More Web Proxy on the site http://driver.im/

review-article

The rise of traffic classification in IoT networks: : A survey

Authors:

Nor Badrul AnuarAuthors Info & Claims

Volume 154, Issue C

https://doi.org/10.1016/j.jnca.2020.102538

Published: 15 March 2020 Publication History

Abstract

With the proliferation of the Internet of Things (IoT), the integration and communication of various objects have become a prevalent practice. The huge growth of IoT devices and different characteristics in the IoT traffic patterns have brought attention to traffic classification methods to address various raised issues in IoT applications. While network traffic classification has been well discussed in a number of surveys and review papers, it is still immature in IoT due to the differences in traffic characteristics in IoT and Non-IoT devices. This survey looks at the emerging trends of network traffic classification in IoT and the utilization of traffic classification in its applications. It also compares the legacy of traffic classification methods and presents an overview of traditional models. This paper extends the discussion with a taxonomy of the current network traffic classification within the IoT context. We then expose commercial and real-world use cases of the IoT traffic classification and finally outline open research issues and challenges in this domain.

References

[1]

N. Al Khater, R.E. Overill, Network traffic classification techniques and challenges, in: 2015 Tenth International Conference on Digital Information Management (ICDIM), 2015, pp. 43–48.

[2]

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of things: a survey on enabling technologies, protocols, and applications, IEEE communications surveys & tutorials 17 (2015) 2347–2376.

[3]

F.A. Alaba, M. Othman, I.A.T. Hashem, F. Alotaibi, Internet of Things security: a survey, J. Netw. Comput. Appl. 88 (2017) 10–28.

Digital Library

[4]

H. Alaiz-Moreton, J. Aveleira-Mata, J. Ondicol-Garcia, A.L. Muñoz-Castañeda, I. García, C. Benavides, Multiclass classification procedure for detecting attacks on MQTT-IoT protocol, Complexity 2019 (2019).

[5]

H. Alaiz-Moreton, J. Aveleira-Mata, J. Ondicol-Garcia, A.L. Muñoz-Castañeda, I. García, C. Benavides, Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol, 2019, Available: https://joseaveleira.es/dataset.

[6]

S. Alexander, R. Droms, DHCP options and BOOTP vendor extensions, 1997, 2070-1721.

[7]

M.S. Ali, E. Hossain, D.I. Kim, LTE/LTE-A random access for massive machine-type communications in smart cities, IEEE Commun. Mag. 55 (2017) 76–83.

[8]

M.A. Alsheikh, S. Lin, D. Niyato, H.-P. Tan, Machine learning in wireless sensor networks: Algorithms, strategies, and applications, IEEE Communications Surveys & Tutorials 16 (2014) 1996–2018.

[9]

M. Ammar, G. Russello, B. Crispo, Internet of Things: a survey on the security of IoT frameworks, Journal of Information Security and Applications 38 (2018) 8–27.

[10]

M. Antonakakis, T. April, M. Bailey, M. Bernhard, E. Bursztein, J. Cochran, et al., Understanding the mirai botnet, in: 26th {USENIX} Security Symposium ({USENIX} Security 17), 2017, pp. 1093–1110.

[11]

Y. Ashibani, Q.H. Mahmoud, Cyber physical systems security: analysis, challenges and solutions, Comput. Secur. 68 (2017) 81–97.

Digital Library

[12]

Y. Ashibani, Q.H. Mahmoud, A user authentication model for IoT networks based on app traffic patterns, in: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2018, pp. 632–638.

[13]

L. Atzori, A. Iera, G. Morabito, The internet of things: a survey, Comput. Network. 54 (2010) 2787–2805.

[14]

Awid (2014): Awid dataset - wireless security datasets project. Available: http://icsdweb.aegean.gr/awid/.

[15]

L. Bai, L. Yao, S.S. Kanhere, X. Wang, Z. Yang, Automatic device classification from network traffic streams of internet of things, in: 2018 IEEE 43rd Conference on Local Computer Networks (LCN), 2018, pp. 1–9.

[16]

V. Balasubramanian, S.-S. Ho, V. Vovk, Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications, Newnes, 2014.

[17]

S. Behnke, Hierarchical Neural Networks for Image Interpretation, vol. 2766, Springer, 2003.

[18]

X.J. Bellekens, C. Tachtatzis, R.C. Atkinson, C. Renfrew, T. Kirkham, A highly-efficient memory-compression scheme for GPU-accelerated intrusion detection systems, in: Proceedings of the 7th International Conference on Security of Information and Networks, 2014, p. 302.

[19]

P. Bermolen, M. Mellia, M. Meo, D. Rossi, S. Valenti, Abacus: accurate behavioral classification of P2P-TV traffic, Comput. Network. 55 (2011) 1394–1411.

[20]

E. Bertino, N. Islam, Botnets and internet of things security, Computer 50 (2017) 76–79.

[21]

T. Bhatia. (2011). OpenDPI. Available: https://github.com/thomasbhatia/OpenDPI.

[22]

M.H. Bhuyan, D.K. Bhattacharyya, J.K. Kalita, tutorials, Network anomaly detection: methods, systems and tools, IEEE Communications Surveys & Tutorials 16 (2013) 303–336.

[23]

M.H. Bhuyan, D.K. Bhattacharyya, J.K. Kalita, Towards generating real-life datasets for network intrusion detection, IJ Network Security 17 (2015) 683–701.

[24]

Bitdefender. Bitdefender box hub. Available: https://www.bitdefender.com/box/ .

[25]

A.L. Buczak, E. Guven, A survey of data mining and machine learning methods for cyber security intrusion detection, IEEE Communications Surveys & Tutorials 18 (2015) 1153–1176.

[26]

C. University, The Stratosphere IPS Project Dataset, 2016, Available: https://stratosphereips.org/category/dataset.html.

[27]

CAIDA, The Cooperative Analysis for Internet Data Analysis, 2011, Available: http://www.caida.org.

[28]

J.J.B.I. Camhi, Former Cisco CEO John Chambers predicts 500 billion connected devices by 2025, 2015.

[29]

O. Can, O.K. Sahingoz, An intrusion detection system based on neural network, in: Paper presented at the 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015.

[30]

C. Cerrudo, An emerging US (and world) threat: cities wide open to cyber attacks, Securing Smart Cities 17 (2015) 137–151.

[31]

V. Cisco, Cisco visual networking index: forecast and trends, 2017–2022, White Paper 1 (2018).

[32]

L. Columbus, Roundup of Internet of Things Forecasts and Market Estimates, 2016, 2016, Forbes Magazine, New York, NY USA.

[33]

R. Coppola, M. Morisio, Connected car: technologies, issues, future trends," ACM Computing Surveys (CSUR), 49 (2016) 46.

[34]

M. Corporation. Common vulnerabilities and exposures [Online]. Available: https://cve.mitre.org/data/downloads/index.html.

[35]

CUJO. CUJOAI. Available: https://www.getcujo.com.

[36]

I. Cvitić, D. Peraković, M. Periša, M. Botica, Smart home IoT traffic characteristics as a basis for DDoS traffic detection, in: 3rd EAI International Conference on Management of Manufacturing Systems, 2018.

[37]

A. Dainotti, A. Pescape, K.C. Claffy, Issues and future directions in traffic classification, IEEE network 26 (2012) 35–40.

[38]

G. De La Torre, P. Rad, K.-K.R. Choo, Implementation of deep packet inspection in smart grids and industrial Internet of Things: challenges and opportunities, J. Netw. Comput. Appl. (2019).

[39]

S. Di Domenico, M. De Sanctis, E. Cianca, L. Silvestri, V. Curcurú, A. Betti, Classification of heterogenous M2M/IoT traffic based on C-plane and U-plane data, in: 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2018, pp. 1–5.

[40]

K.L. Dias, M.A. Pongelupe, W.M. Caminhas, L. de Errico, An innovative approach for real-time network traffic classification, Comput. Network. 158 (2019) 143–157.

[41]

L. Ding, J. Liu, T. Qin, H. Li, Internet traffic classification based on expanding vector of flow, Comput. Network. 129 (2017) 178–192.

[42]

DOJO. DOJO. Available: https://dojo.bullguard.com.

[43]

M.C. Domingo, An overview of the Internet of Things for people with disabilities, J. Netw. Comput. Appl. 35 (2012) 584–596.

