A Hybrid RSA Algorithm in Support of IoT Greenhouse Applications

Internet of Things (IoT) is being utilized in a plethora of applications, many of which aim to improve system performance. IoT nodes suffer from several limitations, such as power supply, computational capability, and information security. The current state of IoT information and the potential for security breaches represent a significant untoward condition, especially regarding the organizational necessity for confidentiality and privacy. In this paper, we propose a strong, simple and energy conserving three-stage data encryption algorithm with a focus on securing IoT data in support of greenhouse applications. The stages include: (1) a novel implementation of the K-Map substitution functions; (2) the utilization of a chaotic equation to generate a sequence of random numbers, which are added to the result of the first stage; (3) the third stage incorporates the Rivest, Shamir, and Adelman (RSA) algorithm, performed on feeds from the output of the second stage, resulting in encrypted data, requiring private key decryption. The proposed algorithm eliminates the handshaking of the traditional RSA to exchange the keys (private and public) between IoT nodes and the cloud (server), then reduce the transmission time by 30%. The proposed cryptography algorithm is implemented and tested using two evaluation methods: a single micro-controller (standalone) and on a server (cloud). The algorithm is tested in both directions up/down link, and provides an acceptable and stable performance with 1.3 faster than the original RSA.

[1]  Hassan Mostafa,et al.  ASIC-oriented comparative review of hardware security algorithms for internet of things applications , 2016, 2016 28th International Conference on Microelectronics (ICM).

[2]  Ch. Naveen,et al.  A proficient chaos based security algorithm for emergency response in WBAN system , 2016, 2016 IEEE Students’ Technology Symposium (TechSym).

[3]  Ali Dehghantanha,et al.  Internet of Things security and forensics: Challenges and opportunities , 2018, Future Gener. Comput. Syst..

[4]  Ali Dehghantanha,et al.  Fuzzy pattern tree for edge malware detection and categorization in IoT , 2019, J. Syst. Archit..

[5]  Ali Dehghantanha,et al.  Towards Better Ocular Recognition for Secure Real-World Applications , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).

[6]  Kim-Kwang Raymond Choo,et al.  IoT Forensics: Amazon Echo as a Use Case , 2019, IEEE Internet of Things Journal.

[7]  C. Diks.  Nonlinear time series analysis , 1999 .

[8]  William Stallings,et al.  Network Security Essentials: Applications and Standards , 1999 .

[9]  Ali Dehghantanha,et al.  An Enhanced Stacked LSTM Method With No Random Initialization for Malware Threat Hunting in Safety and Time-Critical Systems , 2020, IEEE Transactions on Emerging Topics in Computational Intelligence.

[10]  M. Arfan.  Mobile cloud computing security using cryptographic hash function algorithm , 2016, 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE).

[11]  Chong Kuan Chen,et al.  IoT Security: Ongoing Challenges and Research Opportunities , 2014, 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications.

[12]  William Stallings Zhu,et al.  Network Security Essentials : Applications and Standards , 2007 .

[13]  Zhao Jing,et al.  Based on the Improved RSA Keys and Compound Chaotic System and Design of Audio Encryption Algorithm , 2016, 2016 International Conference on Smart City and Systems Engineering (ICSCSE).