research-article

Enhanced network intrusion detection system protocol for internet of things

Authors:

Tomás PitnerAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 1156 - 1163

https://doi.org/10.1145/3341105.3373867

Published: 30 March 2020 Publication History

Abstract

With the emergence of the Internet of Things (IoT), different IoT nodes such as 6LoWPAN devices can be connected as a network to provide integrated services. Since security and intrusion detection are becoming crucial among IoT devices, real-time detection of the attacks are critical to protect the IoT networks. However, there exists limited research for efficient network intrusion detection systems (NIDS) in the IoT networks. This paper therefore proposes a new NIDS protocol with an efficient replica detection algorithm to increase the utility and performance of existing NIDS, where a number of replica test nodes are intentionally inserted into the network to test the reliability and response of witness nodes. The proposed protocol, Enhanced NIDS, can address the vulnerability of NIDS and improve IoT network security to detect severe compromise attacks such as clone attacks. The simulation study shows that compared to the state-of-the-art SVELTE protocol, the proposed protocol can significantly increase the detection probability and reduce the energy consumption for detecting clone attacks in IoT networks.

References

[1]

Omar Y. Al-Jarrah, Carsten Maple, Mehrdad Dianati, David Oxtoby, and Alex Mouzakitis. 2019. Intrusion detection systems for intra-vehicle networks: a review. IEEE Access 7 (2019), 21266--21289.

[2]

Noor Alsaedi, Fazirulhisyam Hashim, and A Sali. 2015. Energy trust system for detecting sybil attack in clustered wireless sensor networks. Malaysia International Conference on Communications (MICC) (2015), 91--95.

[3]

James P Anderson. 1980. Computer security threat monitoring and surveillance. Technical Report, James P. Anderson Company (1980).

[4]

Elhadj Benkhelifa, Thomas Welsh, and Walaa Hamouda. 2018. A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems. IEEE Communications Surveys & Tutorials, v.20, n.4, pp.3496-3509 (2018).

Digital Library

[5]

Elisa Bertino and Nayeem Islam. 2017. Botnets and internet of things security. Computer (2017), 76--79.

[6]

Eyad Abdullah Bogari, Pavol Zavarsky, Dale Lindskog, and Ron Ruhl. 2012. An analysis of security weaknesses in the evolution of RFID enabled passport. In World Congress on Internet Security (WorldCIS-2012). IEEE, 158--166.

Digital Library

[7]

Hamid Bostani and Mansour Sheikhan. 2017. Hybrid of anomaly-based and specification-based ids for internet of things using unsupervised opf based on mapreduce approach. Computer Communications; v 98 (2017), 52--71.

[8]

Anna L Buczak and Erhan Guven. 2016. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials; v.18; n.2 (2016), 1153--1176.

Digital Library

[9]

Lianbing Deng, Daming Li, Xiang Yao, David Cox, and Haoxiang Wang. 2018. Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Computing (2018), 1--16.

[10]

Zubair Md Fadlullah, Fengxiao Tang, Bomin Mao, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani. 2017. State-of-the-art deep learning: Evolving machine intelligence toward tomorrow intelligent network traffic control systems. IEEE Communications Surveys & Tutorials; v.19; n.4 (2017), 2432--2455; 2017.

[11]

Mouzhi Ge, Hind Bangui, and Barbora Buhnova. 2018. Big Data for Internet of Things: a survey. Future Generation Computer Systems; v. 87 (2018), 601--614.

[12]

Elike Hodo, Xavier Bellekens, Andrew Hamilton, Pierre-Louis Dubouilh, Ephraim Iorkyase, Christos Tachtatzis, and Robert Atkinson. 2016. Threat analysis of IoT networks using artificial neural network intrusion detection system. (2016), 1--6.

[13]

Wafa Ben Jaballah, Mauro Conti, Gilberto Filè, Mohamed Mosbah, and Akka Zemmari. 2018. Whac-A-Mole: Smart node positioning in clone attack in wireless sensor networks. Computer Communications 119 (2018), 66--82.

[14]

Arvind Kamble, Virendra S Malemath, and Deepika Patil. 2017. Security attacks and secure routing protocols in RPL-based Internet of Things: Survey. In 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI). IEEE, 33--39.

[15]

Anhtuan Le, Jonathan Loo, Aboubaker Lasebae, Mahdi Aiash, and Yuan Luo. 2012. 6LoWPAN: a study on QoS security threats and countermeasures using intrusion detection system approach. International Journal of Communication Systems 25, 9 (2012), 1189--1212.

Digital Library

[16]

Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas, and Jaime Lloret. 2017. Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors; v 17; n 9 (2017), 1--17.

