Implementing a New Lightweight Data Encryption Algorithm for Internet of Things

Main Article Content

Hussein A. Mustafa
https://orcid.org/0009-0009-4038-5068
Galip Cansever

Abstract

Cyberspace is a complex environment consisting of heterogeneous technologies, i.e., fog computing, Internet of Things, cloud computing, and so forth, that result from interacting with services, software, and people on the Internet. It allows users to interact, share information, swap ideas, engage in social or discussion forums, play games, and conduct business, among many other activities. Cyberspace's biggest challenge is cyber-attacks, which affect security and integrity services. However, many traditional security mechanisms provide protection and security services to solve these issues. Therefore, many researchers have focused on solving security and integrity issues by addressing the need for effective lightweight encryption techniques that incorporate the advantages of lightweight symmetric and asymmetrical algorithms. In this paper, a lightweight encryption technique was created and applied with the following features: Keyless, Encryption & Integrity, Text & Number End-to-End Encryption, Reduce Traffic, and processing overhead. In addition, the proposed system provides data integrity by applying the HASH 256 function to generate a HASH value. The proposed lightweight encryption algorithm focuses on the optimal use of the resources of Internet of Things devices so that it dramatically saves all of (Processor, Memory, Energy, Time, and Bandwidth (no need to distribute the keys)), on the other hand, giving high security, especially against the crypto analyzer. In addition, the proposed lightweight encryption algorithm can manipulate text and numbers in the English and Arabic languages. Also, to achieve data integrity in the proposed system within the Internet of Things environment, 4 hexadecimal digits from the HASH value were used instead of the original 64 hexadecimal digit HASH value to reduce the network bandwidth, processing, and storage.

Metrics

Metrics Loading ...

Article Details

Section
Articles

Plaudit

References

Clark D. Characterizing Cyberspace: Past, Present, and Future. Explorations in Cyber International Relations 2010; 1(2): 1-18.

Shetty N. Vulnerability Assessment for Cybersecurity Using Machine Learning. Social Science Research Network Electronic Journal 2021; 1(1): 1-16. DOI: https://doi.org/10.2139/ssrn.3884044

Smith DL, Anandavalli T, Belonging S. Ioneliness in Cyberspace: Impacts of Social Media on Adolescents’ Well-Being. Australian Journal of Psychology 2021; 73(1): 12-23. DOI: https://doi.org/10.1080/00049530.2021.1898914

Zwitter A, Hazenberg J. Cyberspace, Blockchain, Governance: How Technology Implies Normative Power and Regulation. In: Cappiello B, Carullo G, editors. Blockchain, Law and Governance. Cham: Springer International Publishing; 2021. p. 45-62. DOI: https://doi.org/10.2139/ssrn.3660795

Venish Raja C, Chitra K, Jonafark M. A Survey on Mobile Cloud Computing. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2018; 3(3): 2096–2100.

Ammar M, Russello G, Crispo B. Internet of Things: A Survey on the Security of IoT Frameworks. Journal of Information Security and Applications 2018; 38(1): 8-27. DOI: https://doi.org/10.1016/j.jisa.2017.11.002

Zhang D. Big Data Security and Privacy Protection. International Conference on Virtual Reality and Intelligent Systems; IEEE; 2019. p. 87–89. DOI: https://doi.org/10.1109/ICVRIS.2019.00030

Saeed MSM. Lightweight Cyber Attack Intelligent Detection Model Based on Blockchain in IoT. Ph.D. Thesis. Mosul University, Mosul, Iraq; 2023.

Hussain F, Hussain R, Hassan SA, Hossain E. Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys & Tutorials 2019; 22(3): 1686-1721. DOI: https://doi.org/10.1109/COMST.2020.2986444

Cvitić I, Peraković D, Periša M, Botica M. Smart Home IoT Traffic Characteristics as a Basis for DDoS Traffic Detection. Proceedings of the 3rd European Alliance for Innovation International Conference on Management of Manufacturing Systems; University of Zagreb; 2018. p. 1-10. DOI: https://doi.org/10.4108/eai.6-11-2018.2279336

Vaccari I, Aiello M, Cambiaso E. SlowTT: A Slow Denial of Service Against IoT Networks. MDPI Electronics 2020; 11(9): 1-18. DOI: https://doi.org/10.3390/info11090452

Khan MA, Khan MA, Jan SU, Ahmad J, Jamal SS, Shah AA, Pitropakis N, Buchanan WJ. A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT. MDPI Sensors 2021; 21(21): 1-25. DOI: https://doi.org/10.3390/s21217016

Dizdarević J, Carpio F, Jukan A, Masip-Bruin X. A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration. ACM Computing Surveys 2019; 52(4): 1–30. DOI: https://doi.org/10.1145/3292674

Kraijak S, Tuwanut P. A Survey on IoT Architectures, Protocols, Applications, Security, Privacy, Real-World Implementation and Future Trends. 11th International Conference on Wireless Communications, Networking and Mobile Computing; IEEE; 2015. p. 1-6. DOI: https://doi.org/10.1049/cp.2015.0714

Dankan Gowda V, Sridhara SB, Naveen KB, Ramesha M, Naveena Pai G. Internet of Things: Internet Revolution, Impact, Technology Road Map and Features. Advances in Mathematics: Scientific Journal 2020; 9(7): 4405-4414. DOI: https://doi.org/10.37418/amsj.9.7.11

Fenanir S, Semchedine F, Baadache A. A Machine Learning-Based Lightweight Intrusion Detection System for the Internet of Things. Revue d’Intelligence Artificielle 2019; 33(3): 203–211. DOI: https://doi.org/10.18280/ria.330306

Al-Masri E, Kalyanam KR, Batts J, Kim J, Singh S, Vo T, Yan C. Investigating Messaging Protocols for the Internet of Things (IoT). IEEE Access 2020; 8(1): 94880-94911. DOI: https://doi.org/10.1109/ACCESS.2020.2993363

Cornel-Cristian A, Gabriel T, Arhip-Calin M, Zamfirescu A. Smart Home Automation with MQTT. 54th International Universities Power Engineering Conference; IEEE; 2019. p. 1–5. DOI: https://doi.org/10.1109/UPEC.2019.8893551

Mukherji SV, Sinha R, Basak S, Kar SP. Smart Agriculture Using Internet of Things and MQTT Protocol. International Conference on Machine Learning, Big Data, Cloud and Parallel Computing; IEEE; 2019. p. 14–16. DOI: https://doi.org/10.1109/COMITCon.2019.8862233

Atmoko RA, Yang D. Online Monitoring & Controlling Industrial Arm Robot Using MQTT Protocol. International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics); IEEE; 2018. p. 12–16. DOI: https://doi.org/10.1109/ROBIONETICS.2018.8674672

Hintaw AJ, Manickam S, Karuppayah S, Aladaileh MA, Aboalmaaly MF, Laghari SUA. A Robust Security Scheme Based on Enhanced Symmetric Algorithm for MQTT in the Internet of Things. IEEE Access 2023; 11(1): 43019–43040. DOI: https://doi.org/10.1109/ACCESS.2023.3267718

Ghannadrad A. Machine Learning-Based DoS Attacks Detection for MQTT Sensor Networks. M.Sc. Thesis. Politecnico di Milano University, Italy; 2021.

Medileh S, Laouid A, Nagoudi EMB, Euler R, Bounceur A, Hammoudeh M, AlShaikh M, Eleyan A, Khashan OA. A Flexible Encryption Technique for the Internet of Things Environment. Ad Hoc Networks 2020; 106(1): 1-16. DOI: https://doi.org/10.1016/j.adhoc.2020.102240

Yao X, Chen Z, Tian Y. A Lightweight Attribute-Based Encryption Scheme for the Internet of Things. Future Generation Computer Systems 2015; 49(1): 104–112. DOI: https://doi.org/10.1016/j.future.2014.10.010

Usman M, Ahmed I, Aslam MI, Khan S, Shah UA. SIT: A Lightweight Encryption Algorithm for Secure Internet of Things. International Journal of Advanced Computer Science and Applications 2017; 8(1): 1–10. DOI: https://doi.org/10.14569/IJACSA.2017.080151

Sung BY, Kim KB, Shin KW. An AES-GCM Authenticated Encryption Crypto-Core for IoT Security. IEEE International Conference on Electronics Information and Communication 2018; p. 1–3. DOI: https://doi.org/10.23919/ELINFOCOM.2018.8330586

Hazzaa FI. A New Security Scheme and Lightweight Encryption Algorithm for Voice Over Wireless Networks Connectivity to Internet. Ph.D. Thesis. Anglia Ruskin University, Chelmsford, United Kingdom; 2019.

Bhaskar AV, Baingane A, Jahnige R, Zhang Q, Zhu T. A Secured Protocol for IoT Networks. International Conference on Virtual Reality and Intelligent Systems; Cornell University; 2020. p. 1–5.

Khattabi YM, Matalgah MM, Olama MM. Revisiting Lightweight Encryption for IoT Applications: Error Performance and Throughput in Wireless Fading Channels with and without Coding. IEEE Access 2020; 8(1): 13429-13443. DOI: https://doi.org/10.1109/ACCESS.2020.2966596

Saraˇcevi´c MH, Adamovi´c SZ, Miškovic VA, Elhoseny M, Maˇcek ND, Selim MM, Shankar K. Data Encryption for Internet of Things Applications Based on Catalan Objects and Two Combinatorial Structures. IEEE Transactions on Reliability 2021; 70(2): 819–830. DOI: https://doi.org/10.1109/TR.2020.3010973

Abdul-Jabbar MM, Jassim J. Securing Industrial Internet of Things (Industrial IoT) - A Review of Challenges and Solutions. Al-Rafidain Engineering Journal 2023; 28(1): 312–320. DOI: https://doi.org/10.33899/rengj.2022.135292.1196

Similar Articles

You may also start an advanced similarity search for this article.