[44]

F-Secure. F-secure sense. Available: https://www.f-secure.com/en/web/home_global/sense.

[45]

E. Fernandes, J. Jung, A. Prakash, Security analysis of emerging smart home applications, in: 2016 IEEE Symposium on Security and Privacy (SP), 2016, pp. 636–654.

[46]

M. Finsterbusch, C. Richter, E. Rocha, J.-A. Muller, K. Hanssgen, A survey of payload-based traffic classification approaches, IEEE Communications Surveys & Tutorials 16 (2014) 1135–1156.

[47]

G. Fortino, W. Russo, C. Savaglio, W. Shen, M. Zhou, Agent-oriented cooperative smart objects: from IoT system design to implementation, IEEE Transactions on Systems, Man, and Cybernetics: Systems (2017) 1–18.

[48]

L. Franceschi-Bicchierai, Internet of things teddy bear leaked 2 million parent and kids message recordings, Motherboard (2017).

[49]

T.Z. Fu, Y. Hu, X. Shi, D.M. Chiu, J.C. Lui, Pbs: periodic behavioral spectrum of p2p applications, in: International Conference on Passive and Active Network Measurement, 2009, pp. 155–164.

[50]

J.A. Galache, T. Yonezawa, L. Gurgen, D. Pavia, M. Grella, H. Maeomichi, ClouT: leveraging Cloud computing techniques for improving management of massive IoT data, in: 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications, 2014, pp. 324–327.

[51]

J. Goh, S. Adepu, K.N. Junejo, A. Mathur, A dataset to support research in the design of secure water treatment systems, in: International Conference on Critical Information Infrastructures Security, 2016, pp. 88–99.

[52]

J.V. Gomes, P.R. Inácio, M. Pereira, M.M. Freire, P.P. Monteiro, Detection and classification of peer-to-peer traffic: a survey, ACM Comput. Surv. 45 (2013) 30.

[53]

A. Greenberg, Hackers remotely kill a jeep on the highway—with me in it, Wired 7 (2015) 21.

[54]

K. Greff, R.K. Srivastava, J. Koutník, B.R. Steunebrink, J. Schmidhuber, LSTM: a search space odyssey, IEEE transactions on neural networks and learning systems 28 (2016) 2222–2232.

[55]

J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of Things (IoT): A vision, architectural elements, and future directions, Future Generation Computer Systems 29 (2013) 1645–1660.

[56]

I. Hafeez, A.Y. Ding, M. Antikainen, S. Tarkoma, Real-time IoT device activity detection in edge networks, in: International Conference on Network and System Security, 2018, pp. 221–236.

[57]

A. Hamza, D. Ranathunga, H.H. Gharakheili, M. Roughan, V. Sivaraman, Clear as MUD: generating, validating and applying IoT behavioral profiles, in: Proceedings of the 2018 Workshop on IoT Security and Privacy, 2018, pp. 8–14.

[58]

A. Hamza, D. Ranathunga, H.H. Gharakheili, T.A. Benson, M. Roughan, V. Sivaraman, Verifying and monitoring IoTs network behavior using MUD profiles, arXiv preprint arXiv:1902.02484 (2019).

[59]

HIPPIE. (2008). Hippie. Available: https://hippie.soft112.com.

[60]

F. Hock, P. Kortiš, Commercial and open-source based intrusion detection system and intrusion prevention system (IDS/IPS) design for an IP networks, in: 2015 13th International Conference on Emerging eLearning Technologies and Applications (ICETA), 2015, pp. 1–4.

[61]

E. Hodo, X. Bellekens, A. Hamilton, P.-L. Dubouilh, E. Iorkyase, C. Tachtatzis, et al., Threat analysis of IoT networks using artificial neural network intrusion detection system, in: 2016 International Symposium on Networks, Computers and Communications (ISNCC), 2016, pp. 1–6.

[62]

U. Hunkeler, H.L. Truong, A. Stanford-Clark, MQTT-S—a publish/subscribe protocol for Wireless Sensor Networks, in: 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE'08), 2008, pp. 791–798.

[63]

I. Corporation, Ixia Breakpoint Overview and Specifications, 2016, Available: https://www.ixiacom.com/products/network-security-testing-breakingpoint.