[17]

Surendar Madhawa, P. Balakrishnan, and Umamakeswari Arumugam. 2018. Data driven intrusion detection system for software defined networking enabled industrial internet of things. Journal of Intelligent and Fuzzy Systems 34, 3 (2018), 1289--1300.

Digital Library

[18]

Bacem Mbarek, Mouzhi Ge, and Tomas Pitner. 2019. Self-adaptive RFID authentication for Internet of Things. International Conference on Advanced Information Networking and Applications (2019), 1094--1105.

[19]

Preeti Mishra, Vijay Varadharajan, Uday Tupakula, and Emmanuel S Pilli. 2018. A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Communications Surveys & Tutorials (2018), 686 -- 728.

[20]

Alejandro Molina Zarca, Jorge Bernal Bernabe, Ivan Farris, Yacine Khettab, Tarik Taleb, and Antonio Skarmeta. 2018. Enhancing IoT security through network softwarization and virtual security appliances. International Journal of Network Management 28, 5 (2018), 20--38.

[21]

Daniel K. Molzahn and Jianhui Wang. 2019. Detection and characterization of intrusions to network parameter Data in Electric Power Systems. IEEE Trans. Smart Grid 10, 4 (2019), 3919--3928.

[22]

Nour Moustafa, Benjamin Turnbull, and Kim-Kwang Raymond Choo. 2018. 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), 1--16; 2018.

[23]

Fredrik Österlind, Adam Dunkels, Joakim Eriksson, Niclas Finne, and Thiemo Voigt. 2006. Cross-level sensor network simulation with cooja. In First IEEE International Workshop on Practical Issues in Building Sensor Network Applications (SenseApp 2006).

[24]

B. Parno, A. Perrig, and V. Gligor. 2005. Distributed detection of node replication attacks in sensor networks. In 2005 IEEE Symposium on Security and Privacy (S P'05). 49--63.

[25]

Pavan Pongle and Gurunath Chavan. 2015. Real time intrusion and wormhole attack detection in internet of things. International Journal of Computer Applications 121, 9 (2015).

[26]

Pavan Pongle and Gurunath Chavan. 2015. A survey: Attacks on RPL and 6LoWPAN in IoT. In 2015 International conference on pervasive computing (ICPC). IEEE, 1--6.

[27]

S Prabavathy, K Sundarakantham, and S Mercy Shalinie. 2018. Design of cognitive fog computing for intrusion detection in Internet of Things. Journal of Communications and Networks; v.20; n.3 (2018), 291--298; 2018.

[28]

Yue Qiu and Maode Ma. 2018. Secure group mobility support for 6lowpan networks. IEEE Internet of Things Journal 5, 2 (2018), 1131--1141.

[29]

T. P. Rani and C. Jayakumar. 2017. Unique identity and localization based replica node detection in hierarchical wireless sensor networks. Computers & Electrical Engineering 64 (2017), 148--162.

Digital Library

[30]

Biplob R Ray, Morshed U Chowdhury, and Jemal H Abawajy. 2016. Secure object tracking protocol for the Internet of Things. IEEE Internet of Things Journal 3, 4 (2016), 544--553.

[31]

Shahid Raza, Linus Wallgren, and Thiemo Voigt. 2013. SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks 11, 8 (2013), 2661--2674.

[32]

Dharmini Shreenivas, Shahid Raza, and Thiemo Voigt. 2017. Intrusion Detection in the RPL-connected 6LoWPAN Networks. (2017), 31--38.

[33]

Rupinder Singh, Jatinder Singh, and Ravinder Singh. 2016. TBSD: A defend against sybil attack in wireless sensor networks. International Journal of Computer Science and Network Security (IJCSNS); v16; n11 (2016), 90--100.

[34]

P Raghu Vamsi and Krishna Kant. 2014. A lightweight sybil attack detection framework for wireless sensor networks. In International conference on Contemporary computing (IC3). 387--393.

[35]

Linus Wallgren, Shahid Raza, and Thiemo Voigt. 2013. Routing attacks and countermeasures in the RPL-based internet of things. International Journal of Distributed Sensor Networks 9, 8 (2013), 794326.

[36]

Lidong Wang and Randy Jones. 2017. Big data analytics for network intrusion detection: A survey. International Journal of Networks and Communications; v7; n1 (2017), 24--31.

[37]

Bruno Bogaz Zarpelao, Rodrigo Sanches Miani, Cláudio Toshio Kawakani, and Sean Carlisto de Alvarenga. 2017. A survey of intrusion detection in Internet of Things. Journal of Network and Computer Applications; v84 (2017), 25--37.

[38]

Wassim Znaidi, Marine Minier, and Stéphane Ubéda. 2013. Hierarchical node replication attacks detection in wireless sensor networks. International Journal of Distributed Sensor Networks; v 9; n.4 (2013), 745--769.

Cited By

Hadipuspito ESuryani V(2024)Hybrid Deep Learning for Botnet Attack Detection on IoT Networks using CNN-GRU2024 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA62899.2024.10652152(133-139)Online publication date: 10-Jul-2024
https://doi.org/10.1109/ICoDSA62899.2024.10652152
Vardhan AKumar PAwasthi L(2024)A Resilient Intrusion Detection System for IoT Environment Based on a Modified Stacking Ensemble ClassifierSN Computer Science10.1007/s42979-024-03364-55:8Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1007/s42979-024-03364-5
Yadav PPandey SAgrawal DSahu H(2024)Machine Learning-Based Network Intrusion Detection System for IoT EnvironmentInnovations in Data Analytics10.1007/978-981-97-4928-7_8(101-120)Online publication date: 10-Sep-2024
https://doi.org/10.1007/978-981-97-4928-7_8
Show More Cited By

Index Terms

Enhanced network intrusion detection system protocol for internet of things
1. Networks
  1. Network protocols
    1. Network protocol design
2. Security and privacy
  1. Security services
    1. Authentication
      1. Multi-factor authentication

Recommendations

Beholder – A CEP-based intrusion detection and prevention systems for IoT environments
Abstract
IoT devices are vulnerable to security threats. The connectivity among such devices presents opportunities for cybercriminals to perform different types of attacks. In IoT, application layer protocols play an important role. They form ...
A comprehensive survey on fuzzy-based intelligent intrusion detection system for internet of things

Internet of things (IoT) is rapidly expanding, and it is having a greater impact on daily life. The internet of things is widely applied in a wide range of industries, from small to large. IoT-based is a collection of distributed smart devices with ...
A hybrid intrusion detection system design for computer network security

Intrusions detection systems (IDSs) are systems that try to detect attacks as they occur or after the attacks took place. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 March 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC '20

Sponsor:

SIGAPP

SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
465
Total Downloads

Downloads (Last 12 months)19
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hadipuspito ESuryani V(2024)Hybrid Deep Learning for Botnet Attack Detection on IoT Networks using CNN-GRU2024 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA62899.2024.10652152(133-139)Online publication date: 10-Jul-2024
https://doi.org/10.1109/ICoDSA62899.2024.10652152
Vardhan AKumar PAwasthi L(2024)A Resilient Intrusion Detection System for IoT Environment Based on a Modified Stacking Ensemble ClassifierSN Computer Science10.1007/s42979-024-03364-55:8Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1007/s42979-024-03364-5
Yadav PPandey SAgrawal DSahu H(2024)Machine Learning-Based Network Intrusion Detection System for IoT EnvironmentInnovations in Data Analytics10.1007/978-981-97-4928-7_8(101-120)Online publication date: 10-Sep-2024
https://doi.org/10.1007/978-981-97-4928-7_8
Wang XLiu JZhang C(2023)Network intrusion detection based on multi-domain data and ensemble-bidirectional LSTMEURASIP Journal on Information Security10.1186/s13635-023-00139-y2023:1Online publication date: 26-Jun-2023
https://doi.org/10.1186/s13635-023-00139-y
Örs FLevi A(2023)Data driven intrusion detection for 6LoWPAN based IoT systemsAd Hoc Networks10.1016/j.adhoc.2023.103120143:COnline publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1016/j.adhoc.2023.103120
Srinivasan SDeepalakshmi P(2023) An innovative malware detection methodology employing the amalgamation of stacked BiLSTM and CNN + LSTM ‐based classification networks with the assistance of Mayfly metaheuristic optimization algorithm in cyber‐attack Concurrency and Computation: Practice and Experience10.1002/cpe.767935:10Online publication date: 24-Mar-2023
https://doi.org/10.1002/cpe.7679
Mbarek BGe MPitner T(2022)Precisional Detection Strategy for 6LoWPAN Networks in IoT2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53654.2022.9945346(1006-1011)Online publication date: 9-Oct-2022
https://doi.org/10.1109/SMC53654.2022.9945346
Jayalaxmi PSaha RKumar GConti MKim T(2022)Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A SurveyIEEE Access10.1109/ACCESS.2022.322062210(121173-121192)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3220622
Mbarek BGe MPitner T(2022)An adaptive anti-jamming system in HyperLedger-based wireless sensor networksWireless Networks10.1007/s11276-022-02886-128:2(691-703)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11276-022-02886-1
Gothawal DNagaraj S(2021)An intelligent and lightweight intrusion detection mechanism for RPL routing attacks by applying automata modelInformation Security Journal: A Global Perspective10.1080/19393555.2021.197180332:1(1-20)Online publication date: 7-Sep-2021
https://doi.org/10.1080/19393555.2021.1971803
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents