MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multim...

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Veröffentlicht in:	Multimedia systems Jg. 29; H. 3; S. 1785 - 1797
Hauptverfasser:	Pundir, Sumit, Obaidat, Mohammad S., Wazid, Mohammad, Das, Ashok Kumar, Singh, Devesh Pratap, Rodrigues, Joel J. P. C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023 Springer Nature B.V
Schlagworte:	Artificial neural networks Big Data Communication Computer Communication Networks Computer Graphics Computer Science Cryptology Cybersecurity Data Storage Representation Electronic devices Industrial plants Internet of Things Machine learning Malware Multimedia Multimedia Information Systems Operating Systems Role of Deep Learning Models & Analytics in Industrial Multimedia Environment Smart devices Smart houses Special Issue Paper Surveillance systems Internet of Things (IoT) Simulation Security Malware detection Machine learning Industrial multimedia environment
ISSN:	0942-4962, 1432-1882
Online-Zugang:	Volltext
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Abstract	Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5 % detection and 0.5 % false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks.
AbstractList	Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5% detection and 0.5% false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks. Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5 % detection and 0.5 % false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks.
Author	Singh, Devesh Pratap Pundir, Sumit Obaidat, Mohammad S. Das, Ashok Kumar Wazid, Mohammad Rodrigues, Joel J. P. C.
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Cites_doi	10.1109/ACCESS.2018.2866962 10.3390/s19245539 10.1109/LGRS.2006.877949 10.1109/COMST.2019.2896380 10.1007/s11042-017-5154-3 10.1016/j.future.2019.02.064 10.1007/s11277-016-3433-3 10.1109/ACCESS.2017.2676119 10.1109/TKDE.2010.160 10.1109/ACCESS.2019.2917135 10.1109/TIT.1983.1056650 10.1109/ACCESS.2019.2928564 10.1109/ACCESS.2019.2924045 10.1109/ACCESS.2019.2960412 10.1109/MC.2017.201 10.1007/s11042-018-6288-7 10.1109/JSEN.2017.2787997 10.1109/TDSC.2017.2764083 10.1109/TPDS.2012.105 10.1109/TMC.2008.19 10.1109/ACCESS.2018.2800287 10.1109/JIOT.2019.2944632 10.1109/TKDE.2008.234 10.1016/j.eswa.2011.05.076 10.1016/j.comcom.2020.03.007 10.1002/sec.1652 10.1109/MCE.2019.2953740 10.1109/ACCESS.2019.2907965 10.1145/2990499 10.1109/TC.2002.1004593 10.1109/TEC.2005.847984 10.1109/72.728352 10.1007/s11277-016-3676-z 10.1109/COMST.2019.2926625 10.1109/TETC.2016.2633228 10.1145/3321705.3329847 10.1109/WF-IoT.2019.8767194 10.1002/9781119544678.ch12 10.1002/9781119544678.ch10 10.1007/978-981-13-8759-3_17 10.3390/s20082334 10.7551/mitpress/9969.001.0001 10.23919/SOFTCOM.2017.8115504 10.1002/dac.4024 10.1109/TELFOR.2017.8249458 10.21227/q70p-q449 10.1002/0471756482.ch4 10.1145/3321705.3329857 10.1109/ISSC.2018.8585344 10.1109/MILCOM.2017.8170867
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Keywords	Internet of Things (IoT) Simulation Security Malware detection Machine learning Industrial multimedia environment
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References	Jeon, Damiani, Anisetti (CR18) 2017; 76 Alladi, Chamola, Zeadally (CR5) 2020; 155 CR37 CR36 CR34 CR32 CR31 CR30 Wazid, Das (CR44) 2016; 90 Wazid, Das, Kumari, Khan (CR46) 2016; 9 Wang, Wang, Xie, Wang, Agrawal (CR43) 2008; 7 Ganiz, George, Pottenger (CR13) 2011; 23 Wlas, Krzeminski, Guzinski, Abu-Rub, Toliyat (CR51) 2005; 20 Alaparthy, Morgera (CR3) 2018; 6 Jiang, Zhang, Cai (CR19) 2009; 21 CR6 Li, Tug, Meng, Wang (CR27) 2019; 96 Wazid, Das, Shetty, Rodrigues, Park (CR49) 2019; 19 Sun, Xu, Liang, Zhou (CR38) 2018; 18 Pajouh, Javidan, Khayami, Dehghantanha, Choo (CR33) 2019; 7 Tickle, Andrews, Golea, Diederich (CR39) 1998; 9 Dolev, Yao (CR11) 1983; 29 Wazid, Das, Rodrigues, Shetty, Park (CR48) 2019; 7 Chen, Challita, Saad, Yin, Debbah (CR9) 2019; 21 Wazid, Das, Odelu, Kumar, Susilo (CR47) 2017 Han, Wang, Xu, Zhao, Liu (CR14) 2019; 87 Al-Turjman, Alturjman (CR2) 2020; 79 Hassija, Chamola, Saxena, Jain, Goyal, Sikdar (CR15) 2019; 7 Challa, Wazid, Das, Kumar, Reddy, Yoon, Yoo (CR8) 2017; 5 Cheng, Varshney, Arora (CR10) 2006; 3 CR16 Ahmed, Ali, Abbas, Hadi (CR1) 2016; 16 CR12 Kolias, Kambourakis, Stavrou, Voas (CR23) 2017; 50 Wazid, Das (CR45) 2017; 94 Zhao, Li, Zhang, Geng, Zhang, Sun (CR53) 2019; 7 Messerges, Dabbish, Sloan (CR28) 2002; 51 Sharma, You, Yim, Chen, Cho (CR35) 2019; 7 CR54 Wang, Yan, Wang, Liu (CR41) 2011; 38 Alladi, Chamola, Sikdar, Choo (CR4) 2020; 9 CR50 Verma, Singh, Kumar, Chamola (CR40) 2020; 7 CR29 Chaabouni, Mosbah, Zemmari, Sauvignac, Faruki (CR7) 2019; 21 CR26 CR25 CR24 CR22 CR21 CR20 Zhao, Dong (CR52) 2018; 6 Jan, Ahmed, Shakhov, Koo (CR17) 2019; 7 Wang, Fu, Agrawal (CR42) 2013; 24 S Challa (743_CR8) 2017; 5 GK Verma (743_CR40) 2020; 7 W Li (743_CR27) 2019; 96 743_CR20 M Wlas (743_CR51) 2005; 20 743_CR21 T Alladi (743_CR5) 2020; 155 743_CR29 743_CR26 V Sharma (743_CR35) 2019; 7 743_CR24 SU Jan (743_CR17) 2019; 7 743_CR25 M Wazid (743_CR48) 2019; 7 743_CR22 G Jeon (743_CR18) 2017; 76 L Jiang (743_CR19) 2009; 21 743_CR31 743_CR32 743_CR30 M Wazid (743_CR45) 2017; 94 D Dolev (743_CR11) 1983; 29 743_CR37 L Zhao (743_CR52) 2018; 6 743_CR36 743_CR34 T Alladi (743_CR4) 2020; 9 HH Pajouh (743_CR33) 2019; 7 M Chen (743_CR9) 2019; 21 F Al-Turjman (743_CR2) 2020; 79 SS Wang (743_CR41) 2011; 38 VT Alaparthy (743_CR3) 2018; 6 Y Wang (743_CR43) 2008; 7 V Hassija (743_CR15) 2019; 7 M Wazid (743_CR44) 2016; 90 AB Tickle (743_CR39) 1998; 9 Y Zhao (743_CR53) 2019; 7 MC Ganiz (743_CR13) 2011; 23 Z Sun (743_CR38) 2018; 18 Q Cheng (743_CR10) 2006; 3 C Kolias (743_CR23) 2017; 50 X Han (743_CR14) 2019; 87 TS Messerges (743_CR28) 2002; 51 M Wazid (743_CR46) 2016; 9 M Wazid (743_CR47) 2017 743_CR54 743_CR6 743_CR50 M Wazid (743_CR49) 2019; 19 S Ahmed (743_CR1) 2016; 16 Y Wang (743_CR42) 2013; 24 N Chaabouni (743_CR7) 2019; 21 743_CR16 743_CR12
References_xml	– volume: 6 start-page: 47364 year: 2018 end-page: 47373 ident: CR3 article-title: A multi-level intrusion detection system for wireless sensor networks based on immune theory publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2866962 – ident: CR22 – volume: 19 start-page: 5539 issue: 24 year: 2019 ident: CR49 article-title: LDAKM-EIoT: lightweight device authentication and key management mechanism for edge-based IoT deployment publication-title: Sensors doi: 10.3390/s19245539 – volume: 3 start-page: 491 issue: 4 year: 2006 end-page: 494 ident: CR10 article-title: Logistic regression for feature selection and soft classification of remote sensing data publication-title: IEEE Geosci Rem Sens Lett doi: 10.1109/LGRS.2006.877949 – volume: 21 start-page: 2671 issue: 3 year: 2019 end-page: 2701 ident: CR7 article-title: Network intrusion detection for IoT security based on