[64]

M. Iliofotou, P. Pappu, M. Faloutsos, M. Mitzenmacher, S. Singh, G. Varghese, Network monitoring using traffic dispersion graphs (tdgs), in: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, 2007, pp. 315–320.

[65]

Information-Systems-TechnologyGroupMIT-Lincoln-Lab (2000): DARPA intrusion detection data sets. Available: https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset.

[66]

Y. Jin, N. Duffield, P. Haffner, S. Sen, Z.-L. Zhang, Inferring applications at the network layer using collective traffic statistics, in: 2010 22nd International Teletraffic Congress (lTC 22), 2010, pp. 1–8.

[67]

T. Karagiannis, A. Broido, N. Brownlee, K.C. Claffy, M. Faloutsos, Is P2P dying or just hiding? [P2P traffic measurement], IEEE Global Telecommunications Conference, 2004. GLOBECOM '04, vol. 3, 2004, pp. 1532–1538.

[68]

T. Karagiannis, A. Broido, M. Faloutsos, Transport layer identification of P2P traffic, in: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, 2004, pp. 121–134.

[69]

T. Karagiannis, K. Papagiannaki, M. Faloutsos, BLINC: multilevel traffic classification in the dark, SIGCOMM Comput. Commun. Rev. 35 (2005) 229–240.

[70]

T. Karagiannis, K. Papagiannaki, N. Taft, M. Faloutsos, Profiling the end host, in: International Conference on Passive and Active Network Measurement, 2007, pp. 186–196.

[71]

KDD99 : KDD cup 1999 data [online]. Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

[72]

S.H. Kim, D.I. Kim, "Hybrid backscatter communication for wireless-powered heterogeneous networks, IEEE Trans. Wirel. Commun. 16 (2017) 6557–6570.

[73]

S.H. Kim, D.I. Kim, Traffic-aware backscatter communications in wireless-powered heterogeneous networks, IEEE Trans. Mob. Comput. (2019).

[74]

J. Kim, J. Lee, J. Kim, J. Yun, M2M service platforms: survey, issues, and enabling technologies, IEEE Communications Surveys & Tutorials 16 (2014) 61–76.

[75]

R. Kitchin, M. Dodge, The (in) security of smart cities: vulnerabilities, risks, mitigation, and prevention, J. Urban Technol. 26 (2019) 47–65.

[76]

C. Kolias, G. Kambourakis, A. Stavrou, S. Gritzalis, Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset, IEEE Communications Surveys & Tutorials 18 (2015) 184–208.

[77]

C. Kolias, G. Kambourakis, A. Stavrou, S. Gritzalis, Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset, IEEE Communications Surveys & Tutorials 18 (2016) 184–208.

[78]

C. Kolias, G. Kambourakis, A. Stavrou, J. Voas, DDoS in the IoT: mirai and other botnets, Computer 50 (2017) 80–84.

[79]

I. Kotenko, I. Saenko, A. Kushnerevich, A. Branitskiy, Attack detection in IoT critical infrastructures: a machine learning and big data processing approach, in: 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2019, pp. 340–347.

[80]

B. Krebs, Hacked cameras, DVRs powered today's massive internet outage, Krebs on Security (2016).

[81]

A. Kumar, T.J. Lim, Early detection of mirai-like IoT bots in large-scale networks through sub-sampled packet traffic analysis, in: Future of Information and Communication Conference, 2019, pp. 847–867.

[82]

M. Laner, P. Svoboda, M. Rupp, Detecting M2M traffic in mobile cellular networks, in: IWSSIP 2014 Proceedings, 2014, pp. 159–162.

[83]

M. Laner, N. Nikaein, P. Svoboda, M. Popovic, D. Drajic, S. Krco, Traffic models for machine-to-machine (M2M) communications: types and applications, in: Machine-to-machine (M2M) Communications, Elsevier, 2015, pp. 133–154.

[84]

E. Lear, R. Droms, D. Romascanu, Manufacturer usage description specification, 2070-1721 (2019).

[85]

Lehti, R.; Virolainen, P. (1999): AIDE (advanced intrusion detection environment). Available: https://aide.github.io/.

[86]

J. Leyden, Water treatment plant hacked, chemical mix changed for tap supplies, The Register (2016).

[87]

Z.C. Lipton, J. Berkowitz, C. Elkan, A critical review of recurrent neural networks for sequence learning, arXiv preprint arXiv:1506.00019 (2015).

[88]

M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, J. Lloret, Network traffic classifier with convolutional and recurrent neural networks for Internet of Things, IEEE Access 5 (2017) 18042–18050.

[89]

X. Lu, D. Niyato, H. Jiang, D.I. Kim, Y. Xiao, Z. Han, Ambient backscatter assisted wireless powered communications, IEEE Wireless Communications 25 (2018) 170–177.

[90]

LUMA. LUMAHOME. Available: https://lumahome.com/.

[91]

J. McHugh, Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory, ACM Trans. Inf. Syst. Secur. 3 (2000) 262–294.

Digital Library

[92]

A. Mehmood, Z. Lv, J. Lloret, M.M. Umar, ELDC: an artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs, IEEE Transactions on Emerging Topics in Computing (2019) 1–1.

[93]

Y. Meidan, M. Bohadana, A. Shabtai, J.D. Guarnizo, M. Ochoa, N.O. Tippenhauer, et al., ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis, in: Proceedings of the Symposium on Applied Computing, 2017, pp. 506–509.

[94]

Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, et al., N-BaIoT—network-Based detection of IoT botnet attacks using deep autoencoders, IEEE Pervasive Computing 17 (2018) 12–22.

[95]

Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, et al., N-BaIoT Data Set, 2018, Available: https://archive.ics.uci.edu/ml/datasets/detection_of_IoT_botnet_attacks_N_BaIoT.

[96]

Mell, P.; Hu, V.; Lippmann, R.; Haines, J.; Zissman, M. (2003): An overview of issues in testing intrusion detection systems. Available: https://www.nist.gov/publications/overview-issues-testing-intrusion-detection-systems.

[97]

J. Mena, Investigative Data Mining for Security and Criminal Detection, Butterworth-Heinemann, 2003.

[98]

M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A.-R. Sadeghi, S. Tarkoma, IoT Sentinel: automated device-type identification for security enforcement in IoT, in: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017, pp. 2177–2184.

[99]

M. Miettinen, S. Marchal, I. Hafeez, T. Frassetto, N. Asokan, A.-R. Sadeghi, et al., IoT sentinel demo: automated device-type identification for security enforcement in IoT, in: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017, pp. 2511–2514.

[100]

J. Moon, Y. Lim, A reinforcement learning approach to access management in wireless cellular networks, Wirel. Commun. Mob. Comput. 2017 (2017).

[101]

A.W. Moore, K. Papagiannaki, Toward the accurate identification of network applications, Berlin, Heidelberg (2005) 41–54.

[102]

A.W. Moore, D. Zuev, Internet traffic classification using bayesian analysis techniques, in: ACM SIGMETRICS Performance Evaluation Review, 2005, pp. 50–60.

[103]

N. Moustafa, J. Slay, UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set), in: 2015 Military Communications and Information Systems Conference (MilCIS), 2015, pp. 1–6.

[104]

N. Moustafa, J. Slay, The significant features of the UNSW-NB15 and the KDD99 data sets for network intrusion detection systems, in: 2015 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), 2015, pp. 25–31.

[105]

N. Moustafa, B. Turnbull, K.-K.R. Choo, An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things, IEEE Internet of Things Journal (2018).

[106]

E. Nakashima, “Foreign hackers targeted US water plant in apparent malicious cyber attack, expert says, Wash. Post (2011).

[107]

N. Neshenko, E. Bou-Harb, J. Crichigno, G. Kaddoum, N. Ghani, Demystifying IoT security: an exhaustive survey on IoT vulnerabilities and a first empirical look on internet-scale IoT exploitations, IEEE Communications Surveys & Tutorials (2019).

[108]

B. Ng, M. Hayes, W.K. Seah, Developing a traffic classification platform for enterprise networks with SDN: experiences & lessons learned, 2015 IFIP Networking Conference (IFIP Networking) (2015) 1–9.

[109]

T.T. Nguyen, G.J. Armitage, A survey of techniques for internet traffic classification using machine learning, IEEE Communications Surveys and Tutorials 10 (2008) 56–76.

