnsripinyo (CR14) 2011; 34 CR42 Blessie, Karthikeyan (CR61) 2012; 6 Shafiq, Tian, Sun, Du, Guizani (CR45) 2020; 107 Stergiou, Psannis, Gupta (CR4) 2020 Jurcut, Niculcea, Ranaweera, Le-Khac (CR8) 2020; 1 Jesudoss, Subramaniam (CR71) 2014; 5 Soe, Feng, Santosa, Hartanto, Sakurai (CR46) 2020; 9 Manzoor, Kumar (CR26) 2017; 88 Letteri, Penna, Gasperis (CR69) 2019; 15 Kamarudin, Maple, Watson (CR24) 2019; 13 Hur, Ihm, Park (CR60) 2017; 2017 Gao, Shan, Hu, Niu, Liu (CR40) 2019; 7 Sasan, Sharma (CR10) 2016; 8 Chandrashekar, Sahin (CR25) 2014; 40 Strobl, Boulesteix, Zeileis, Hothorn (CR62) 2007; 8 Tewari, Gupta (CR1) 2020; 108 Hasan, Islam, Zarif, Hashem (CR44) 2019; 7 CR58 Al-Garadi, Mohamed, Al-Ali, Du, Ali, Guizani (CR31) 2020; 22 Olakanmi, Dada (CR5) 2019; 9 CR12 CR51 Gupta, Tanwar, Tyagi, Kumar (CR20) 2020; 153 CR50 Wu, Chen, Li (CR39) 2018; 6 Pajouh, Javidan, Khayami, Dehghantanha, Choo (CR22) 2019; 7 Latah, Toker (CR49) 2018; 7 Svetnik, Liaw, Tong, Culberson, Sheridan, Feuston (CR65) 2003; 43 Abomhara (CR70) 2015; 4 Buczak, Guven (CR30) 2015; 18 Louati, Ktata (CR66) 2020; 2 Belavagi, Muniyal (CR72) 2016; 89 Tama, Comuzzi, Rhee (CR53) 2019; 7 Karami, Guerrero-Zapata (CR18) 2015; 149 Tian, Han, Li, Liu, Duan, Castiglione (CR43) 2020 Kaja, Shaout, Ma (CR56) 2019; 49 Tama, Rhee (CR16) 2017; 100 CR68 Idhammad, Afdel, Belouch (CR48) 2018; 48 CR67 Gan, Duanmu, Wang, Cong (CR17) 2013; 40 Bhuyan, Bhattacharyya, Kalita (CR27) 2013; 16 Yang, Zheng, Wu, Niu, Yang (CR41) 2019; 9 Stergiou, Psannis, Gupta, Ishibashi (CR3) 2018; 19 CR63 Bao, Rong, Angelov, Chen, Wong (CR64) 2017; 26 Çavuşoğlu (CR54) 2019; 49 Huang, Cai, Xu (CR29) 2007; 28 AS Eesa (5181_CR23) 2015; 42 5181_CR47 C Stergiou (5181_CR3) 2018; 19 A Karami (5181_CR18) 2015; 149 M Al-Qatf (5181_CR52) 2018; 6 I Manzoor (5181_CR26) 2017; 88 T Chandak (5181_CR55) 2019; 130 N Koroniotis (5181_CR34) 2019; 100 5181_CR42 G Chandrashekar (5181_CR25) 2014; 40 HH Pajouh (5181_CR38) 2017; 48 N Sultana (5181_CR11) 2019; 12 N Moustafa (5181_CR21) 2019; 128 J Huang (5181_CR29) 2007; 28 5181_CR9 N Kaja (5181_CR56) 2019; 49 M Hasan (5181_CR44) 2019; 7 MH Bhuyan (5181_CR27) 2013; 16 T Su (5181_CR59) 2020; 8 A Jurcut (5181_CR8) 2020; 1 5181_CR12 5181_CR58 R Gupta (5181_CR20) 2020; 153 Ü Çavuşoğlu (5181_CR54) 2019; 49 EC Blessie (5181_CR61) 2012; 6 C Strobl (5181_CR62) 2007; 8 5181_CR51 5181_CR50 HPS Sasan (5181_CR10) 2016; 8 F Louati (5181_CR66) 2020; 2 OO Olakanmi (5181_CR5) 2019; 9 MH Kamarudin (5181_CR24) 2019; 13 B Remeseiro (5181_CR28) 2019; 112 M Latah (5181_CR49) 2018; 7 5181_CR68 Y Yang (5181_CR41) 2019; 9 5181_CR67 HH Pajouh (5181_CR22) 2019; 7 BA Tama (5181_CR53) 2019; 7 AL Buczak (5181_CR30) 2015; 18 5181_CR63 BA Tama (5181_CR16) 2017; 100 YN Soe (5181_CR46) 2020; 9 R-J Bao (5181_CR64) 2017; 26 J Ren (5181_CR15) 2019; 2019 K Wu (5181_CR39) 2018; 6 A Tewari (5181_CR1) 2020; 108 R Chapaneri (5181_CR19) 2019 5181_CR6 5181_CR33 5181_CR32 5181_CR35 5181_CR7 MA Al-Garadi (5181_CR31) 2020; 22 M Idhammad (5181_CR48) 2018; 48 5181_CR37 5181_CR36 Q Tian (5181_CR43) 2020 CL Stergiou (5181_CR4) 2020 5181_CR73 I Letteri (5181_CR69) 2019; 15 A Tewari (5181_CR2) 2019; 15 RA Ghazy (5181_CR57) 2020; 111 M Abomhara (5181_CR70) 2015; 4 P Sangkatsanee (5181_CR14) 2011; 34 X Gao (5181_CR40) 2019; 7 A Jesudoss (5181_CR71) 2014; 5 SM Tahsien (5181_CR13) 2020; 161 X-S Gan (5181_CR17) 2013; 40 M Shafiq (5181_CR45) 2020; 107 MC Belavagi (5181_CR72) 2016; 89 V Svetnik (5181_CR65) 2003; 43 J-H Hur (5181_CR60) 2017; 2017 |
| References_xml | – year: 2020 ident: CR4 article-title: IoT-based big data secure management in the fog over a 6G wireless network publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3033131 – volume: 128 start-page: 33 year: 2019 end-page: 55 ident: CR21 article-title: A holistic review of network anomaly detection systems: a comprehensive survey publication-title: J. Netw. Comput. Appl. – volume: 48 start-page: 61 issue: 1 year: 2017 end-page: 74 ident: CR38 article-title: Two-tier network anomaly detection model: a machine learning approach publication-title: J. Intell. Inf. Syst. – ident: CR68 – volume: 108 start-page: 909 year: 2020 end-page: 920 ident: CR1 article-title: Security, privacy and trust of different layers in internet-of-things (IoTs) framework publication-title: Future Gener. Comput. Syst. – volume: 2017 start-page: 6817627 year: 2017 ident: CR60 article-title: A variable impacts measurement in random forest for mobile cloud computing publication-title: Wirel. Commun. Mob. Comput. doi: 10.1155/2017/6817627 – volume: 112 start-page: 103375 year: 2019 ident: CR28 article-title: A review of feature selection methods in medical applications publication-title: Comput. Biol. Med. – volume: 6 start-page: 52 843 year: 2018 end-page: 52 856 ident: CR52 article-title: Deep learning approach combining sparse autoencoder with SVM for network intrusion detection publication-title: IEEE Access – ident: CR51 – ident: CR12 – volume: 40 start-page: 1 year: 2013 end-page: 6 ident: CR17 article-title: Anomaly intrusion detection based on PLS feature extraction and core vector machine publication-title: Knowl. Based Syst. – volume: 100 start-page: 779 year: 2019 end-page: 796 ident: CR34 article-title: Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset publication-title: Future Gener. Comput. Syst. – volume: 22 start-page: 1646 issue: 3 year: 2020 end-page: 1685 ident: CR31 article-title: A survey of machine and deep learning methods for internet of things (IoT) security publication-title: IEEE Commun. Surv. Tutor. – volume: 100 start-page: 1729 issue: 8 year: 2017 end-page: 1737 ident: CR16 article-title: HFSTE: hybrid feature selections and tree-based classifiers ensemble for intrusion detection system publication-title: IEICE Trans. Inf. Syst. – ident: CR35 – ident: CR58 – volume: 8 start-page: 17 issue: 1 year: 2016 end-page: 25 ident: CR10 article-title: Intrusion detection using feature selection and machine learning algorithm with misuse detection publication-title: Int. J. Comput. Sci. Inf. Technol. – volume: 49 start-page: 3235 issue: 9 year: 2019 end-page: 3247 ident: CR56 article-title: An intelligent intrusion detection system publication-title: Appl. Intell. – ident: CR42 – volume: 7 start-page: 314 issue: 2 year: 2019 end-page: 323 ident: CR22 article-title: A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks publication-title: IEEE Trans. Emerg. Top. Comput. – volume: 8 start-page: 25 issue: 1 year: 2007 ident: CR62 article-title: Bias in random forest variable importance measures: illustrations, sources and a solution publication-title: BMC Bioinform. – volume: 9 start-page: 79 issue: 2 year: 2019 end-page: 98 ident: CR5 article-title: An efficient privacy-preserving approach for secure verifiable outsourced computing on untrusted platforms publication-title: Int. J. Cloud Appl. Comput. (IJCAC) – volume: 7 start-page: 82 512 year: 2019 end-page: 82 521 ident: CR40 article-title: An adaptive ensemble machine learning model for intrusion detection publication-title: IEEE Access – volume: 161 start-page: 102630 year: 2020 ident: CR13 article-title: Machine learning based solutions for security of internet of things (IoT): a survey publication-title: J. Netw. Comput. Appl. – volume: 48 start-page: 3193 issue: 10 year: 2018 end-page: 3208 ident: CR48 article-title: Semi-supervised machine learning approach for DDoS detection publication-title: Appl. Intell. – ident: CR67 – volume: 7 start-page: 453 issue: 6 year: 2018 end-page: 459 ident: CR49 article-title: Towards an efficient anomaly-based intrusion detection for software-defined networks