learning techniques publication-title: IEEE Commun Surv Tutorials doi: 10.1109/COMST.2019.2896380 – ident: CR16 – ident: CR12 – volume: 76 start-page: 24589 issue: 23 year: 2017 end-page: 24593 ident: CR18 article-title: Computational intelligence for multimedia and industrial applications publication-title: Multimed Tools Appl doi: 10.1007/s11042-017-5154-3 – volume: 96 start-page: 481 year: 2019 end-page: 489 ident: CR27 article-title: Designing collaborative blockchained signature-based intrusion detection in IoT environments publication-title: Future Gen Comput Syst doi: 10.1016/j.future.2019.02.064 – ident: CR29 – volume: 90 start-page: 1971 issue: 4 year: 2016 end-page: 2000 ident: CR44 article-title: An efficient hybrid anomaly detection scheme using K-means clustering for wireless sensor networks publication-title: Wirel Pers Commun doi: 10.1007/s11277-016-3433-3 – ident: CR54 – volume: 5 start-page: 3028 year: 2017 end-page: 3043 ident: CR8 article-title: Secure signature-based authenticated key establishment scheme for future IoT applications publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2676119 – ident: CR25 – volume: 23 start-page: 1022 issue: 7 year: 2011 end-page: 1034 ident: CR13 article-title: Higher order Nave Bayes: a novel non-IID approach to text classification publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2010.160 – volume: 7 start-page: 556 year: 2019 end-page: 580 ident: CR35 article-title: Briot: behavior rule specification-based misbehavior detection for IoT-embedded cyber-physical systems publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917135 – volume: 29 start-page: 198 issue: 2 year: 1983 end-page: 208 ident: CR11 article-title: On the security of public key protocols publication-title: IEEE Trans Inf Theory doi: 10.1109/TIT.1983.1056650 – ident: CR21 – volume: 7 start-page: 95397 year: 2019 end-page: 95417 ident: CR53 article-title: A survey of networking applications applying the software defined networking concept based on machine learning publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2928564 – volume: 7 start-page: 82721 year: 2019 end-page: 82743 ident: CR15 article-title: A survey on IoT security: application areas, security threats, and solution architectures publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2924045 – volume: 7 start-page: 459 year: 2019 end-page: 476 ident: CR48 article-title: IoMT malware detection approaches: analysis and research challenges publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2960412 – ident: CR50 – ident: CR32 – ident: CR36 – volume: 87 start-page: 588 issue: 101 year: 2019 ident: CR14 article-title: Recognizing roles of online illegal gambling participants: an ensemble learning approach publication-title: Comput Secur – ident: CR26 – volume: 50 start-page: 80 issue: 7 year: 2017 end-page: 84 ident: CR23 article-title: DDoS in the IoT: Mirai and Other Botnets publication-title: Computer doi: 10.1109/MC.2017.201 – volume: 79 start-page: 8627 issue: 13 year: 2020 end-page: 8648 ident: CR2 article-title: 5G/IoT-enabled UAVs for multimedia delivery in industry-oriented applications publication-title: Multimed Tools Appl doi: 10.1007/s11042-018-6288-7 – volume: 18 start-page: 1971 issue: 5 year: 2018 end-page: 1984 ident: CR38 article-title: An intrusion detection model for wireless sensor networks with an improved V-detector algorithm publication-title: IEEE