[110]

N. Nikaein, M. Laner, K. Zhou, P. Svoboda, D. Drajic, M. Popovic, et al., Simple traffic modeling framework for machine type communication, in: ISWCS 2013; the Tenth International Symposium on Wireless Communication Systems, 2013, pp. 1–5.

[111]

Norton. Norton Core. Available: Available: https://us.norton.com/core.

[112]

NTOP. nDPI. Available: https://www.ntop.org/products/deep-packet-inspection/ndpi/.

[113]

O. WIPS-NG. Open WIPS-NG. Available: http://openwips-ng.org/.

[114]

(OISF). Open information security foundation: Suricata. Available: https://suricata-ids.org/.

[115]

A. Onuchowska, S. Chakraborty, W. Jank, U. Shrivastava, Detection and classification of attacks on IoT networks, 2018.

[116]

J. Ortiz, C. Crawford, F. Le, DeviceMien: network device behavior modeling for identifying unknown IoT devices, in: Proceedings of the International Conference on Internet of Things Design and Implementation, 2019, pp. 106–117.

[117]

OSSEC. (Open source HIDS security ). Available: https://www.ossec.net/.

[118]

F. Pacheco, E. Exposito, M. Gineste, C. Baudoin, J. Aguilar, Towards the deployment of Machine Learning solutions in network traffic classification: a systematic survey, in: IEEE Communications Surveys & Tutorials, 2018.

[119]

V.J. C.n. Paxson, Bro: a system for detecting network intruders in real-time 31 (1999) 2435–2463.

[120]

I. Possebon, A. da Silva, L. Granville, A. Schaeffer-Filho, A. Marnerides, Improved network traffic classification using ensemble learning, 2019.

[121]

Q. I. Security. The sagan log analysis engine. Available: https://quadrantsec.com/sagan_log_analysis_engine/.

[122]

S. Ransbotham, R.G. Fichman, R. Gopal, A. Gupta, Special section introduction—ubiquitous IT and digital vulnerabilities, Inf. Syst. Res. 27 (2016) 834–847.

[123]

S. Rezvy, Y. Luo, M. Petridis, A. Lasebae, T. Zebin, An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks, in: 2019 53rd Annual Conference on Information Sciences and Systems (CISS), 2019, pp. 1–6.

[124]

M. Roesch, Snort: lightweight intrusion detection for networks, in: Lisa, 1999, pp. 229–238.

[125]

S. Sabour, N. Frosst, G.E. Hinton, Dynamic routing between capsules, in: Advances in Neural Information Processing Systems, 2017, pp. 3856–3866.

[126]

F. Sallabi, F. Naeem, M. Awad, K. Shuaib, Managing IoT-based smart healthcare systems traffic with software defined networks, in: 2018 International Symposium on Networks, Computers and Communications (ISNCC), 2018, pp. 1–6.

[127]

Samhain. Samhain. Available: https://la-samhna.de/samhain/.

[128]

D. Sanger, Utilities cautioned about potential for a cyberattack after Ukraine's, in: The New York Times. Available via the New York Times., 2016.

[129]

M.R. Santos, R.M. Andrade, D.G. Gomes, A.C. Callado, An efficient approach for device identification and traffic classification in IoT ecosystems, in: 2018 IEEE Symposium on Computers and Communications (ISCC), 2018, pp. 304–309.

[130]

B. Schneier, Will giving the internet eyes and ears mean the end of privacy, Guardian 16 (2013).

[131]

B. Schneier, The Internet of Things is wildly insecure–and often unpatchable, Schneier on Security 6 (2014).

[132]

S. Sen, O. Spatscheck, D. Wang, Accurate, scalable in-network identification of p2p traffic using application signatures, in: Proceedings of the 13th International Conference on World Wide Web, 2004, pp. 512–521.

[133]

M.Z. Shafiq, L. Ji, A.X. Liu, J. Pang, J. Wang, Large-scale measurement and characterization of cellular machine-to-machine traffic, IEEE/ACM Trans. Netw. 21 (2013) 1960–1973.

[134]

M. Shafiq, X. Yu, A.A. Laghari, L. Yao, N.K. Karn, F. Abdessamia, Network traffic classification techniques and comparative analysis using machine learning algorithms, in: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, pp. 2451–2455.