publication-title: IET Netw. – ident: CR50 – volume: 153 start-page: 406 year: 2020 end-page: 440 ident: CR20 article-title: Machine learning models for secure data analytics: a taxonomy and threat model publication-title: Comput. Commun. – volume: 1 start-page: 193 issue: 4 year: 2020 ident: CR8 article-title: Security considerations for internet of things: a survey publication-title: SN Comput. Sci. doi: 10.1007/s42979-020-00201-310.1007/s42979-020-00201-310:1007/s42979-020-00201-3 – ident: CR9 – start-page: 345 year: 2019 end-page: 356 ident: CR19 article-title: A comprehensive survey of machine learning-based network intrusion detection publication-title: Smart Intelligent Computing and Applications – ident: CR32 – volume: 7 start-page: 100059 year: 2019 ident: CR44 article-title: Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches publication-title: Internet Things – ident: CR36 – volume: 15 start-page: 170 issue: 3–4 year: 2019 end-page: 182 ident: CR69 article-title: Security in the internet of things: botnet detection in software-defined networks by deep learning techniques publication-title: Int. J. High Perform. Comput. Netw. – volume: 28 start-page: 1825 issue: 13 year: 2007 end-page: 1844 ident: CR29 article-title: A hybrid genetic algorithm for feature selection wrapper based on mutual information publication-title: Pattern Recogn. Lett. – volume: 13 start-page: 232 issue: 2 year: 2019 end-page: 240 ident: CR24 article-title: Hybrid feature selection technique for intrusion detection system publication-title: Int. J. High Perform. Comput. Netw. – volume: 2019 start-page: 7130868 year: 2019 ident: CR15 article-title: Building an effective intrusion detection system by using hybrid data optimization based on machine learning algorithms publication-title: Secur. Commun. Netw. doi: 10.1155/2019/7130868 – volume: 7 start-page: 94 497 year: 2019 end-page: 94 507 ident: CR53 article-title: TSE-IDS: a two-stage classifier ensemble for intelligent anomaly-based intrusion detection system publication-title: IEEE Access – volume: 6 start-page: 385 issue: 3 year: 2012 end-page: 394 ident: CR61 article-title: SIGMIS: a feature selection algorithm using correlation based method publication-title: J. Algorithms Comput. Technol. – volume: 4 start-page: 65 issue: 1 year: 2015 end-page: 88 ident: CR70 article-title: Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks publication-title: J. Cyber Secur. Mob. – ident: CR47 – volume: 88 start-page: 249 year: 2017 end-page: 257 ident: CR26 article-title: A feature reduced intrusion detection system using ANN classifier publication-title: Expert Syst. Appl. – volume: 42 start-page: 2670 issue: 5 year: 2015 end-page: 2679 ident: CR23 article-title: A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems publication-title: Expert Syst. Appl. – ident: CR37 – volume: 9 start-page: 238 issue: 2 year: 2019 ident: CR41 article-title: Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks publication-title: Appl. Sci. – volume: 130 start-page: 79 year: 2019 end-page: 83 ident: CR55 article-title: “An analysis of a feature reduced intrusion detection system using ANN classifier” by Akashdeep et al. expert systems with applications (2017) publication-title: Expert Syst. Appl. – ident: CR33 – year: 2020 ident: CR43 article-title: An intrusion detection approach based on improved deep belief network publication-title: Appl. Intell. doi: 10.1007/s10489-020-01694-4 – volume: 2 start-page: 1 issue: 4 year: 2020 end-page: 13 ident: CR66 article-title: A deep learning-based multi-agent system for intrusion detection publication-title: SN Appl. Sci. – ident: CR6 – volume: 26 start-page: 1324 issue: 3 year: 2017 end-page: 1338 ident: CR64 article-title: Correntropy-based evolving fuzzy neural system publication-title: IEEE Trans. Fuzzy Syst. – ident: CR63 – volume: 149 start-page: 1253 year: 2015 end-page: 1269 ident: CR18 article-title: A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks publication-title: Neurocomputing – volume: 8 start-page: 29 575 year: 2020 end-page: 29 585 ident: CR59 article-title: BAT: deep learning methods on network intrusion detection using NSL-KDD dataset publication-title: IEEE Access – volume: 18 start-page: 1153 issue: 2 year: 2015 end-page: 1176 ident: CR30 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. – volume: 5 start-page: 71 issue: 2 year: 2014 end-page: 77 ident: CR71 article-title: A survey on authentication attacks and countermeasures in a distributed environment publication-title: Indian J. Comput. Sci. Eng. (IJCSE) – volume: 107 start-page: 433 year: 2020 end-page: 442 ident: CR45 article-title: Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city publication-title: Future Gener. Comput. Syst. – volume: 34 start-page: 2227 issue: 18 year: 2011 end-page: 2235 ident: CR14 article-title: Practical real-time intrusion detection using machine learning approaches publication-title: Comput. Commun. – ident: CR73 – volume: 16 start-page: 303 issue: 1 year: 2013 end-page: 336 ident: CR27 article-title: Network anomaly detection: methods, systems and tools publication-title: IEEE Commun. Surv. Tutor. – volume: 40 start-page: 16 issue: 1 year: 2014 end-page: 28 ident: CR25 article-title: A survey on feature selection methods publication-title: Comput. Electr. Eng. – volume: 43 start-page: 1947 issue: 6 year: 2003 end-page: 1958 ident: CR65 article-title: Random forest: a classification and regression tool for compound classification and QSAR modeling publication-title: J. Chem. Inf. Comput. Sci. – volume: 15 start-page: 106 issue: 1–2 year: 2019 end-page: 120 ident: CR2 article-title: A novel ECC-based lightweight authentication protocol for internet of things devices publication-title: Int. J. High Perform. Comput. Netw. – volume: 89 start-page: 117 issue: 2016 year: 2016 end-page: 123 ident: CR72 article-title: Performance evaluation of supervised machine learning algorithms for intrusion detection publication-title: Procedia Comput. Sci. – volume: 111 start-page: 375 issue: 1 year: 2020 end-page: 393 ident: CR57 article-title: Feature selection ranking and subset-based techniques with different classifiers for intrusion detection publication-title: Wirel. Pers. Commun. – volume: 12 start-page: 493 issue: 2 year: 2019 end-page: 501 ident: CR11 article-title: Survey on SDN based network intrusion detection system using machine learning approaches publication-title: Peer-to-Peer Netw. Appl. – volume: 9 start-page: 144 issue: 1 year: 2020 ident: CR46 article-title: Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features publication-title: Electronics – ident: CR7 – volume: 19 start-page: 174 year: 2018 end-page: 184 ident: CR3 article-title: Security, privacy and efficiency of sustainable cloud computing for big data and IoT publication-title: Sustain. Comput. Inform. Syst. – volume: 6 start-page: 50 850 year: 2018 end-page: 50 859 ident: CR39 article-title: A novel intrusion detection model for a massive network using convolutional neural networks publication-title: IEEE Access – volume: 49 start-page: 2735 issue: 7 year: 2019 end-page: 2761 ident: CR54 article-title: A new hybrid approach for intrusion detection using machine learning methods publication-title: Appl. Intell. – volume: 88 start-page: 249 year: 2017 ident: 5181_CR26 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.07.005 – volume: 161 start-page: 102630 year: 2020 ident: 5181_CR13 publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2020.102630 – volume: 1 start-page: 193 issue: 4 year: 2020 ident: 5181_CR8 publication-title: SN Comput. Sci. doi: 10.1007/s42979-020-00201-310.1007/s42979-020-00201-310:1007/s42979-020-00201-3 – year: 2020 ident: 5181_CR4 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3033131 – volume: 40 start-page: 16 issue: 1 year: 2014 ident: 5181_CR25 publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2013.11.024 – volume: 49 start-page: 2735 issue: 7 year: 2019 ident: 5181_CR54 publication-title: Appl. Intell. doi: 10.1007/s10489-018-01408-x – volume: 7 start-page: 82 512 year: 2019 ident: 5181_CR40 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2923640 – ident: 5181_CR47 doi: 10.1007/978-81-322-2695-6_45 – volume: 2 start-page: 1 issue: 4 year: 2020 ident: 5181_CR66 publication-title: SN Appl. Sci. doi: 10.1007/s42452-020-2414-z – volume: 9 start-page: 144 issue: 1 year: 2020 ident: 5181_CR46 publication-title: Electronics doi: 10.3390/electronics9010144 – volume: 43 start-page: 1947 issue: 6 year: 2003 ident: 5181_CR65 publication-title: J. Chem. Inf. Comput. Sci. doi: 10.1021/ci034160g – volume: 40 start-page: 1 year: 2013 ident: 5181_CR17 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2012.09.004 – volume: 8 start-page: 29 575 year: 2020 ident: 5181_CR59 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2972627 – volume: 6 start-page: 50 850 year: 2018 ident: 5181_CR39 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2868993 – ident: 5181_CR36 – volume: 5 start-page: 71 issue: 2 year: 2014 ident: 5181_CR71 publication-title: Indian J. Comput. Sci. Eng. (IJCSE) – volume: 2019 start-page: 7130868 year: 2019 ident: 5181_CR15 publication-title: Secur. Commun. Netw. doi: 10.1155/2019/7130868 – ident: 5181_CR32 doi: 10.1109/CISDA.2009.5356528 – ident: 5181_CR33 doi: 10.26483/ijarcs.v10i2.6395 – volume: 7 start-page: 453 issue: 6 year: 2018 ident: 5181_CR49 publication-title: IET Netw. doi: 10.1049/iet-net.2018.5080 – volume: 100 start-page: 779 year: 2019 ident: 5181_CR34 publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.05.041 – start-page: 345 volume-title: Smart Intelligent Computing and Applications year: 2019 ident: 5181_CR19 doi: 10.1007/978-981-13-1921-1_35 – volume: 107 start-page: 433 year: 2020 ident: 5181_CR45 publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2020.02.017 – volume: 8 start-page: 17 issue: 1 year: 2016 ident: 5181_CR10 publication-title: Int. J. Comput. Sci. Inf. Technol. – volume: 153 start-page: 406 year: 2020 ident: 5181_CR20 publication-title: Comput. Commun. doi: 10.1016/j.comcom.2020.02.008 – volume: 112 start-page: 103375 year: 2019 ident: 5181_CR28 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.103375 – ident: 5181_CR68 – ident: 5181_CR6 – volume: 13 start-page: 232 issue: 2 year: 2019 ident: 5181_CR24 publication-title: Int. J. High Perform. Comput. Netw. doi: 10.1504/IJHPCN.2019.097503 – volume: 9 start-page: 238 issue: 2 year: 2019 ident: 5181_CR41 publication-title: Appl. Sci. doi: 10.3390/app9020238 – ident: 5181_CR73 doi: 10.1109/CCDC.2013.6560973 – volume: 15 start-page: 170 issue: 3–4 year: 2019 ident: 5181_CR69 publication-title: Int. J. High Perform. Comput. Netw. doi: 10.1504/IJHPCN.2019.106095 – volume: 128 start-page: 33 year: 2019 ident: 5181_CR21 publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2018.12.006 – ident: 5181_CR37 – volume: 2017 start-page: 6817627 year: 2017 ident: 5181_CR60 publication-title: Wirel. Commun. Mob. Comput. doi: 10.1155/2017/6817627 – volume: 9 start-page: 79 issue: 2 year: 2019 ident: 5181_CR5 publication-title: Int. J. Cloud Appl. Comput. (IJCAC) – ident: 5181_CR50 – volume: 7 start-page: 94 497 year: 2019 ident: 5181_CR53 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2928048 – ident: 5181_CR9 doi: 10.1007/978-3-030-14070-0_13 – ident: 5181_CR12 doi: 10.1109/GCCE46687.2019.9015465 – volume: 42 start-page: 2670 issue: 5 year: 2015 ident: 5181_CR23 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.11.009 – volume: 130 start-page: 79 year: 2019 ident: 5181_CR55 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.04.017 – volume: 48 start-page: 61 issue: 1 year: 2017 ident: 5181_CR38 publication-title: J. Intell. Inf. Syst. doi: 10.1007/s10844-015-0388-x – volume: 49 start-page: 3235 issue: 9 year: 2019 ident: 5181_CR56 publication-title: Appl. Intell. doi: 10.1007/s10489-019-01436-1 – ident: 5181_CR7 – volume: 18 start-page: 1153 issue: 2 year: 2015 ident: 5181_CR30 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2015.2494502 – volume: 26 start-page: 1324 issue: 3 year: 2017 ident: 5181_CR64 publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2017.2719619 – ident: 5181_CR63 doi: 10.1109/INISTA.2012.6247011 – ident: 5181_CR67 doi: 10.1145/2939672.2939785 – volume: 12 start-page: 493 issue: 2 year: 2019 ident: 5181_CR11 publication-title: Peer-to-Peer Netw. Appl. doi: 10.1007/s12083-017-0630-0 – volume: 6 start-page: 385 issue: 3 year: 2012 ident: 5181_CR61 publication-title: J. Algorithms Comput. Technol. doi: 10.1260/1748-3018.6.3.385 – volume: 7 start-page: 100059 year: 2019 ident: 5181_CR44 publication-title: Internet Things doi: 10.1016/j.iot.2019.100059 – volume: 6 start-page: 52 843 year: 2018 ident: 5181_CR52 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2869577 – volume: 4 start-page: 65 issue: 1 year: 2015 ident: 5181_CR70 publication-title: J. Cyber Secur. Mob. doi: 10.13052/jcsm2245-1439.414 – volume: 100 start-page: 1729 issue: 8 year: 2017 ident: 5181_CR16 publication-title: IEICE Trans. Inf. Syst. doi: 10.1587/transinf.2016ICP0018 – year: 2020 ident: 5181_CR43 publication-title: Appl. Intell. doi: 10.1007/s10489-020-01694-4 – volume: 149 start-page: 1253 year: 2015 ident: 5181_CR18 publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.08.070 – volume: 8 start-page: 25 issue: 1 year: 2007 ident: 5181_CR62 publication-title: BMC Bioinform. doi: 10.1186/1471-2105-8-25 – ident: 5181_CR51 – volume: 89 start-page: 117 issue: 2016 year: 2016 ident: 5181_CR72 publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2016.06.016 – volume: 34 start-page: 2227 issue: 18 year: 2011 ident: 5181_CR14 publication-title: Comput. Commun. doi: 10.1016/j.comcom.2011.07.001 – volume: 22 start-page: 1646 issue: 3 year: 2020 ident: 5181_CR31 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2020.2988293 – volume: 111 start-page: 375 issue: 1 year: 2020 ident: 5181_CR57 publication-title: Wirel. Pers. Commun. doi: 10.1007/s11277-019-06864-3 – volume: 19 start-page: 174 year: 2018 ident: 5181_CR3 publication-title: Sustain. Comput. Inform. Syst. – volume: 28 start-page: 1825 issue: 13 year: 2007 ident: 5181_CR29 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2007.05.011 – volume: 48 start-page: 3193 issue: 10 year: 2018 ident: 5181_CR48 publication-title: Appl. Intell. doi: 10.1007/s10489-018-1141-2 – volume: 7 start-page: 314 issue: 2 year: 2019 ident: 5181_CR22 publication-title: IEEE Trans. Emerg. Top. Comput. doi: 10.1109/TETC.2016.2633228 – volume: 16 start-page: 303 issue: 1 year: 2013 ident: 5181_CR27 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/SURV.2013.052213.00046 – volume: 108 start-page: 909 year: 2020 ident: 5181_CR1 publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2018.04.027 – volume: 15 start-page: 106 issue: 1–2 year: 2019 ident: 5181_CR2 publication-title: Int. J. High Perform. Comput. Netw. doi: 10.1504/IJHPCN.2019.103548 – ident: 5181_CR35 – ident: 5181_CR58 doi: 10.1007/978-981-32-9343-4_16 – ident: 5181_CR42 doi: 10.1109/ICASID.2019.8925239 |
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| Title | Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks |
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