Sens J doi: 10.1109/JSEN.2017.2787997 – year: 2017 ident: CR47 article-title: Secure remote user authenticated key establishment protocol for smart home environment publication-title: IEEE Trans Depend Secure Comput doi: 10.1109/TDSC.2017.2764083 – ident: CR37 – volume: 24 start-page: 342 issue: 2 year: 2013 end-page: 355 ident: CR42 article-title: Gaussian versus uniform distribution for intrusion detection in wireless sensor networks publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2012.105 – volume: 7 start-page: 698 issue: 6 year: 2008 end-page: 711 ident: CR43 article-title: Intrusion detection in homogeneous and heterogeneous wireless sensor networks publication-title: IEEE Trans Mobile Comput doi: 10.1109/TMC.2008.19 – ident: CR30 – ident: CR6 – volume: 6 start-page: 4608 year: 2018 end-page: 4617 ident: CR52 article-title: An industrial internet of things feature selection method based on potential entropy evaluation criteria publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2800287 – volume: 7 start-page: 2563 issue: 4 year: 2020 end-page: 2572 ident: CR40 article-title: CB-CAS: certificate-based efficient signature scheme with compact aggregation for industrial Internet of Things environment publication-title: IEEE Internet Things J doi: 10.1109/JIOT.2019.2944632 – volume: 21 start-page: 1361 issue: 10 year: 2009 end-page: 1371 ident: CR19 article-title: A Novel Bayes Model: hidden Naive Bayes publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2008.234 – volume: 38 start-page: 15234 issue: 12 year: 2011 end-page: 15243 ident: CR41 article-title: An integrated intrusion detection system for cluster-based wireless sensor networks publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.05.076 – volume: 155 start-page: 1 year: 2020 end-page: 8 ident: CR5 article-title: Industrial control systems: cyberattack trends and countermeasures publication-title: Comput Commun doi: 10.1016/j.comcom.2020.03.007 – volume: 9 start-page: 4596 issue: 17 year: 2016 end-page: 4614 ident: CR46 article-title: Design of sinkhole node detection mechanism for hierarchical wireless sensor networks publication-title: Secur Commun Netw doi: 10.1002/sec.1652 – ident: CR31 – volume: 9 start-page: 17 issue: 2 year: 2020 end-page: 25 ident: CR4 article-title: Consumer IoT: security vulnerability case studies and solutions publication-title: IEEE Consumer Electron Mag doi: 10.1109/MCE.2019.2953740 – volume: 7 start-page: 42450 year: 2019 end-page: 42471 ident: CR17 article-title: Toward a lightweight intrusion detection system for the Internet of Things publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2907965 – volume: 16 start-page: 1 issue: 4 year: 2016 end-page: 25 ident: CR1 article-title: Intelligent intrusion detection in low-power IoTs publication-title: ACM Trans Internet Technol. doi: 10.1145/2990499 – ident: CR34 – volume: 51 start-page: 541 issue: 5 year: 2002 end-page: 552 ident: CR28 article-title: Examining smart-card security under the threat of power analysis attacks publication-title: IEEE Trans Comput doi: 10.1109/TC.2002.1004593 – volume: 20 start-page: 520 issue: 3 year: 2005 end-page: 528 ident: CR51 article-title: Artificial-neural-network-based sensorless nonlinear control of induction motors publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2005.847984 – volume: 9 start-page: 1057 issue: 6 year: 1998 end-page: 1068 ident: CR39 article-title: The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks publication-title: IEEE Trans Neural Netw doi: 10.1109/72.728352 – volume: 94 start-page: 1165 issue: 3 year: 2017 end-page: 1191 ident: CR45 article-title: A secure group-based blackhole node detection scheme for hierarchical wireless sensor networks publication-title: Wirel Pers Commun doi: 10.1007/s11277-016-3676-z – volume: 21 start-page: 3039 issue: 4 year: 2019 end-page: 3071 ident: CR9 article-title: Artificial neural networks-based machine learning for wireless networks: a tutorial publication-title: IEEE commun Surv Tutorials doi: 10.1109/COMST.2019.2926625 – ident: CR24 – volume: 7 start-page: 314 issue: 2 year: 2019 end-page: 323 ident: CR33 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 Topics Comput doi: 10.1109/TETC.2016.2633228 – ident: CR20 – volume: 76 start-page: 24589 issue: 23 year: 2017 ident: 743_CR18 publication-title: Multimed Tools Appl doi: 10.1007/s11042-017-5154-3 – volume: 51 start-page: 541 issue: 5 year: 2002 ident: 743_CR28 publication-title: IEEE Trans Comput doi: 10.1109/TC.2002.1004593 – volume: 9 start-page: 1057 issue: 6 year: 1998 ident: 743_CR39 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.728352 – ident: 743_CR6 doi: 10.1145/3321705.3329847 – volume: 9 start-page: 17 issue: 2 year: 2020 ident: 743_CR4 publication-title: IEEE Consumer Electron Mag doi: 10.1109/MCE.2019.2953740 – year: 2017 ident: 743_CR47 publication-title: IEEE Trans Depend Secure Comput doi: 10.1109/TDSC.2017.2764083 – ident: 743_CR16 – volume: 38 start-page: 15234 issue: 12 year: 2011 ident: 743_CR41 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.05.076 – ident: 743_CR12 – volume: 155 start-page: 1 year: 2020 ident: 743_CR5 publication-title: Comput Commun doi: 10.1016/j.comcom.2020.03.007 – ident: 743_CR25 doi: 10.1109/WF-IoT.2019.8767194 – volume: 50 start-page: 80 issue: 7 year: 2017 ident: 743_CR23 publication-title: Computer doi: 10.1109/MC.2017.201 – volume: 96 start-page: 481 year: 2019 ident: 743_CR27 publication-title: Future Gen Comput Syst doi: 10.1016/j.future.2019.02.064 – ident: 743_CR30 doi: 10.1002/9781119544678.ch12 – volume: 6 start-page: 4608 year: 2018 ident: 743_CR52 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2800287 – volume: 24 start-page: 342 issue: 2 year: 2013 ident: 743_CR42 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2012.105 – volume: 19 start-page: 5539 issue: 24 year: 2019 ident: 743_CR49 publication-title: Sensors doi: 10.3390/s19245539 – volume: 87 start-page: 588 issue: 101 year: 2019 ident: 743_CR14 publication-title: Comput Secur – volume: 9 start-page: 4596 issue: 17 year: 2016 ident: 743_CR46 publication-title: Secur Commun Netw doi: 10.1002/sec.1652 – volume: 21 start-page: 1361 issue: 10 year: 2009 ident: 743_CR19 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2008.234 – ident: 743_CR31 doi: 10.1002/9781119544678.ch10 – ident: 743_CR37 doi: 10.1007/978-981-13-8759-3_17 – volume: 79 start-page: 8627 issue: 13 year: 2020 ident: 743_CR2 publication-title: Multimed Tools Appl doi: 10.1007/s11042-018-6288-7 – volume: 7 start-page: 95397 year: 2019 ident: 743_CR53 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2928564 – volume: 7 start-page: 698 issue: 6 year: 2008 ident: 743_CR43 publication-title: IEEE Trans Mobile Comput doi: 10.1109/TMC.2008.19 – volume: 16 start-page: 1 issue: 4 year: 2016 ident: 743_CR1 publication-title: ACM Trans Internet Technol. doi: 10.1145/2990499 – volume: 7 start-page: 82721 year: 2019 ident: 743_CR15 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2924045 – ident: 743_CR54 doi: 10.3390/s20082334 – ident: 743_CR24 – volume: 29 start-page: 198 issue: 2 year: 1983 ident: 743_CR11 publication-title: IEEE Trans Inf Theory doi: 10.1109/TIT.1983.1056650 – ident: 743_CR32 doi: 10.7551/mitpress/9969.001.0001 – volume: 23 start-page: 1022 issue: 7 year: 2011 ident: 743_CR13 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2010.160 – ident: 743_CR36 doi: 10.23919/SOFTCOM.2017.8115504 – volume: 5 start-page: 3028 year: 2017 ident: 743_CR8 publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2676119 – volume: 20 start-page: 520 issue: 3 year: 2005 ident: 743_CR51 publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2005.847984 – volume: 3 start-page: 491 issue: 4 year: 2006 ident: 743_CR10 publication-title: IEEE Geosci Rem Sens Lett doi: 10.1109/LGRS.2006.877949 – ident: 743_CR50 doi: 10.1002/dac.4024 – ident: 743_CR34 doi: 10.1109/TELFOR.2017.8249458 – ident: 743_CR21 doi: 10.21227/q70p-q449 – volume: 7 start-page: 459 year: 2019 ident: 743_CR48 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2960412 – volume: 6 start-page: 47364 year: 2018 ident: 743_CR3 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2866962 – ident: 743_CR26 doi: 10.1002/0471756482.ch4 – ident: 743_CR29 doi: 10.1145/3321705.3329857 – volume: 7 start-page: 556 year: 2019 ident: 743_CR35 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917135 – volume: 21 start-page: 2671 issue: 3 year: 2019 ident: 743_CR7 publication-title: IEEE Commun Surv Tutorials doi: 10.1109/COMST.2019.2896380 – ident: 743_CR22 doi: 10.1109/ISSC.2018.8585344 – volume: 7 start-page: 314 issue: 2 year: 2019 ident: 743_CR33 publication-title: IEEE Trans Emerg Topics Comput doi: 10.1109/TETC.2016.2633228 – ident: 743_CR20 doi: 10.1109/MILCOM.2017.8170867 – volume: 7 start-page: 42450 year: 2019 ident: 743_CR17 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2907965 – volume: 21 start-page: 3039 issue: 4 year: 2019 ident: 743_CR9 publication-title: IEEE commun Surv Tutorials doi: 10.1109/COMST.2019.2926625 – volume: 94 start-page: 1165 issue: 3 year: 2017 ident: 743_CR45 publication-title: Wirel Pers Commun doi: 10.1007/s11277-016-3676-z – volume: 7 start-page: 2563 issue: 4 year: 2020 ident: 743_CR40 publication-title: IEEE Internet Things J doi: 10.1109/JIOT.2019.2944632 – volume: 18 start-page: 1971 issue: 5 year: 2018 ident: 743_CR38 publication-title: IEEE Sens J doi: 10.1109/JSEN.2017.2787997 – volume: 90 start-page: 1971 issue: 4 year: 2016 ident: 743_CR44 publication-title: Wirel Pers Commun doi: 10.1007/s11277-016-3433-3
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SubjectTerms	Artificial neural networks Big Data Communication Computer Communication Networks Computer Graphics Computer Science Cryptology Cybersecurity Data Storage Representation Electronic devices Industrial plants Internet of Things Machine learning Malware Multimedia Multimedia Information Systems Operating Systems Role of Deep Learning Models & Analytics in Industrial Multimedia Environment Smart devices Smart houses Special Issue Paper Surveillance systems
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Title	MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach
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