[135]

M.R. Shahid, G. Blanc, Z. Zhang, H. Debar, IoT devices recognition through network traffic analysis, in: 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 5187–5192.

[136]

J. Shen, Y. Li, B. Li, H. Chen, J. Li, IoT eye an efficient system for dynamic IoT devices auto-discovery on organization level, in: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), 2017, pp. 294–299.

[137]

A. Shiravi, H. Shiravi, M. Tavallaee, A.A. J.c. Ghorbani, security, Toward developing a systematic approach to generate benchmark datasets for intrusion detection 31 (2012) 357–374.

[138]

A. Sivanathan, D. Sherratt, H.H. Gharakheili, A. Radford, C. Wijenayake, A. Vishwanath, et al., Characterizing and classifying IoT traffic in smart cities and campuses, in: 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2017, pp. 559–564.

[139]

A. Sivanathan, H.H. Gharakheili, F. Loi, A. Radford, C. Wijenayake, A. Vishwanath, et al., Classifying IoT devices in smart environments using network traffic characteristics, IEEE Trans. Mob. Comput. (2018).

[140]

A. Sleptchenko, M.E. Johnson, Maintaining secure and reliable distributed control systems, Inf. J. Comput. 27 (2014) 103–117.

[141]

J. Song, H. Takakura, Y. Okabe, Description of Kyoto University Benchmark Data, 2006, Available at link: http://www.takakura.com/Kyoto_data/BenchmarkData-Description-v5.pdf.

[142]

A. Soule, Kavé Salamatian, N. Taft, Combining filtering and statistical methods for anomaly detection, in: Presented at the Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, 2005, Berkeley, CA.

[143]

M. Stanislav, T. Beardsley, Hacking iot: a case study on baby monitor exposures and vulnerabilities, Rapid7 Report (2015).

[144]

S. Suthaharan, Big data classification: problems and challenges in network intrusion prediction with machine learning, ACM SIGMETRICS Perform. Eval. Rev. 41 (2014) 70–73.

[145]

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

[146]

R. Thupae, B. Isong, N. Gasela, A.M. Abu-Mahfouz, Machine learning techniques for traffic identification and classifiacation in SDWSN: a survey, in: IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, 2018, pp. 4645–4650.

[147]

UNB-ISCX (2017): Intrusion detection evaluation dataset (CICIDS2017). Available: https://www.unb.ca/cic/datasets/ids-2017.html.

[148]

UNIBS, University of Brescia Dataset, 2009, Available: http://netweb.ing.unibs.it/~ntw/tools/traces/.

[149]

Unsw-nb15 (2015): Unsw-nb15. Available: http://www.cybersecurity.unsw.adfa.edu.au/ADFA%20NB15%20Datasets/.

[150]

N.T.T. Van, T.N. Thinh, Accelerating anomaly-based IDS using neural network on GPU, in: Paper presented at the 2015 international conference on Advanced Computing and Applications (ACOMP), 2015.

[151]

P. Velan, M. Čermák, P. Čeleda, M. Drašar, A survey of methods for encrypted traffic classification and analysis, Int. J. Netw. Manag. 25 (2015) 355–374.

[152]

T. Verwoerd, R. J. C. c Hunt, Intrusion detection techniques and approaches 25 (2002) 1356–1365.

[153]

Wandoujia. Wandoujia dataset [online]. Available: http://www.liuxuanzhe.com/appdata/.

[154]

WANDUOW, Libprotoident, 2009, Available: https://research.wand.net.nz/software/libprotoident.php.

[155]

W. Wang, M. Zhu, X. Zeng, X. Ye, Y. Sheng, Malware traffic classification using convolutional neural network for representation learning, in: 2017 International Conference on Information Networking (ICOIN), 2017, pp. 712–717.

[156]

K. Xu, Z.-L. Zhang, S. Bhattacharyya, Profiling internet backbone traffic: behavior models and applications, in: ACM SIGCOMM Computer Communication Review, 2005, pp. 169–180.

[157]

H. Yao, P. Gao, J. Wang, P. Zhang, C. Jiang, Z. Han, Capsule network assisted IoT traffic classification mechanism for smart cities, IEEE Internet of Things Journal (2019).

[158]

J. Zhang, X. Chen, Y. Xiang, W. Zhou, J. Wu, Robust network traffic classification, IEEE/ACM Trans. Netw. 23 (2015) 1257–1270.

[159]

H. Zheng, F. Yang, X. Tian, X. Gan, X. Wang, S. Xiao, et al., Data gathering with compressive sensing in wireless sensor networks: A random walk based approach, IEEE Transactions on Parallel and Distributed Systems 26 (2014) 35–44.

Cited By

Xia JWu MLi P(2025)SFMLFuture Generation Computer Systems10.1016/j.future.2024.107487162:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.future.2024.107487
Swarnkar MKumar R(2024)BitIoT: A Bit Level Deep Packet Inspection Method for Identification of MQTT-Based IoT Devices in the WildIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337388721:3(2866-2875)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3373887
Gilliard ELiu JAliyu A(2024)Knowledge graph reasoning for cyber attack detectionIET Communications10.1049/cmu2.1273618:4(297-308)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1049/cmu2.12736
Show More Cited By

Index Terms

The rise of traffic classification in IoT networks: A survey
1. Networks
  1. Network services
    1. Network monitoring
  2. Network types

Index terms have been assigned to the content through auto-classification.

Recommendations

Consumer IoT device deployment optimisation through deep learning: a CNN-LSTM solution for traffic classification and service identification

The internet of things (IoT) has revolutionised our world, connecting devices and creating a more intelligent and interconnected environment. However, managing and utilising the vast amount of data generated by these devices is a major challenge. To ...
Protecting IoT devices from security attacks using effective decision-making strategy of appropriate features
Abstract
The term "Internet of things (IoT)” refers to a network in which data from all connected devices may be gathered, analyzed, and modified as per requirements to offer new services. IoT devices require a constant Internet connection to exchange ...
Performance evaluation of the SRE and SBPG components of the IoT hardware platform security advisor framework
Abstract
The applications of Internet of Things (IoT) and associated technologies have been spreading rapidly across a wide range of domains, including environmental monitoring, home automation, and supply chain, having a significant bearing on ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Network and Computer Applications

Journal of Network and Computer Applications Volume 154, Issue C

Mar 2020

106 pages

ISSN:1084-8045

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Academic Press Ltd.

United Kingdom

Publication History

Published: 15 March 2020

Author Tags

Qualifiers

Review-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

30
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xia JWu MLi P(2025)SFMLFuture Generation Computer Systems10.1016/j.future.2024.107487162:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.future.2024.107487
Swarnkar MKumar R(2024)BitIoT: A Bit Level Deep Packet Inspection Method for Identification of MQTT-Based IoT Devices in the WildIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337388721:3(2866-2875)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3373887
Gilliard ELiu JAliyu A(2024)Knowledge graph reasoning for cyber attack detectionIET Communications10.1049/cmu2.1273618:4(297-308)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1049/cmu2.12736
Nadif SSabir EElbiaze HHaqiq A(2024)Green grant-free power allocation for ultra-dense Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2024.103908229:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.jnca.2024.103908
Li NLi TMa ZHu XZhang SLiu FQuan XLuo XRen GFeng HZhang S(2024)HpGraphNEIInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10381061:5Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103810
Babayigit BAbubaker M(2024)Towards a generalized hybrid deep learning model with optimized hyperparameters for malicious traffic detection in the Industrial Internet of ThingsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107515128:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107515
Cai WHou CCui MWang BXiong GGou G(2024)Incremental encrypted traffic classification via contrastive prototype networksComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110591250:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110591
Wu ZXie YTang SLiu X(2024)Estimating the composition ratios of network services carried in mixed trafficComputer Communications10.1016/j.comcom.2024.04.022222:C(1-12)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.comcom.2024.04.022
Murthy AAsghar MTu W(2024)A lightweight Intrusion Detection for Internet of Things‐based smart buildingsSecurity and Privacy10.1002/spy2.3867:4Online publication date: 5-Jul-2024
Mani SZhou YHsieh KSegarra SEberl TAzulai EFrizler IChandra RKandula S(2023)Enhancing Network Management Using Code Generated by Large Language ModelsProceedings of the 22nd ACM Workshop on Hot Topics in Networks10.1145/3626111.3628183(196-204)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3626111.3628183
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents