Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 4/2024

13-04-2024

A Lightweight Cooperative Intrusion Detection System for RPL-based IoT

Authors: Hanane Azzaoui, Akram Zine Eddine Boukhamla, Pericle Perazzo, Mamoun Alazab, Vinayakumar Ravi

Published in: Wireless Personal Communications | Issue 4/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The successful deployment of an Intrusion Detection System (IDS) in the Internet of Things (IoT) is subject to two primary criteria: the detection method and the deployment strategy. IDS schemes should take into account that IoT devices often have limited resources. Thus, IDS should be limited in devices’ memory and power usage. In this paper, we design, implement, and evaluate an effective cross-layer lightweight IDS scheme for the IoT (RPL-IDS). The proposed IDS scheme cooperates with the RPL routing protocol using its selected parents as distributed agents. A lightweight artificial neural network (ANN) model is deployed in these agents to detect malicious traffic and collaborates with a centralized system. According to the topology built by the Routing Protocol for Low-Power and Lossy Networks (RPL), these agents are automatically selected, i.e., the routers (parents) of the topology are chosen to act as IDS agents. We implemented RPL-IDS using the Contiki operating system and then comprehensively evaluated it with the Cooja simulator. Experimental results indicate that RPL-IDS is lightweight and can be deployed on devices with limited resources. Most state-of-the-art IDS schemes do not consider the limitation of resources of IoT devices, making them impractical for deployment in many IoT applications. Furthermore, the proposed RPL-IDS demonstrated one of the highest detection rates in the literature while incurring an insignificant energy overload, allowing for scalability in large-scale networks.
previous article Performance Investigation of 400 × 100 Gb/s Ultra-Dense WDM System Using Different Modulation Techniques with Varying Channel Spacing
next article Software Effort Estimation Using Stacked Ensemble Technique and Hybrid Principal Component Regression and Multivariate Adaptive Regression Splines

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Meng, W., Li, W., & Kwok, L. F. (2014). EFM: Enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism. Computers & Security, 43, 189–204.CrossRef
2.
Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of Network and Computer Applications, 128, 105–140.CrossRef
3.
Azzaoui, H., Boukhamla, A. Z. E., Arroyo, D., & Bensayah, A. (2022). Devel-oping new deep-learning model to enhance network intrusion classification. Evolving Systems, 13, 17–25.CrossRef
4.
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020). Machine learning based solutions for security of Internet of Things (IoT): A survey. Journal of Network and Computer Applications, 161, 102630.CrossRef
5.
Nguyen, H. T., Ngo, Q. D., Nguyen, D. H., & Le, V. H. (2020). PSI-rooted subgraph: A novel feature for IoT botnet detection using classifier algorithms. ICT Express., 6(2), 128–138.CrossRef
6.
Azzaoui, H., & Boukhamla, A. (2020). Two-stages intrusion detec-tion system based on hybrid methods. In Proceedings of the 10th international conference on information systems and technologies (pp. 1–7).
7.
Kumar, S., Andersen, M. P., Kim, H. S., & Culler, D. E. (2020). Performant TCP for low-power wireless networks. In 17th USENIX symposium on networked systems design and implementation (NSDI 20) (pp. 911–932).
8.
Kumar, A., Shridhar, M., Swaminathan, S., & Lim, T. J. (2022). Machine learning-based early detection of IoT botnets using network-edge traffic. Computers & Security, 117, 102693.CrossRef
9.
da Silva, T. B., Chaib, R. S., Cerqueira, A., Righi, R. D. R., & Alberti, A. M. (2021). Towards Future Internet of Things Experimentation and Evaluation. IEEE Internet of Things Journal., 9(11), 8469–8484.CrossRef
10.
Lamaazi, H., & Benamar, N. (2018). OF-EC: A novel energy consumption aware objective function for RPL based on fuzzy logic. Journal of Network and Computer Applications, 117, 42–58.CrossRef
11.
Shukla, P. (2017). Ml-ids: A machine learning approach to detect wormhole attacks in internet of things. In 2017 intelligent systems conference (IntelliSys) (pp. 234–240). IEEE.
12.
Jun, C., & Chi, C. (2014). Design of complex event-processing ids in internet of things. In 2014 sixth international conference on measuring technology and mechatronics automation (pp. 226–229). IEEE.
13.
Otoum, Y., Liu, D., & Nayak, A. (2019). DL-IDS: a deep learning–based intrusion detection framework for securing IoT. Transactions on Emerging Telecommunications Technologies, 33(3), e3803.CrossRef
14.
Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.L., Iorkyase, E., Tach-tatzis, C. & Atkinson, R. (2016). Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 Interna-tional symposium on networks, computers and communications (ISNCC) (pp. 1–6). IEEE.
15.
Eskandari, M., Janjua, Z. H., Vecchio, M., & Antonelli, F. (2020). Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet of Things Journal, 7(8), 6882–6897.CrossRef
16.
Alhowaide, A., Alsmadi, I., & Tang, J. (2021). Ensemble detection model for IoT IDS. Internet of Things, 16, 100435.CrossRef
17.
Le, A., Loo, J., Chai, K. K., & Aiash, M. (2016). A specification-based IDS for detecting attacks on RPL-based network topology. Information, 7(2), 25.CrossRef
18.
Sforzin, A., M´armol, F. G., Conti, M., & Bohli, J. M. (2016). RPiDS: Raspberry Pi IDS—A fruitful intrusion detection system for IoT. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) (pp. 440–448). IEEE.
19.
Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., & Sakurai, K. (2019). Implementing lightweight IoT-IDS on raspberry Pi using correlation-based feature selection and its performance evaluation. In International Conference on Advanced Information Networking and Applications (pp. 458–469). Springer, Cham.
20.
Mehmood, A., Mukherjee, M., Ahmed, S. H., Song, H., & Malik, K. M. (2018). NBC-MAIDS: Na¨ıve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks. The Journal of Supercomputing, 74(10), 5156–5170.CrossRef
21.
Canbalaban, E., & Sen, S. (2020). A cross-layer intrusion detection system for RPL-based Internet of Things. In International conference on Ad-hoc networks and wireless (pp. 214–227). Springer, Cham.
22.
Aloul, F., Zualkernan, I., Abdalgawad, N., Hussain, L., & Sakhnini, D. (2021). Network intrusion detection on the IoT edge using Adver-sarial autoencoders. In 2021 International conference on information technology (ICIT) (pp. 120–125). IEEE
23.
Nimbalkar, P., & Kshirsagar, D. (2021). Feature selection for intrusion detection system in Internet-of-Things (IoT). ICT Express, 7(2), 177–181.CrossRef
24.
Mbarek, B., Ge, M., & Pitner, T. (2021). Proactive trust classification for detection of replication attacks in 6LoWPAN-based IoT. Internet of Things, 16, 100442.CrossRef
25.
Violettas, G., Simoglou, G., Petridou, S., & Mamatas, L. (2021). A Soft-warized intrusion detection system for the RPL-based Internet of Things networks. Future Generation Computer Systems, 125, 698–714.CrossRef
26.
Sapre, S., Islam, K., & Ahmadi, P. (2021). A comprehensive data sampling analysis applied to the classification of rare IoT network intrusion types. In 2021 IEEE 18th annual consumer communications & networking conference (CCNC) (pp. 1–2). IEEE
27.
Khaldi, Y., & Benzaoui, A. (2020). A new framework for grayscale ear images recognition using generative adversarial networks under uncon-strained conditions. Evolving Systems, 12(4), 1–12.
28.
Khaldi, Y., & Benzaoui, A. (2020). Region of interest synthesis using image-to-image translation for ear recognition. In 2020 international conference on advanced aspects of software engineering (ICAASE) (pp. 1–6). IEEE
29.
Atul, D. J., Kamalraj, R., Ramesh, G., Sankaran, K. S., Sharma, S., & Khasim, S. (2021). A machine learning based IoT for providing an intru-sion detection system for security. Microprocessors and Microsystems, 82, 103741.CrossRef
30.
Raza, S., Wallgren, L., & Voigt, T. (2013). SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc Networks, 11(8), 2661–2674.CrossRef
31.
Seo, S. H., Won, J., Sultana, S., & Bertino, E. (2014). Effective key management in dynamic wireless sensor networks. IEEE Transactions on Information Forensics and Security, 10(2), 371–383.
32.
Shreenivas, D., Raza, S., & Voigt, T. (2017). Intrusion detection in the RPL-connected 6LoWPAN networks. In Proceedings of the 3rd ACM international workshop on IoT privacy, trust, and security (pp. 31–38)
33.
Amouri, A., Morgera, S. D., Bencherif, M. A., & Manthena, R. (2018). A cross-layer, anomaly-based IDS for WSN and MANET. Sensors, 18(2), 651.CrossRef
34.
Pongle, P., & Chavan, G. (2015). Real time intrusion and wormhole attack detection in internet of things. International Journal of Computer Applications, 121(9), 1–9.CrossRef
35.
Medjek, F., Tandjaoui, D., Djedjig, N., & Romdhani, I. (2021). Multicast DIS attack mitigation in RPL-based IoT-LLNs. Journal of Information Security and Applications, 61, 102939.CrossRef
36.
Winter, T., Thubert, P., Brandt, A., Hui, J.W., Kelsey, R., Levis, P., Pister, K., Struik, R., Vasseur, J.P. and Alexander, R.K., 2012. RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks. rfc, 6550, pp.1–157.
37.
Iova, O., Picco, P., Istomin, T., & Kiraly, C. (2016). Rpl: The routing standard for the internet of things… or is it? IEEE Communications Magazine, 54(12), 16–22.CrossRef
38.
Tsvetkov, T., & Klein, A. (2011). RPL: IPv6 routing protocol for low power and lossy networks. Network, 59, 59–66.
39.
Clausen, T., Herberg, U. & Philipp, M. (2011). A critical eval-uation of the IPv6 routing protocol for low power and lossy networks (RPL). In 2011 IEEE 7th international conference on wireless and mobile computing, networking and communications (WiMob) (pp. 365–372). IEEE
40.
Ancillotti, E., Bruno, R., & Conti, M. (2013). The role of the RPL routing protocol for smart grid communications. IEEE Communications Magazine, 51(1), 75–83.CrossRef
41.
Maind, S. B., & Wankar, P. (2014). Research paper on basic of artificial neural network. International Journal on Recent and Innovation Trends in Computing and Communication, 2(1), 96–100.
42.
Sharafaldin, I., Lashkari, A.H. & Ghorbani, A.A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. In ICISSP (pp. 108–116)
43.
Gharib, A., Sharafaldin, I., Lashkari, A.H. & Ghorbani, A.A. (2016). An evaluation framework for intrusion detection dataset. In 2016 International conference on information science and security (ICISS) (pp. 1–6). IEEE
44.
Eriksson, J., Osterlind, F., Voigt, T., Finne, N., Raza, S., Tsiftes, N., & Dunkels, A. (2009). Demo abstract: Accurate power profiling of sensornets with the COOJA/MSPsim simulator. In 2009 IEEE 6th inter-national conference on mobile adhoc and sensor systems (pp. 1060–1061). IEEE
45.
46.
Fatani, A., Abd Elaziz, M., Dahou, A., Al-Qaness, M. A., & Lu, S. (2021). IoT intrusion detection system using deep learning and enhanced transient search optimization. IEEE Access, 9, 123448–123464.CrossRef
47.
Khaldi, Y., Benzaoui, A., Ouahabi, A., Jacques, S., & Taleb-Ahmed, A. (2021). Ear recognition based on deep unsupervised active learning. IEEE Sensors Journal, 21(18), 20704–20713.CrossRef
Metadata
Title
A Lightweight Cooperative Intrusion Detection System for RPL-based IoT
Authors
Hanane Azzaoui
Akram Zine Eddine Boukhamla
Pericle Perazzo
Mamoun Alazab
Vinayakumar Ravi
Publication date
13-04-2024
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 4/2024
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-024-11009-2

Other articles of this Issue 4/2024

Wireless Personal Communications 4/2024 Go to the issue

OriginalPaper

RIS-Assisted Non-orthogonal Multiple Access System Based on SSK

OriginalPaper

An Adaptable Algorithm for Optimizing Bus Line Distribution Using the Clustering Method

OriginalPaper

Cryptanalysis of a Certificateless Signcryption Scheme for Wireless Sensor Networks in Ubiquitous Healthcare Systems

ReviewPaper

A Review on FoG Computing in 5G Wireless Technologies: Research Challenges, Issues and Solutions

OriginalPaper

Opportunistic Control of Dual-Band Half-Circular U-Shape MIMO Design and Analysis for Wireless Applications

OriginalPaper

A Strategic Approach for Robust Dysarthric Speech Recognition